Productivity Engineering of Tractors and Agriculture - Smart/Intelligent/Autonomous/IoT Tractors

In the case of new technology; products, basic production technologies or processes, industrial engineers have to first acquire knowledge about engineering elements. They have to become aware of variety of brands or designs available for each generic element and their prices (inputs and whole components). They have to know applicable standards, if standards are already prescribed. They also have to know the human related elements in processes.

Sensors

Agricultural tractor control system and method - Patent - granted in 2022.

Abstract

An agricultural tractor (10) control system comprising an electronic control unit (ECU) (16) is provided in addition to a method of controlling a tractor (10). The ECU (16) is arranged to receive in real time signals from a plurality of sensors (40, 42) associated with operating functions of the tractor. The ECU (16) is also arranged to output control signals to a plurality of controlled operating components. Macroinstructions, or macros, are inputted to the ECU (16) by a user by direct definition or Internet download for example. Each macro comprises a condition and a command. The condition includes a trigger value and a corresponding input variable sensed by a respective sensor (40, 42, 50). The command involves the transmission of one of said output control signals to control an operating component in a predetermined manner. The ECU (16) is operable to run said macro during which the command is executed in response to the condition being met.

EP2624678A1

European Patent Office

https://patents.google.com/patent/EP2624678A1

Data Acquisition Application Farm Tractors

https://metromatics.com.au/data-acquisition-applications-farm-tractors/

Sensor Architecture and Task Classification for Agricultural Vehicles and Environments

Francisco Rovira-Más
Departamento de Ingeniería Rural y Agroalimentaria, Universidad Politécnica de Valencia, Camino de Vera s/n,  46022 Valencia, Spain;
Received: 20 October 2010; in revised form: 26 November 2010 / Accepted: 1 December 2010 / Published: 8 December 2010

https://www.mdpi.com/1424-8220/10/12/11226/htm

Sensor Architecture

The sensor architecture proposed  is articulated around four structural subsystems: local perception, global localization, actuation and control, and data processing. 

The fourth subsystem, data processing, comprises the set of computers, processing units, DSPs (digital signal processors), and embedded controllers hosting decision making algorithms, receiving sensor data, and sending actuation commands according to a given software architecture. The other three subsystems incorporate a multiplicity of sensors that have been grouped in the subsections 1 to 4 developed below. There will be, additionally, other ancillary components as extra batteries, power converters, signal conditioners, or emergency stop buttons, which assist the entire vehicle and therefore do not belong to any of the four structural subsystems.  The practical necessity of redundancy and sensor fusion to enhance reliability forces the fluent cooperation among the four key subsystems.  A popular example is given by the combination of local information captured by lidars or machine vision (perception subsystem) with global localization provided by a satellite navigation system. Two crucial aspects need to be considered before fusing the information coming from different sensors: frequency and system of coordinates. It is evident that global localization and (local) vehicle-fixed positioning will naturally have different coordinate systems, and therefore the appropriate coordinate transformation will have to be carried out before merging the data. Each sensor will generate readings at a particular rate, however there will be a main loop frequency at which the orders are executed by the processing subsystem.  Consequently, a suitable architectural design needs to be implemented in order to achieve the best performance for the selected onboard sensors.

Four-core subsystem architecture for intelligent agricultural vehicles.

1. Sensors for Local Perception and Vicinity Monitoring

The vicinity of an agricultural vehicle provides perception systems with critical information for vehicle mobility as well as on valuable data for improving productivity. Such endeavors as simultaneous localization and mapping (SLAM), crop-track guidance, three-dimensional (3D) terrain mapping, obstacle detection or avoidance, nitrogen content mapping, vegetation health monitoring, water stress early detection, and many other activities strongly rely and depend on the local perception subsystem.

Ultrasonic rangefinders have traditionally been well accepted for small robots roaming indoors, and under closed controlled environments, because of their affordability. A more convenient alternative to map ranges is offered by lidar (light detection and ranging) heads, optical devices based on the principle of time-of-flight whose beams of coherent light—usually laser—provide a way to estimate ranges with high resolution.  A double scanning platform rotating simultaneously in two perpendicular planes can generate three-dimensional maps with a single laser beam, but the synchronization of both rotational movements leads to complicated practical solutions when compared to other alternatives such as stereoscopic vision.

The great amount and diversity of information acquirable with vision sensors makes them indispensable in the general configuration of intelligent agricultural vehicles.  The monocular camera  can work in the visible range and near infrared (NIR), and has been used to track crop rows and guide a tractor. When a NIR filter is mounted between the imager and the lens, only NIR reflectance passes through the filter, enhancing vegetation and facilitating the segmentation of the rows. With this kind of camera, the system integrator has to decide between visible spectrum or NIR band. The multispectral camera  on the contrary, grabs three images simultaneously in three predefined bands: red, green, and NIR. This option allows the combination of reflectance values from different wavelength intervals for exactly the same pixel areas, and therefore for the same features in the scene. It has been extensively applied to the monitoring of vegetation indices like the NDVI (normalized difference vegetation index). The displacement along the electromagnetic spectrum towards long-wavelength infrared intervals can be registered with thermocameras.  Thermographic maps have been used to detect water content in the field, water stress in plants, and as a safety feature to sense the presence of living beings immersed in tall crops.  When the three dimensions of space—X, Y, Z—need to be properly determined, monocular vision is not enough and stereoscopic cameras  have to be incorporated in the perception system of the vehicle. Stereo vision is the perceptual system that best resembles human vision, where the pair of eyes is substituted by two identical cameras separated a horizontal distance denominated baseline.

Main visual perception sensors for agricultural vehicles: (a) Monocular camera. (b) Multispectral camera. (c) Thermocamera. (d) Stereoscopic camera.

The availability of the three dimensions for every point—pixel—in the scene, habitually in the form of a 3D point cloud, provides a wealth of information every time an image is acquired. Since stereo cameras can estimate ranges, they have been used as safeguarding tools, and their outcomes are faster and richer than maps generated with lidars or sonars. Stereo-based 3D vision has also been used to automatically guide a harvester by detecting the edge of the crop being cut, and to recreate field scenes through virtual terrain maps [7]. The development of compact stereo cameras during the last decade has placed these sensors among the most cost-effective solutions commercially available. Camera manufacturers normally supply efficient correlation software to generate 3D clouds in real time. The main disadvantage of stereo cameras, however, is the high computational cost involved in 3D perception, especially when handling massive amounts of points, although the fast increase in processor speed given by Moore’s law is palliating this hindrance. Figure 5 shows four popular imaging sensors currently being used in agricultural robotics, and Figure 6 provides some sample images obtained with them. Figure 6(a) is a familiar RGB color image of grapevine rows acquired with a digital color camera. The field image of Figure 6 (b) has been altered with a NIR filter to highlight vegetation from soil and ease segmentation and thresholding. The thermographic map of Figure 6(c) correlates the temperature gradient of a field with the water accumulated in its soil. The virtual tree of Figure 6(d) corresponds to the 3D representation, in the form of a point cloud, of a tree surrounded by turf. Apart from the three Cartesian coordinates of every point, each pixel contains its RGB color code that helps to distinguish the detected trees from the surrounding yellowish grass.

