Changing of environmental conditions and shortage of water demands a system that can manage irrigation efficiently. Autonomous irrigation systems are developed to optimize water use for agricultural crops. In dry areas or in case of inadequate rainfall, irrigation becomes difficult. So, it needs to be automated for proper yield and handled remotely for farmer safety. The aim of this study is to review the needs of soil moisture sensors in irrigation, sensor technology and their applications in irrigation scheduling and, discussing prospects. The review further discusses the literature of sensors remotely communicating with self-propelled sprinkler irrigation systems, distributed wireless sensor networks, sensors and integrated data management schemes and autonomous sprinkler control options. On board and field-distributed sensors can collect data necessary for real-time irrigation management decisions and transmit the information directly or through wireless networks to the main control panel or base computer. Communication systems such as cell phones, satellite radios, and internet-based systems are also available allowing the operator to query the main control panel or base computer from any location at any time. Selection of the communication system for remote access depends on local and regional topography and cost. Traditional irrigation systems may provide unnecessary irrigation to one part of a field while leading to a lack of irrigation in other parts. New sensors or remotely sensing capabilities are required to collect real time data for crop growth status and other parameters pertaining to weather, crop and soil to support intelligent and efficient irrigation management systems for agricultural processes. Further development of wireless sensor applications in agriculture is also necessary for increasing efficiency, productivity and profitability of farming operations.
Keywords: intelligent sprinkler irrigation, precision agriculture, soil moisture sensors, wireless sensor network, remote data management, autonomous system
DOI: 10.25165/j.ijabe.20181101.3557

Citation: Zhu X Y, Chikangaise P, Shi W D, Chen W H, Yuan S Q. Review of intelligent sprinkler irrigation technologies for remote autonomous system. Int J Agric & Biol Eng, 2018; 11(1): 23–30.

Review of intelligent sprinkler irrigation technologies for remote autonomous system

MEMS (Micro-Electro-Mechanical Systems) gyroscope is the core component in the posture recognition and assistant positioning, of which the complex noise limits its performance. It is essential to filter the noise and obtain the true value of the measurements. Then an adaptive filtering method was proposed. Firstly, noises of MEMS gyroscope were analyzed to build the basic framework of the dynamic noise model. Secondly, the dynamic Allan variance was improved with a novel truncation window based on the entropy features, which referred to the parameters in the noise model. Thirdly, the adaptive Kalman filter was derived from the dynamic noise model. Finally, the simulation and experiment were carried out to verify the method. The results prove that the improved dynamic Allan variance can extract noise feature distinctly, and the filtering precision in the new method is relatively high.

Adaptive filtering for MEMS gyroscope with dynamic noise model.

Path detection for autonomous traveling in orchards using patch-based CNN

Application of a 3D tractor-driving simulator for slip estimation-based path-tracking control of auto-guided tillage operation

Design and validation testing of a complete paddy field-coverage path planner for a fully autonomous tillage tractor

The use of low-cost single GPS receivers and inertial sensors for auto-guidance applications has been limited by their reduced accuracy and signal drift over time compared to real-time kinematic (RTK) differential GPS units and fiber-optic gyroscope (FOG) sensors. In this study, a prototype low-cost GPS/INS integrated system consisting of a triangle-shaped array of three Garmin 19x GPS receivers and an Xsens inertial measurement unit (IMU) to improve the accuracy of position and heading angle measured with a single GPS receiver was developed. A triangular algorithm that uses data collected from the three single GPSs mounted on the angular points of a triangular frame was designed. A sensor fusion algorithm based on the Kalman filter combining the GPS and IMU data was developed by integrating position data and heading angles of a triangular array of GPS receivers. The optimized values of two noise covariance matrixes (Q and R) for the Kalman filtering were determined using the Central Composite Design (CCD) method. As compared to the use of a single Garmin GPS receiver, use of the developed GPS/INS system showed improved accuracy performance in terms of both position and heading angle, with reductions in root mean square errors (RMSEs) from 2.7 m to 0.64 m for position and from 8.9o to 2.1o for heading angle. The accuracy improvements show new potential for agricultural auto-guidance applications.
Keywords: global positioning system, tractor, automatic navigation, sensor fusion, Kalman filter, inertial sensor, heading angle
DOI: 10.3965/j.ijabe.20171002.3070

Citation: Han X Z, Kim H J, Jeon C W, Moon H C, Kim J H. Development of a low-cost GPS/INS integrated system for tractor automatic navigation. Int J Agric & Biol Eng, 2017; 10(2): 123–131.

