Development of an Electro-Mechanical System to Identify & Map Adverse Soil Compaction Using GIS&GPS

A stand-alone electro-mechanical system with a 32-inch disc coulter was developed and tested to identify soil compaction in a 1-acre field located at the University of Kentucky Animal Research Center (UKARC). The system was evaluated by making four passes in the square grid cell. With the aid of hydraulic actuation, the coulter oscillated between depths of 100mm (4-in) and 330mm (13-in) as it moved forward and recorded the vertical impedance force given by the soil continuously. Forty standard soil cone penetrometer measurements along the diagonals to a depth of 400mm (16-in) were taken and the average cone indices (MPa) at different depths for the entire grid cell were compared to the average coulter indices (CuI(N/mm), defined as the penetration force divided by the perimeter of the coulter disc in contact with soil) at corresponding depths. Ten soil bulk density measurements were taken at depths of (100,150,200,250,300mm) per each grid cell and averaged. Pearson correlation coefficient (r) and coefficient of determination (r2) were found to be 0.716 and 0.51between CuI and CI respectively. The depth and spatial locations of maximum vertical impedance force and maximum CuI were determined

Development of an Electro-Mechanical System to Identify & Map Adverse Soil Compaction Using GIS&GPS

A stand-alone electro-mechanical system with a 32-inch disc coulter was developed and tested to identify soil compaction in a 1-acre field located at the University of Kentucky Animal Research Center (UKARC). The system was evaluated by making four passes in the square grid cell. With the aid of hydraulic actuation, the coulter oscillated between depths of 100mm (4-in) and 330mm (13-in) as it moved forward and recorded the vertical impedance force given by the soil continuously. Forty standard soil cone penetrometer measurements along the diagonals to a depth of 400mm (16-in) were taken and the average cone indices (MPa) at different depths for the entire grid cell were compared to the average coulter indices (CuI(N/mm), defined as the penetration force divided by the perimeter of the coulter disc in contact with soil) at corresponding depths. Ten soil bulk density measurements were taken at depths of (100,150,200,250,300mm) per each grid cell and averaged. Pearson correlation coefficient (r) and coefficient of determination (r2) were found to be 0.716 and 0.51between CuI and CI respectively. The depth and spatial locations of maximum vertical impedance force and maximum CuI were determined

Multi-Robot System Control Architecture (MRSCA) for Agricultural Production

Coordinating multiple autonomous robots for achieving an assigned collective task presents a complex engineering challenge. In this paper multi robot system control architecture (MRSCA) for the coordination of multiple agricultural robots is developed. The two important aspects of MRSCA; coordination strategy and inter-robot communication were discussed with typical agricultural tasks as examples. Classification of MRS into homogeneous and heterogeneous robots was done to identify appropriate form of cooperative behavior and inter-robot communication. The framework developed, proposes that inter-robot communication is not always required for a MRS. Three types of cooperative behaviors; No-cooperation, modest cooperation and absolute cooperation for a MRS were devised for accomplishing a variety of coordinated operations in agricultural production

Inter-row Robot Navigation using 1D Ranging Sensors

In this paper a fuzzy logic navigation controller for an inter-row agricultural robot is developed and evaluated in laboratory settings. The controller receives input from one-dimensional (1D) ranging sensors on the robotic platform, and operated on ten fuzzy rules for basic row-following behavior. The control system was implemented on basic hardware for proof of concept and operated on a commonly available microcontroller development platform and open source software libraries. The robot platform used for experimentation was a small tracked vehicle with differential steering control. Fuzzy inferencing and defuzzification, step response and cross track error were obtained from the test conducted to characterize the transient and steady state response of the controller. Controller settling times were within 4 seconds. Steady state centering errors for smooth barrier navigation were found to be within 3.5% of center for 61 cm wide solid barrier tests, and within 38% for simulated 61 cm corn row tests

Inter-row Robot Navigation using 1D Ranging Sensors

In this paper a fuzzy logic navigation controller for an inter-row agricultural robot is developed and evaluated in laboratory settings. The controller receives input from one-dimensional (1D) ranging sensors on the robotic platform, and operated on ten fuzzy rules for basic row-following behavior. The control system was implemented on basic hardware for proof of concept and operated on a commonly available microcontroller development platform and open source software libraries. The robot platform used for experimentation was a small tracked vehicle with differential steering control. Fuzzy inferencing and defuzzification, step response and cross track error were obtained from the test conducted to characterize the transient and steady state response of the controller. Controller settling times were within 4 seconds. Steady state centering errors for smooth barrier navigation were found to be within 3.5% of center for 61 cm wide solid barrier tests, and within 38% for simulated 61 cm corn row tests