4.2. Sensors for Global Localization

The generic name Global Navigation Satellite Systems, or GNSS, considers all the satellite-based global localization systems that can be publicly used for vehicle positioning. This technology was pioneered by NAVSTAR GPS (USA), the Russian independent system GLONASS has been updated with 20 operational satellites out of 26 in constellation (November 2010), the European Galileo is launching satellites, and the Chinese Beidou is under development.   The only system fully operative worldwide at present is GPS. Nevertheless, a considerable effort is being made to assure compatibility among receivers such that a single receiver will be able to accept signals from a variety of global localization systems in the near future.

The possibility of knowing the precise position of a vehicle in real time has opened the gold mine of information technology (IT) applications to agricultural fields; yield monitoring, variable rate prescriptions, and automatic steering all rely on GPS localization. However, not every operation requires the highest level of accuracy; automatic steering during harvesting demands the utmost precision, but yield monitoring or other mapping application with the purpose of creating historical maps can be successfully carried out with more modest equipment. Different alternatives are currently available for the average producer. The simplest one consists of a lightbar display that indicates how much the vehicle is offset from a predefined path by means of a horizontal set of lights. The driver, following the directions given by the lightbar, can follow a predefined course without the necessity of terrain marks. This procedure has been a cost-effective solution that became very popular at the beginning of the GPS era, and is still in use for some producers and common tasks. When the basic capabilities offered by multipurpose GPS manufacturers are not enough for a given field application, more sophisticated methods have to be implemented. Unlike airplanes and automobiles, off-road vehicles move around small areas where some important errors remain approximately constant. This fact may be used to correct the original signal received from the satellites with that emitted by a GPS reference receiver of well-known location, leading to the technique known as Differential GPS (DGPS). Differential corrections improve localization data considerably, but not all sources of errors can be suppressed; multipath and receiver errors will still be possible. There are several ways to achieve differential corrections by establishing a network of reference stations distributed over moderate pieces of land (Local-Area DGPS) or over wide areas of the globe (Wide-Area DGPS). The latter has resulted in various specific systems according to the area of coverage: the North-American Wide Area Augmentation System (WAAS), the European Geostationary Navigation Overlay System (EGNOS), or the Japanese Multi-functional Satellite Augmentation System (MSAS). A wide-area DGPS can reach less than two meters positioning accuracy, but some agricultural operations require precisions at the decimeter level, which can be attained with commercial carrier phase differential signal providers. These private signal providers usually possess their own geostationary satellites to assure greater levels of accuracy, but users need to pay a periodic signal subscription. The top level of accuracy reachable with GPS is about two centimeters and can be accomplished with the Real Time Kinematik GPS (RTK-GPS). RTK sets contain two receivers, a radio link, and computer software with the purpose of enhancing GPS positioning accuracy by calculating differential corrections from a base station placed in the field, or nearby, where the vehicle is operating. The most important disadvantages of RTK systems are a coverage limitation of around 10 km between vehicle and base, and higher acquisition costs, although there is no need to pay additional subscription fees once the system has been implemented in the field. Figure 7 (b) shows a commercial DGPS installed in a tractor and some of the features displayed in the onboard user interface.

GPS for agricultural operations: (a) Lightbar steering assistance system. (b) DGPS onboard system.

 In fact, the conventional coordinate system in which GPS data is primarily output following the NMEA code format is the World Geodetic System 1984 (WGS 84), an ellipsoid of revolution that models the shape of the earth. These coordinates—latitude, longitude, and altitude—seem to be inappropriate for the modest size of farms. However, given that the curvature of the earth has a negligible effect on agricultural fields, which can be considered flat in most of the cases, a more practical and intuitive system of coordinates can be used for agricultural applications; the Local Tangent Plane (LTP) system of reference. This reference frame allows user-defined origins close to particular operating fields, and employs the familiar orthogonal coordinates North (N), East (E), and Altitude (Z) as graphically defined in Figure 8. A step-by-step conversion between geodetic and LTP coordinates can be followed in [14].

3. Sensors for Vehicle Attitude and Motion Control

 A basic need of intelligent vehicles is navigation assistance, which typically requires the real-time measurement of the angle turned by the wheels in front-axle/rear-axle steering, or the angular misalignment between front and rear bodies of articulated vehicles. The former case is especially relevant for off-road vehicles as many tractors, combines, sprayers, and self-propelled farm equipment in general achieve steering by actuating on the mechanical linkage that causes wheels to alter their orientation with respect to the chassis of the vehicle. The angle turned by front (front-axle steering) or rear wheels (real-axle steering) can be estimated with three sensors: linear potentiometers, flow meters, and optical encoders. Linear potentiometers  give an indirect measure of the wheel angle by tracking the displacement of the cylinder rod actuating the steering mechanism.  A more compact solution, on the other hand, is available with oil flow meters. This alternative also implies an indirect measurement of the wheel angle by quantifying the oil flow moving in and out of the cylinder chambers to achieve a turn. While the interaction with branches and plants is practically inexistent because the sensor is internally integrated in the oil circuit, the need to manipulate and alter the primary fluid power circuit of the vehicle and the not always suitable accuracy of flow meters has reduced its universal use. The third option allows a direct measurement of the wheel angle with an optical encoder, a device comprising a free axle attached to a strapped disc whose position is easily tracked by a light beam. The ideal location for an optical encoder is right on the kingpin of the wheel, in such a way that the steering angle turned by the wheel is equivalent to the angle spun by the encoder axle. This solution requires that either the housing of the encoder or the axle affixed to the disc has to remain immobile when turning, what entails the design and assemblage of a customized frame in the usually constricted area close to the kingpin. 

Wheel angle sensors: (a) Linear potentiometer. (b) Optical encoder.