Development of a low-cost GPS/INS integrated system for tractor automatic navigation

High-precision position estimations of agricultural mobile robots (AMRs) are crucial for implementing control instructions. Although the global navigation satellite system (GNSS) and real-time kinematic GNSS (RTK-GNSS) provide high-precision positioning, the AMR accuracy decreases when the signals interfere with buildings or trees. An improved position estimation algorithm based on multisensor fusion and autoencoder neural network is proposed. The multisensor, RTK-GNSS, inertial-measurement-unit, and dual-rotary-encoder data are fused with Extended Kalman filter (EKF). To optimize the EKF noise matrix, the autoencoder and radial basis function (ARBF) neural network was used for modeling the state equation noise and EKF measurement equation. A multisensor AMR test platform was constructed for static experiments to estimate the circular error probability and twice-the-distance root-mean-squared criteria. Dynamic experiments were conducted on road, grass, and field environments. To validate the robustness of the proposed algorithm, abnormal working conditions of the sensors were tested on the road. The results showed that the positioning estimation accuracy was improved compared to the RTK-GNSS in all three environments. When the RTK-GNSS signal experienced interference or rotary encoders failed, the system could still improve the position estimation accuracy. The proposed system and optimization algorithm are thus significant for improving AMR position prediction performance.

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Improved Position Estimation Algorithm of Agricultural Mobile Robots Based on Multisensor Fusion and Autoencoder Neural Network

This article reports a simulation study conducted using a three-dimensional tractor-driving simulator to develop an implement control algorithm and evaluate the tillage coverage and field efficiencies of a virtual autonomous tractor following paths generated based on different headland turning methods. To minimize the no-tilled or unnecessarily tilled areas that occurred in our previous study, a tillage implement control algorithm was designed by enabling the raising or lowering of a three-point hitch with appropriate delay times. The effects of headland turning methods, i.e., X-shaped, R-shaped, and C-shaped turns, on path tracking and full-path simulation of autonomous tillage operations were studied. The results of the simulation studies were evaluated in terms of tracking error, skipped area, and field efficiency. The developed implement control algorithm effectively reduced both no-tilled and unnecessarily tilled areas in comparison with those obtained without the implement control algorithm. The magnitudes of changes in no-tilled and unnecessarily tilled areas were from 4.5 to 0.4 m2 and from 4.6 to 0.3 m2, respectively. In a study in which an autonomous tractor followed a desired path in a virtual field of 100 m × 40 m at a constant traveling speed of 4 km h−1 designed to investigate the influence of the three different headland turning methods, the simulator allowed a quantifiable comparison of tillage operation performance for the various headland turns by showing lateral deviations < 9 cm, heading angle errors < 16°, ratios of skipped area < 2%, and field efficiencies ranging from 81.3 to 86.6%. Full-path simulation tests of the autonomous tillage operation conducted in virtual rectangular fields with a length of 100 m and various widths showed that field efficiency was inversely proportional to the field width. The use of the 3D tractor-driving simulator was effective for designing tillage implement control algorithms and studying the effects of headland turning methods on path tracking. These results are applicable to the development of path generation and tracking algorithms suitable for autonomous tillage operations.

Simulation Study to Develop Implement Control and Headland Turning Algorithms for Autonomous Tillage Operations

. Tracked vehicle platforms are widely employed in different areas where high levels of mobility are required such as agriculture and military due to their high traction efficiency in rough and moist environments. Automatic steering of a head feed combine harvester with crawler tracks that simultaneously modulates the speed and direction of the vehicle requires precise control of relative angular velocities of the left and right tracks and coupled operation of clutch and brake. This paper describes the development of a tracked vehicle kinematic model-based controller for an auto-guided head feed combine harvester that enhance its capability to follow along predefined a straight working line. A variable-speed hydrostatic transmission (HST) was employed to provide the functions of clutch and brake of the tested combine harvester. The path tracking and steering algorithms were implemented using a fuzzy logic method that considers the non-linear characteristics of the HST mechanism and various steering variables, such as angular velocity, lateral deviation and heading error. Prior to field testing, a feasibility of using the developed steering control algorithms was investigated in a simulation environment developed in our previous research. A rice harvesting test was conducted with an autonomous combine harvester equipped with the developed steering controller in a paddy field. The validity of the developed simulation was confirmed by comparing the tracking trajectory with field test in same condition. The autonomous combine harvester followed the predefined line path with acceptable tracking, showing lateral error and heading error within 15cm and 3deg respectively.

Chan Woo Jeon

Papers

Application of a 3D tractor-driving simulator for slip estimation-based path-tracking control of auto-guided tillage operation

Development of a low-cost GPS/INS integrated system for tractor automatic navigation

Improved Position Estimation Algorithm of Agricultural Mobile Robots Based on Multisensor Fusion and Autoencoder Neural Network

Simulation Study to Develop Implement Control and Headland Turning Algorithms for Autonomous Tillage Operations

Fuzzy logic-based Steering Controller for an Autonomous Head -Feed Combine Harvester