The dynamic analysis of a vehicle requires the estimation of the vehicle main states, such as position, velocity, acceleration, or the Euler angles roll, pitch, and yaw. These parameters are essential for applications involving autonomous operations, mainly if they include sensor fusion techniques like the Kalman filter. GNSS receivers can provide an estimate of the global position and average velocity of the vehicle, but the instantaneous attitude of the vehicle or its heading angle necessitates the complementary data given by inertial sensors. Inertial measurement units (IMU) combine accelerometers and gyroscopes, typically three of each disposed along the three orthogonal axes of a Cartesian frame. The accelerometers detect velocity changes over time—i.e., the acceleration—and allow the calculation of speed and position by integration. The gyroscopes, on the contrary, are sensitive to instantaneous angular rates experienced by the vehicle around the main Cartesian axes. The integration over time of the three angular rates leads to the attitude angles yaw, pitch, and roll. Accurate IMUs tend to be costly, although their most notable disadvantage is the accumulation of error after extended periods of time, technically known as the sensor drift. The negative effects of drift need to be taken into account in agricultural environments where navigation paths tend to be fairly narrow. As a result, many navigation strategies incorporate sensor fusion methods to increase reliability. An inertial sensor is essential when the vehicle traverses terrains with significant slopes such as forestry exploitation sites, where roll and pitch are basic parameters for navigation and safety. However, the majority of agricultural fields are approximately flat, and therefore pitch and roll are usually negligible. In this situation, there are two vehicle states of great importance: heading and forward velocity. A straightforward means of estimating vehicle speed is by counting the number of rotations spun by the driven wheel with a magnetic counter mounted on the chassis. This calculation provides the theoretical speed of the vehicle, but due to the phenomenon of slippage, very frequent in off-toad terrains, the real speed of the vehicle does not usually coincide with the theoretical one, and therefore the theoretical cannot be used to estimate the actual speed. The theoretical velocity is useful, however, to calculate the vehicle slip as long as the real velocity is measurable with alternative sensors such as radars. The heading is a crucial parameter in the transformation from local to global coordinates, and in many path planning algorithms. It provides the orientation of the vehicle with respect to the north, and can be estimated with an inertial measurement unit as the yaw angle is determined by integrating the yaw rate around axes perpendicular to the local tangent plane. An optional sensor to estimate headings is the fluxgate compass, but the amount of electronic devices inside the cabin may create magnetic fields and affect the performance of the compass.

The fact that onboard GPS receivers can provide position and time for vehicles at a frequency of 5 Hz makes these sensors susceptible to estimate velocity and heading. This feature may or may not be acceptable according to the application pursued. To begin with, GPS heading cannot be known instantaneously unless a series of points have been properly recorded. But even in these circumstances, accuracy and signal stability have to be quite high; otherwise, fluctuations around average values will result in unacceptable headings, as instantaneous heading values will oscillate unrealistically. Additionally, the time needed to get stable series of data may be excessively long and no heading or speed will be available until the vehicle has traveled a significant portion of the planned course. The following two figures illustrate this issue when three-dimensional instantaneous mapping tests were conducted in a winery vineyard in the summer of 2010. Figure 10(a) shows the trajectory followed by the mapping vehicle [tractor in Figure 2 (c)] represented in local tangent plane coordinates; a complete row west-east, and half neighboring row in the return direction east-west. The tractor followed the straight lanes of the vineyard with approximate heading angles of +80° and −100° respectively, easily deducible from the GPS-based trajectory of Figure 10(a). The instantaneous heading for each point of the trajectory, estimated with an algorithm that considers 32-point series, is represented in Figure 10(b). At first sight, the plot seems stable and correct except for the two outliers noticeable in Figure 10, but when heading angles were introduced in the mapping algorithm designed to merge multiple 3D point clouds into a unique map, several inaccuracies showed up.

GPS-based heading estimation: (a) Vehicle trajectory. (b) Instant heading.

The individual maps with origins located at the coordinates given by the two outliers found in Figure 10 were automatically eliminated by the mapping algorithm and therefore do not appear in Figure 11(b), but they were the cause of large errors in the alignment of the images. Notice that many of these 3D images were correctly displayed, but when they were fused to complete the global map of the two rows, the lack of accuracy in the estimation of the heading resulted in frequent misalignments and defective orientation for various portions of the lane. The complete map is rendered in the bird-eye view of Figure 11(b, bottom), and two augmented portions are displayed in the top image. For this application, a more reliable source of headings is therefore necessary. Furthermore, the reliability of GPS was not satisfactory either, as two important outliers appeared, even though there were always between six and nine satellites in solution.

4. Non-visual Sensors for Monitoring Production Parameters

There are other parameters that are important to producers and cannot be determined remotely. The real time estimation of the harvested crop is normally tracked by a yield sensor mounted inside the combine harvester. Average values of yield are globally referenced with a GPS receiver so that yield maps can be generated at the end of the season. Yield monitoring is popular for grain production, mainly corn and soybeans, as well as for wine production. Other interesting maps like those representing rainfall or soil properties cannot be built “on the fly”, generally speaking, as penetrometers, PH-meters, conductivity probes, and other sensors have not been incorporated to vehicles with normality yet.

The complete automation of an agricultural vehicle involves many more functions than automatic steering. Navigation, for example, may require gear shifting, brake activation, throttle control, or differential locking. All these actions, when executed automatically, need to track the position of levers and pedals with potentiometers and encoders. An intelligent implement, for instance, needs to sense its position (up for road traveling and headlands; down for farming) as well as the drag force incurred by the pulling vehicle (axle load cells).

5. Onboard Integration of the Complete Sensor Network

All the sensors that comprise the architecture proposed need to be optimally integrated in the intelligent agricultural vehicle for its use to be easy, comfortable, and safe. The physical location of the sensors is decisive and needs to be carefully planned. The main processor(s) of the vehicle, as well as monitors and screens, will preferably be installed inside the cabin, where vibration, dust, and moisture will have a minimum impact. Consequently, provisions should be made for setting a neat framework of multiple cables entering and exiting the cabin. A second battery, independent from the vehicle’s own battery, is always very helpful to preserve the desired autonomy of the diesel engine. Code debugging and the simultaneity of multiple sensors can easily exhaust the main battery when the engine is not running but computers and sensors are on. In addition, starting the engine results in temporary voltage drops that turn the onboard sensors off, invalidating previous initialization routines. GPS receivers, for example, need several minutes to lock the proper number of satellites, and every time the voltage is cut off, the constellation search needs to start over again. For this reason, an automated double-battery charge system is very convenient. This system for powering the added electronic devices was successfully implemented in the tractors, and it charges both batteries with the engine alternator when the engine is running, but powers all the electronics onboard just with the secondary battery. Only in the unlikely event of running the secondary battery out while the engine is off, the principal battery would power sensors and computers.

The sensors which do not require a special position in the vehicle, such as compasses or inertial measurement units, are better kept in the cabin; they are well protected and connections—power and signal—are kept short. For many sensors, however, there is an advantageous, or even unique, location in the vehicle. Optical encoders, for instance, need to be mounted on the (front-axle) wheel kingpins, and therefore no other placement makes sense to directly track steering angles. The GNSS antenna receives better data when located high, as multipath reflections from the ground and from low vegetation can be avoided; thus, a centered position on the cabin roof is usually the preferred option. Lidars and cameras may be mounted either at the front of the vehicle or on the cabin, depending on the sort of scenes being sensed. The complexity of orchestrating all the sensors, actuators, and computers, while assuring the right voltage power and the synchronization of data acquired at various frequencies, calls for a well designed sensor and system architecture. Figure 12 shows a pictorial representation of a generic sensor network for an intelligent agricultural vehicle.

After the network of sensors onboard has been properly designed, choosing the optimum sensors for each subsystem, selecting their most favorable location within the vehicle, and linking them reliably with the main processor, it is time to revisit the three-layer task classification and discuss on the intelligent capabilities of the architecture proposed. There is no physical embodiment of the task layers because they are conceptually conceived as containers of such virtual elements as information, risk prediction, or expert systems. The Machine Actuation Layer is the layer that holds the set of algorithms conferring intelligence to the system, which may be physically located in the main computer, in several DSPs, or even in a multiplicity of sensor-based agents as the processing board of a smart camera. The design and interrelation of all these algorithms is what some authors consider to be the system architecture, although in reality it is the software architecture. The model envisioned in this article considers the system architecture to be the envelope that comprises both hardware and software architectures. The former refers to the sensor and complementary hardware described along this article. A detailed exposition of the latter would require another paper in the line followed by [8], although a generic view may be outlined here. Taken as a whole, the actuation plan for the vehicle can follow the biology-based reactive approach of the subsumption architecture developed by Brooks [15], or on the contrary it may include a cognitive engine inside the Actuation Layer. While both have been proved to perform successfully for a number of robots in particular situations, agricultural vehicles usually benefit from both approaches, and consequently the best results are often achieved with a hybrid model implementing ideas taken from both. Several software architectures for agricultural off-road vehicles are described in the study cases presented in [14].

Conclusions

New ways of carrying out traditional tasks, such as automatic harvesting, variable rate applications, or water stress site-specific detection can be key in the future to assure novel production systems compatible with population growth and environment preservation. These technologies, however, require the optimum implementation of sensors, actuators, and computers in the so-called intelligent vehicles. This article proposes a sensor architecture to endow agricultural vehicles with the necessary capabilities to perform tasks within the framework of precision agriculture and field robotics. This sensor architecture, in conjunction with the software architecture, constitutes the vehicle system architecture. The hardware architecture developed defines four key groups, or families, of sensors: local perception and vicinity monitoring, global positioning, attitude and control, and non-visual tracking of production parameters. These four sensor families are normally present in most intelligent vehicles, although the particular sensors actually included in each group, depend on each specific application. The arrangement of sensors according to this architecture has favored redundancy and the practical implementation of new technologies in agricultural off-road vehicles, complying with especial requirements of tasks and environments. 

The QUAD-AV Project: multi-sensory approach for obstacle detection in agricultural autonomous robotics 

Raphaël Rouveure et al.

The QUAD-AV project (funded by the ICT-AGRI European Research Program) investigates 

the potential of four technologies: vision/stereovision, ladar, thermography and microwave 

radar.

Within the following paragraphs, four sensor modalities are briefly described, and examples of results are 

presented.

1. Stereovision

.2. Ladar 

Continuously rotating laser range finders (also known as ladar: laser detection and ranging) 

are popular sensors for autonomous vehicles. By measuring the distance at which a rotating 

laser beam is reflected, information about the environment is collected without any 

dependency on lighting conditions.

3. Thermography

The test was performed with FLIR A615 VGA thermal camera. Thermal images were loosely 

synchronized through software, because the camera did not allow synching

4. Microwave radar

The microwave radar technology has been reserved during a long period for military or space 

applications. But today civil applications are interested by some specific characteristics of 

radar technology such as (i) robustness in harsh environmental conditions (using millimeter 

or centimeter wavelength, the radar is not disturbed by dust, rain, light variations, etc.) and 

(ii) ability to achieve measurements over long range distances.

Types Of Smart Sensors In Agriculture For Farming in India

JUNE 3, 2021 

https://www.tractorjunction.com/blog/types-of-smart-sensors-in-agriculture-for-farming-in-india/

1. Optical Sensors In Agriculture

These sensors placed on vehicles or drones, allowing soil reflectance and plant colour data to be gathered and processed. Optical sensors can determine clay, organic matter, and soil moisture content. 

2. Electrochemical Sensors For Soil Nutrient Detection

It helps to collect soil chemical data.

3. Mechanical Soil Sensors For Agriculture

These sensors use a mechanism that cuts through the soil and documents the force measured by pressure scales or load cells.

4. Dielectric Soil Moisture Sensors

It measures moisture levels in the soil.  This allows for the observation of soil moisture conditions when vegetation level is low.

5. Location Sensors In Agriculture

These sensors determine the range, distance and height of any position within the required area. They take the help of GPS satellites for this purpose.

6. Electronic Sensors

They are installed on tractors and other field equipment to check equipment operations.

7. Airflow Sensors

The desired output is the pressure needed to push a decided amount of air into the ground at a prescribed depth. Various soil properties, including compaction, structure, soil type, and moisture level, produce a different identifying signature. 

8. Agriculture IoT Sensors

This sensor provides information such as air temperature, soil temperature at various depths, rainfall, leaf wetness, chlorophyll, wind speed, dew point temperature, wind direction, relative humidity, solar radiation, and atmospheric pressure is measured and recorded at scheduled intervals.

There are number  of sensors used in the category agriculture IOT sensors in areas such as: 

(a) Monitor Climate Conditions

(b) Greenhouse Automation

(c) Crop Management

(d) Cattle Management And Monitoring

(e) Smart Precision Based Agriculture Using Sensors.

Sensors in Tractors - Patents

https://patents.google.com/patent/US4769700A/en

https://patents.google.com/patent/US3812916

https://patents.google.com/patent/EP2624678A1

https://patents.google.com/patent/EP0017418B1

https://patents.google.com/patent/US9095089

https://www.allindianpatents.com/patents/232368-a-sensing-device-for-use-with-a-tractor

https://patents.google.com/patent/US4023622

https://www.quickcompany.in/patents/a-digital-slipmeter-for-wheeled-drive-tractors

https://patents.google.com/patent/US20090236101

Sensor arrangement for a tractor vehicle

http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1476235&dswid=1545

Research Review in IOT Based Smart Tractor for Field Monitoring and Ploughing.

S. Tharik Ahamed, et. al. International Journal of Engineering Research and Applications

www.ijera.com

ISSN: 2248-9622, Vol. 11, Issue 2, (Series-I) February 2021, pp. 32-38

Types of sensors:  used in agricultural fields.

soil water content sensor, 

soil moisture content sensor, 

soil electrical conductivity sensor, pH sensor, colour sensor, temperature sensor, optical sensor, 

mechanical sensors used in agricultural fields.

http://www.ijera.com/papers/vol11no2/Series-1/E1102013238.pdf

Analog Sensor

Providing you the best range of temperature sensors - fits different tractor models.. with effective & timely delivery.

Temperature Sensors - Fits Different Tractor Models.

https://www.vintageautoparts.in/analog-sensor.html

SMART AGRICULTURE

2022

Sehaj Synergy Technologies Pvt. Ltd. 3rd Floor, J-9/J-7/3, Opp JVVNL Power House,Bhagwan Marg, Swage Farm, Sodala, Jaipur-302019

The adoption of Internet of Things (IoT)-related technologies in the Smart Farming domain is rapidly emerging. The ultimate goal is to collect, monitor, and effectively employ relevant data for agricultural processes, with the purpose of achieving an optimized and more environmentally sustainable agriculture.

A low-cost, modular, and Long-Range Wide-Area Network (LoRaWAN)-based IoT platform, denoted as “LoRaWAN-based Smart Farming Modular IoT Architecture” (LoRaFarM), and aimed at improving the management of generic farms in a highly customizable way, is presented. The platform, built around a core middleware, is easily extensible with ad-hoc low-level modules (feeding the middleware with data coming from the sensors deployed in the farm) or high-level modules (providing advanced functionalities to the farmer).

Collected environmental data (air/soil temperature and humidity) related to the growth of farm products (namely grapes and greenhouse vegetables) over a period of three months. A web-based visualization tool for the collected data is also presented, to validate the LoRaFarM architecture.

https://www.sstpl.in/smart-agriculture

Thursday, July 16, 2020

5 innovative ways to use IoT in Agriculture

https://agroautomation.blogspot.com/2020/07/5-innovative-ways-to-use-iot-in.html

krishi IoT: a comprehensive Internet of Things solution to help farmers - Final Report

We presented krishi IoT, a comprehensive Internet of Things solution to help farmers execute agricultural operations in smarter and efficient way. krishi IoT demonstrated smart sensors (krishi IoT prototype devices), an gateway device with GSM based Internet connectivity (krishi IoT gateway) and a mobile app (krishi IoT App) and Web app (powered by IBM Bluemix cloud foundry). krishi IoT devices senses the sensor parameters (such as temperature, humidity, soil moisture, crop images, etc), and relays the information to local krishi IoT gateway. 

https://krishi-iot.blogspot.com/2017/02/krishi-iot-final-report.html

John Deere - Fully Autonomous Tractor

John Deere revealed a fully autonomous tractor that’s ready for large-scale production. Using John Deere Operations Center Mobile, farmers can  start the machine. While the machine is working the farmer can leave the field to focus on other tasks, while monitoring the machine’s status from their mobile device. The autonomous tractor will be available to farmers later this year.

https://ruralradio.com/krvn/video/%F0%9F%9A%9C-john-deere-reveals-fully-autonomous-tractor-agriculture-news-headlines-jan-6-2021/

2021

Smart Farm Firm Fieldin Acquires Tractor Retrofitter Midnight Robotics

Midnight sells a LiDAR retrofit kit, allowing farmers to turn almost any tractor into an autonomous machine.

Written by Ben Wodecki

26th November 2021

https://www.iotworldtoday.com/2021/11/26/smart-farm-firm-fieldin-acquires-tractor-retrofitter-midnight-robotics/

John Deere turns to IoT to make smart farming a reality

British tractor and farm equipment manufacturer John Deere has turned to the Internet of Things (IoT) to boost the capabilities of its innovations.

The firm is in the process of developing new technologies and making use of existing solutions to aid its products for preparing, planting, feeding and harvesting.

As part of this, the company has started using Telit’s deviceWISE Industrial IoT (IioT) platform at its factory operations, to collect and analyse real-time assembly information to improve line efficiency, prevent unplanned downtime, and improve efficiency throughout the supply chain.

https://internetofbusiness.com/john-deere-turns-iot-smart-farming/

IoT based Tractors - Benefits

Tractors can work 40x faster and be significantly less expensive than off-farm labor. Most farmers, however, can’t afford to own their own tractors and most tractor service providers operate well below their potential.

Hello Tractor has developed a solution to address these problems. The company has developed a low-cost monitoring device that when placed on a tractor provides the owner with powerful software and analytics tools to ensure tractors are both profitable and properly cared for. The software connects tractor owners to farmers in need of tractor services – just like Uber for tractors. 

https://olc.worldbank.org/content/iot-internet-tractors

Development of an Iot Based Tractor Tracking Device to Be Used as a Precision Agriculture Tool for Turkey's Agricultural Tractors

October 2019

https://www.researchgate.net/publication/339446763_Development_of_an_Iot_Based_Tractor_Tracking_Device_to_Be_Used_as_a_Precision_Agriculture_Tool_for_Turkey's_Agricultural_Tractors  

Research Review in IOT Based Smart Tractor

http://www.ijera.com/papers/vol11no2/Series-1/E1102013238.pdf

Types Of Smart Sensors In Agriculture For Farming in India

JUNE 3, 2021 

https://www.tractorjunction.com/blog/types-of-smart-sensors-in-agriculture-for-farming-in-india/

1. Optical Sensors In Agriculture

These sensors placed on vehicles or drones, allowing soil reflectance and plant colour data to be gathered and processed. Optical sensors can determine clay, organic matter, and soil moisture content. 

2. Electrochemical Sensors For Soil Nutrient Detection

It helps to collect soil chemical data.

3. Mechanical Soil Sensors For Agriculture

These sensors use a mechanism that cuts through the soil and documents the force measured by pressure scales or load cells.

4. Dielectric Soil Moisture Sensors

It measures moisture levels in the soil.  This allows for the observation of soil moisture conditions when vegetation level is low.

5. Location Sensors In Agriculture

These sensors determine the range, distance and height of any position within the required area. They take the help of GPS satellites for this purpose.

6. Electronic Sensors

They are installed on tractors and other field equipment to check equipment operations.

7. Airflow Sensors

The desired output is the pressure needed to push a decided amount of air into the ground at a prescribed depth. Various soil properties, including compaction, structure, soil type, and moisture level, produce a different identifying signature. 

8. Agriculture IoT Sensors

This sensor provides information such as air temperature, soil temperature at various depths, rainfall, leaf wetness, chlorophyll, wind speed, dew point temperature, wind direction, relative humidity, solar radiation, and atmospheric pressure is measured and recorded at scheduled intervals.

There are number  of sensors used in the category agriculture IOT sensors in areas such as: 

(a) Monitor Climate Conditions

(b) Greenhouse Automation

(c) Crop Management

(d) Cattle Management And Monitoring

(e) Smart Precision Based Agriculture Using Sensors.

Digital Twin of Tractors

CLAAS TRACTOR - Case Study - VIRTUAL TWIN DESIGN and MANUFACTURING PROCESS VALIDATION

3D models are now used across all departments at CLAAS Tractor.

Having a virtual twin of both its products and the factory is having a particularly positive impact on CLAAS Tractor’s design to manufacturing process, helping to ensure that each component can be produced within the Le Mans factory using existing equipment and will work within the finished tractor.

https://www.3ds.com/sites/default/files/2020-09/claas-tradtor-case-study-en-web-2020.pdf

Detailed note on the above case study: 

Design - Manufacturing Interface Productivity Engineering Using 3D Modeling and Virtual Simulation - Case Study

Autonomous tractor captures imagination

Robin Booker

March 4, 2021

https://www.producer.com/crops/autonomous-tractor-captures-imagination/

Digital twins in smart farming

Cor Verdouw, Bedir Tekinerdogan Adrie Beulens, Sjaak Wolfert

Agricultural Systems, Volume 189, April 2021, 103046

https://www.sciencedirect.com/science/article/pii/S0308521X20309070#!

Introducing digital twins to agriculture

Christos Pylianidis, Sjoukje Osinga, Ioannis N.Athanasiadis

Computers and Electronics in Agriculture

Volume 184, May 2021, 105942

https://www.sciencedirect.com/science/article/pii/S0168169920331471

Outlining the mission profile of agricultural tractors through CAN-BUS data analytics

Computers and Electronics in Agriculture

Volume 184, May 2021, 106078

https://www.sciencedirect.com/science/article/abs/pii/S016816992100096X

Research Review in IOT Based Smart Tractor for Field Monitoring and Ploughing.

S. Tharik Ahamed, et. al. International Journal of Engineering Research and Applications

www.ijera.com

ISSN: 2248-9622, Vol. 11, Issue 2, (Series-I) February 2021, pp. 32-38

Types of sensors:  used in agricultural fields.

soil water content sensor, 

soil moisture content sensor, 

soil electrical conductivity sensor, pH sensor, colour sensor, temperature sensor, optical sensor, 

mechanical sensors used in agricultural fields.

http://www.ijera.com/papers/vol11no2/Series-1/E1102013238.pdf

John Deere’s tech-fueled mission to feed a hungry world, one seed at a time

by Karen Field | Jan 12, 2021 3:58pm

https://www.fierceelectronics.com/electronics/john-deere-s-tech-fueled-mission-to-feed-a-hungry-world-one-seed-at-a-time

Analog Sensor

Providing you the best range of temperature sensors - fits different tractor models.. with effective & timely delivery.

Temperature Sensors - Fits Different Tractor Models.

https://www.vintageautoparts.in/analog-sensor.html

16 February 2018

Autonomous Solutions, Inc. (ASI)

Autonomous Solutions, Inc. (ASI) has been named a finalist for the 2018 Edison Awards for its work in the development of the Autonomous Tractor Concept with CNH Industrial and its brands Case IH and New Holland Agriculture.

The Autonomous Tractor Concept is the first fully functioning large scale autonomous tractor. It is capable of autonomous seeding, planting, and tillage for broad acre and row crop farming. The vehicles are also capable of obstacle detection which will enhance safety in the agriculture industry.
https://www.roboticstomorrow.com/news/2018/02/16/autonomous-solutions-named-finalist-for-prestigious-award-for-autonomous-tractor-concept/11388/


Design Award for Magnum,  Case IH Autonomous Tractor
https://www.farmersjournal.ie/design-award-for-case-ih-autonomous-tractor-335576


Autonomous Tractor Corp

http://www.autonomoustractor.com
https://www.farmjournalagtech.com/company/autonomous-tractor-corp



Some of the key players in the autonomous tractor market include Aurotron Pty Ltd, John Deere US, Case IH, Kubota Tractor Corporation, New Holland, AGCO Corporation, Yanmar, Kinze Manufacturing, Autonomous Tractor Corporation and Fendt Corporation.
http://markets.businessinsider.com/news/stocks/autonomous-tractor-global-market-outlook-2017-2023-1002949265

Not a Tractor

Jun 30, 2017

New approach to an Autonomous Farm Power Equipment

A Canadian engineer and inventor rethought the idea of a farm power unit to create a new way to maximize labor-free farm work.
http://www.farmindustrynews.com/equipment/new-approach-autonomous-tractor


June 1, 2017
John Deere Rolls Out Smarter S700 Combines & Front-End Equipment
New harvesting solutions includes 4 combine models and new headers

John Deere introduces its smarter S700 Combines for model year 2018 production
https://www.deere.com/en_US/corporate/our_company/news_and_media/press_releases/2017/agriculture/2017jun1_s700_combine.page

Developments in autonomous tractors

19 Jul 2017

1. Technology underpinning autonomous tractors is relatively advanced
2. The technology is in the early stages of commercialisation.
3. Tractor manufacturers i.e. John Deere and CNH have successfully tested concept vehicles.
https://grdc.com.au/resources-and-publications/grdc-update-papers/tab-content/grdc-update-papers/2017/07/developments-in-autonomous-tractors


30 August 2016

CNH Industrial brands - Magnum concept autonomous tractor

                                 Driverless Tractor along with Case IH Early Riser 2150 Planter



CNH Industrial brands reveal concept autonomous tractor development: driverless technology to boost precision and productivity

Based on the existing Case IH Magnum and New Holland T8 high-horsepower conventional tractors, and using GPS in conjunction with the most accurate satellite correction signals for ultra-precise guidance and immediate recording and transmission of field data, the CNH Industrial autonomous tractor concept has been designed to allow completely remote deployment, monitoring and control of the machines.

CNH Industrial’s autonomous technology to completely remove the operator from the cab – in the case of the cabless concept Case IH Magnum.

For more details see the press release
https://media.cnhindustrial.com/EMEA/CNH-INDUSTRIAL-CORPORATE/cnh-industrial-brands-reveal-concept-autonomous-tractor-development--driverless-technology-to-boost-/s/a2259742-061a-412a-8a12-d307dbaedd88


https://www.caseih.com/northamerica/en-us/Pages/campaigns/autonomous-concept-vehicle.aspx

Smart Tractor

____________

____________



Nonlinear modeling and Analyzing of Tractor-Semitrailer Driving Stability Based on Simulink
Chuan-jin Ou et al.
Page 104
International Symposium for Intelligent Transportation and Smart City (ITASC) 2017 Proceedings: Branch of ISADS (The International Symposium on Autonomous Decentralized Systems)
Xiaoqing Zeng, Xiongyao Xie, Jian Sun, Limin Ma, Yinong Chen
Springer, 06-Apr-2017 - Technology & Engineering - 301 pages
This book presents research advances in intelligent transportation and smart cities in detail, mainly focusing on green traffic and urban utility tunnels, presented at the 3rd International Symposium for Intelligent Transportation and Smart City (ITASC) held at Tongji University, Shanghai, on May 19–20, 2017. It discusses a number of hot topics, such as the 2BMW system (Bus, Bike, Metro and Walking), transportation safety and environmental protection, urban utility design and application, as well as the application of BIM (Building Information Modeling) in city design. By connecting the theory and applications of intelligent transportation in smart cities, it enhances traffic efficiency and quality. The book gathers numerous selected papers and lectures, including contributions from respected scholars and the latest engineering advances, to provide guidance to researchers in the field of transportation and urban planning at universities and in related industries.
https://books.google.co.in/books?id=oNufDgAAQBAJ

Navigation of Autonomous Tractor: Positioning and Sensors 

by Tofael Ahamed (Author)
188 pages
Publisher: LAP LAMBERT Academic Publishing (September 30, 2011)
https://www.amazon.com/Navigation-Autonomous-Tractor-Positioning-Sensors/dp/3846519405

www.mdpi.com/journal/sensors
journal/sensors

Sensor Architecture and Task Classification for Agricultural Vehicles and Environments

Francisco Rovira-Más
Departamento de Ingeniería Rural y Agroalimentaria, Universidad Politécnica de Valencia, Camino de Vera s/n,  46022 Valencia, Spain;
Received: 20 October 2010; in revised form: 26 November 2010 / Accepted: 1 December 2010 / Published: 8 December 2010

The complexity inherent to intelligent vehicles is rooted in the selection and coordination of the optimum sensors, the computer reasoning techniques to process the acquired data, and the resulting control strategies for automatic actuators. The article proposes a sensor architecture especially adapted to cope with them. The strategy proposed groups  sensors into four specific subsystems: global localization, feedback control and vehicle pose, non-visual monitoring, and local perception. The designed architecture responds to vital vehicle tasks classified within three layers devoted to safety, operative information, and automatic actuation.


The solutions brought by the new technologies, i.e., precision farming and
agricultural robotics, seem to better match the revolution sought in farming. The incorporation of the
technologies of precision farming and agricultural robotics, into agricultural production not only benefits productivity and environmental conditions, but it also improves the working conditions of farm managers, laborers, and vehicle operators.


The farm machinery automation started as early as 1924, when Willrodt  designed a steering attachment capable of following furrows to guide a machine automatically across the field. Until the appearance of
electronics and computers, the sensing devices used to automate operations were purely mechanical. In fact, the majority of sensors used in agricultural vehicles have been related to autonomous navigation. For this purpose, the devices used both in North America  and in Europe  have been mechanical feelers, computer vision cameras, global positioning systems, geomagnetic direction sensors, laser scanners, and ultrasonic rangefinders. However, there are many other sensors of frequent use in precision agriculture such as yield monitoring estimators, soil properties probes, moisture content analyzers, and many others being developed at present. The usage of sensors in agricultural vehicles has evolved through time.

In a study of patents devoted to in-field automatic navigation, Rovira-Más found that beacons, pseudolite localization devices, and optical sensors excluding cameras were popular during the period
1985–2000, but inertial measurement units, GPS-based applications, and imaging devices became
predominant in the 2001–2008 period. The particular case of GPS can be justified by the cancellation
of selective availability in May 2000, which permitted the use of more accurate positioning data for
civilian applications.

Blackmore et al. provide a list of behaviors for an autonomous tractor, where simple processes as watching and waiting mingle with complex tasks such as route planning and navigation.

Typical agricultural vehicles weigh between 2 and 20 tons, incorporate diesel engines with a rated
power between 20 kW and 500 kW, and can reach retail prices over $300,000.

The sensor architecture proposed to meet the requirements of agricultural environments, vehicles,
and tasks is articulated around four structural subsystems: local perception, global localization, actuation and control, and data processing. The fourth subsystem, data processing, comprises the set of computers, processing units, DSPs (digital signal processors), and embedded controllers hosting decision making algorithms, receiving sensor data, and sending actuation commands according to a given software architecture. The other three subsystems incorporate a multiplicity of sensors that have been grouped and explained  in the subsections 4.1 to 4.4.

The complete automation of an agricultural vehicle involves many more functions than automatic steering. Navigation, for example, may require gear shifting, brake activation, throttle control, or differential locking. All these actions, when executed automatically, need to track the position of levers and pedals with potentiometers and encoders. An intelligent implement, for instance, needs to sense its position (up for road  traveling and headlands; down for farming) as well as the drag force incurred by the pulling vehicle (axle load cells).


Lidar

A more convenient alternative to map ranges
is offered by lidar (light detection and ranging) heads, optical devices based on the principle of
time-of-flight whose beams of coherent light—usually laser—provide a way to estimate ranges with
high resolution. The main disadvantage of lidars is the need to spin the beam in order to cover the
widest possible area in front of the vehicle, typically between 180m and 270m, which requires a
mechanism permanently in rotation. The speed of this circular movement limits the real-time
capabilities of the sensor.

4.1. Sensors for Local Perception and Vicinity Monitoring
4.2. Sensors for Global Localization
4.3. Sensors for Vehicle Attitude and Motion Control
4.4. Non-visual Sensors for Monitoring Production Parameters



4.5. Onboard Integration of the Complete Sensor Network

A second battery, independent from the vehicle’s own battery, is always very helpful to preserve the desired autonomy of the diesel engine.

For many sensors, there is an advantageous, or even unique, location in the vehicle.

Taken as a whole, the actuation plan for the vehicle can follow the biology-based reactive approach of the subsumption architecture developed by Rodney Brooks , or on the contrary it may include a cognitive engine inside the Actuation Layer.

https://www.mdpi.com/1424-8220/10/12/11226/htm

Mechatronics and Intelligent Systems for Off-road Vehicles

Francisco Rovira Más, Qin Zhang, Alan C. Hansen
Springer Science & Business Media, 30-Nov-2010 - Technology & Engineering - 277 pages


Rapid developments in electronics over the past two decades have induced a move from purely mechanical vehicles to mechatronics design. Recent advances in computing, sensors, and information technology are pushing mobile equipment design to incorporate higher levels of automation under the concept of intelligent vehicles. Mechatronics and Intelligent Systems for Off-road Vehicles introduces this new concept, and provides an overview of the recent applications and future approaches within this field. Real examples are provided of vehicles designed to move in off-road environments, including agriculture, forestry, and construction machines. These examples describe and illustrate features such as automatic steering, safeguarding, and precision agriculture capabilities.

Mechatronics and Intelligent Systems for Off-road Vehicles will be of great interest to professional engineers and researchers in vehicle automation, robotics, and the application of artificial intelligence to mobile equipment. 

https://books.google.co.in/books?id=z2cLPFmgQX0C


Control of Autonomous Tractor
Master's Thesis at Ørsted•DTU, Automation
March 31st, 2006
Authors:Asbjørn Mejnertsen, Anders Reske-Nielsen
http://etd.dtu.dk/thesis/191163/oersted_dtu2633.pdf

Company and Specific Product Based Developments

Mahindra Showcases its First Ever Driverless Tractor in India

September 19, 2017

Developed at Mahindra Research Valley in Chennai, Driverless Tractor technology set to take farm mechanization to new heights


_________________


__________________

Driverless Tractor set to make farming more productive & profitable, reduce health hazard for farmers and change the future of food production
This technology is designed to enable tractors to perform a variety of farming applications & operate varied implements
The tractor equipped with this technology can be programmed to carry out specific tasks & can also be operated remotely to perform in the field
To be available commercially from early 2018, in a phased manner

Mahindra & Mahindra Ltd., displayed its first ever Driverless Tractor. Developed at the Mahindra Research Valley, the Group’s hub of innovation and technology located in Chennai.

The driverless tractor is all set to redefine the mechanization process for the global farmer.

This innovation will change the future of farming by increasing productivity, leading to increased food production to feed the growing needs of the world. This innovative mechanization for the global farming community, in line with Mahindra's Farming 3.0 proposition.

Rajesh Jejurikar, President, Farm Equipment Sector, Mahindra & Mahindra Ltd. said, “Today the need for farm mechanisation is higher than ever before, due to labour shortage and the need to improve productivity and farm produce yield. Coupled with our ‘DiGiSENSE’ technology that we launched last year, the driverless tractor offers a distinct advantage to the Indian farmer by bringing an unprecedented level of intelligence to the tractor”.

This technology will be deployed across Mahindra tractor platforms in due course of time. It will also be deployed across international markets such as USA and Japan.,  Mahindra plans to offer the driverless tractor technology across its range of tractors from 20 HP to 100 HP over a period of time.

Unique Features of the Driverless Tractor

The pioneering driverless tractor is equipped with state-of-the-art technology and boasts of several unique features:

Auto steer – GPS based technology that enables a tractor to travel along a straight line.

Auto-headland turn – Enables the tractor to orient itself along adjacent rows for continuous operation without any steering input from the farmer.

Auto-implement lift – Feature in the tractor that automatically lifts the work tool from the ground at the end of a row and lowers the tool after the tractor has oriented itself for operation at the next row.

Skip passing - This technology feature enables the tractor to steer to the next row for continuous operation without any intervention of the driver.

Safety Features

In addition, the driverless tractor is also equipped with some unique safety features as below:

Geofence lock - Prevents tractor from going outside the boundaries of the farm

Control via Tablet User Interface – Enables the farmer to program various inputs needed to farm efficiently. Also offers controls to prevent the tractor veering off from its intended path or desired operation. He can also control the tractor remotely via a tablet.

Remote Engine Start Stop - Ability to stop the engine and hence, bring the tractor to a complete STOP if needed in cases of emergency

With the deployment of this technology on Mahindra tractors, the farmers can work their fields for long hours without exposing themselves to harsh weather or difficult operating conditions. They can also protect themselves from potential health hazards resulting from operations like insecticide spraying which now can be done without human intervention. It will also ensure better quality and consistency in farming operations, leading to higher productivity and farm produce yields.
http://www.mahindra.com/news-room/press-release/mahindra-showcases-its-first-ever-driverless-tractor-in-india


_________________


_________________

New Holland Fiat (India) launched the GPS and the GPRS technologies on its tractors under
the name of "Sky Watch" in 2012.  This technology will enable farmers to monitor and trace their tractors'' health and performance for better control and
maintenance, easy operations, and improved productivity. Tractor owners  can know the hourly
usage, performance parameters, and the maintenance when the tractor is rented out.


Automating Agriculture


Internet Of Things Based Innovative Agriculture Automation Using AGRIBOT
SSRG International Journal of Electronics and Communication Engineering - (ICRTECITA-2017) - Special Issue - March 2017
http://www.internationaljournalssrg.org/IJECE/2017/Special-Issues/ICRTECITA/IJECE-ICRTECITA-P132.pdf


Agricultural Automation: Fundamentals and Practices
Qin Zhang, Francis J. Pierce
CRC Press, 19-Apr-2016 - Science - 411 pages


Agricultural automation is the core technology for computer-aided agricultural production management and implementation. An integration of equipment, infotronics, and precision farming technologies, it creates viable solutions for challenges facing the food, fiber, feed, and fuel needs of the human race now and into the future. Agricultural Automation: Fundamentals and Practices provides a comprehensive introduction of automation technologies for agriculture.

From basics to applications, topics in this volume include:

Agricultural vehicle robots and infotronic systems
Precision agriculture, with its focus on efficiency and efficacy of agricultural inputs and the spatial and temporal management of agricultural systems
Specific agricultural production systems, including those related to field crops, cotton, orchards and vineyards, and animal housing and production
Automation relative to specific inputs in agricultural production systems, such as nutrition management and automation, automation of pesticide application systems, and automated irrigation management with soil and canopy sensing
Liability issues with regard to surrounding awareness and worksite management
Postharvest automation—perhaps the most advanced component of agricultural production in terms of automation and an important factor in global agriculture
Agricultural mechanization, one of the top ranked engineering accomplishments in the past century, has created revolutionary change in crop production technology and made it possible to harvest sufficient products to meet the population’s continuously growing needs. Continued progress is essential to the future of agriculture. This book provides an up-to-date overview of the current state of automated agriculture and important insight into its upcoming challenges.
https://books.google.co.in/books?id=TlrNBQAAQBAJ


Agricultural Mechanization and Automation - Volume I

Paul McNulty, Patrick M. Grace
EOLSS Publications, 28-Jul-2009 - Technology & Engineering - 518 pages


Agricultural Mechanization and Automation is a component of Encyclopedia of Food and Agricultural Sciences, Engineering and Technology Resources in the global Encyclopedia of Life Support Systems (EOLSS), which is an integrated compendium of twenty one Encyclopedias.

The mechanization of farming practices throughout the world has revolutionized food production, enabling it to maintain pace with population growth except in some less-developed countries, most notably in Africa. Agricultural mechanization has involved the partial or full replacement of human energy and animal-powered equipment (e.g. plows, seeders and harvesters) by engine-driven equipment. The theme on Agricultural Mechanization and Automation cover six main topics:  Technology and Power in Agriculture; Farm Machinery; Facilities and Equipment for Livestock Management; Environmental Monitoring; Recovery and Use of Wastes and by-Products; Slaughtering and Processing of Livestock, which are then expanded into multiple subtopics, each as a chapter.  These two volumes are aimed at the following five major target audiences: University and College students Educators, Professional practitioners, Research personnel and Policy analysts, managers, and decision makers and NGOs.
https://books.google.co.in/books?id=G-GvCwAAQBAJ

FARMBOT – SMALLSCALE, LOW COST FARMING
AUTOMATION SYSTEM
PROJECT REFERENCE NO.: 39S_BE_1871
COLLEGE : ACHARYA INSTITUTE OF TECHNOLOGY, BENGALURU
BRANCH : DEPARTMENT OF ELECTRONICS AND COMMUNICATION
 ENGINEERING
GUIDE : PROF. JAYALAXMI H
STUDENTS : MR. CHETHAN M
 MR. AMARESH G
 MR. ANUDEEP C
 MR. KRISHNA

http://www.kscst.iisc.ernet.in/spp/39_series/SPP39S/02_Exhibition_Projects/135_39S_BE_1871.pdf

ADVANCED TECHNOLOGIES AND AUTOMATION IN AGRICULTURE
J. De Baerdemaeker, H. Ramon, and J. Anthonis
K.U. Leuven, Leuven, Belgium
H. Speckmann , and A. Munack
Federal Agricultural Research Centre(FAL), Braunschweig, Germany
http://www.eolss.net/sample-chapters/c18/e6-43-35-04.pdf


Updated 10.5.2022,  29.1.2022,  18.1.2022, 28 May 2021

2018 - 10 March 2018, 27 February