Multi-Robot System Control Architecture (MRSCA) for Agricultural Production

Coordinating multiple autonomous robots for achieving an assigned collective task presents a complex engineering challenge. In this paper multi robot system control architecture (MRSCA) for the coordination of multiple agricultural robots is developed. The two important aspects of MRSCA; coordination strategy and inter-robot communication were discussed with typical agricultural tasks as examples. Classification of MRS into homogeneous and heterogeneous robots was done to identify appropriate form of cooperative behavior and inter-robot communication. The framework developed, proposes that inter-robot communication is not always required for a MRS. Three types of cooperative behaviors; No-cooperation, modest cooperation and absolute cooperation for a MRS were devised for accomplishing a variety of coordinated operations in agricultural productio

Cut Crop Edge Detection Using a Laser Sensor

An off-the-shelf low cost laser sensor was tested and evaluated both in laboratory and field conditions. The sensor identified the angular and straight edges of the laboratory test surface and replicated the straight edge profile with an error of 4%. In field conditions, the sensor identified three types of cut crop edges (wheat, alfalfa and corn) and replicated distinct shapes (triangle, curved and rectangular edges). The sensor was tested at two sensor path offset distances and three tractor/sensor speeds (3.2, 6.4 and 9.6 km/h). In all test runs the sensor detected the cut-crop edges. Standard deviations and RMSE values in determining the actual cut-crop edges for the entire field test were within 210 cm and 13 cm respectively. The sensor performed the best in the case of wheat cut-crop edge where the RMSE was 4.2 cm (sensor path offset = 1m, speed 3.2 km/h) and performed the worst in the case of alfalfa cut-crop edge where the RMSE was 16.7 cm (sensor path offset = .30 m and speed 9.6 km/h)

Guidance Directrix Generation Using Laser Sensors

A sensor array consisting of two laser sensors was utilized to determine the guidance directrix (offset distance-d, heading angle-ø) that are required as reference inputs for an automated guidance system. The sensor array was evaluated in both laboratory and field conditions. Under laboratory conditions the sensor array replicated the physical profile of the target surface with a 4% error in determining the heading angle. Field tests were conducted in two types of crops; corn and alfalfa. The sensor array identified the cut-crop edge profile ahead of the tractor and replicated distinct shapes of the cut-crop edge. RMSE values in determining the offset distances and heading angles of the cut-crop edge in corn were within 5.5 cm and 4.39°. In the case of alfalfa cut-crop edge the RMSE values were within 6.6 cm and 4.32°

Sensor Ranging Technique for Determining Corn Plant Population

Mapping of corn plant population can provide useful information for making field management decisions. This research focused on using low cost infra-red sensors to count plants. The voltage output data from the sensors were processed using an algorithm developed to extract plant populations. Preliminary investigations were conducted using sensors mounted on a stationary track for laboratory testing and on a row crop tractor for field testing. Repeated measurements were taken on a manually counted corn row. Visual inspection of the data from the field test indicated the potential to identify corn stalks based on approximate physical widths of the stalks. Corn plant populations tended to be overestimated for all eight field trials, with errors ranging from +0.7% to +4.4%. Overestimation was most likely due to leaves or other objects detected by the sensors during the field trials wrongly identified as corn stalks

Cut Crop Edge Detection Using a Laser Sensor

An off-the-shelf low cost laser sensor was tested and evaluated both in laboratory and field conditions. The sensor identified the angular and straight edges of the laboratory test surface and replicated the straight edge profile with an error of 4%. In field conditions, the sensor identified three types of cut crop edges (wheat, alfalfa and corn) and replicated distinct shapes (triangle, curved and rectangular edges). The sensor was tested at two sensor path offset distances and three tractor/sensor speeds (3.2, 6.4 and 9.6 km/h). In all test runs the sensor detected the cut-crop edges. Standard deviations and RMSE values in determining the actual cut-crop edges for the entire field test were within 210 cm and 13 cm respectively. The sensor performed the best in the case of wheat cut-crop edge where the RMSE was 4.2 cm (sensor path offset = 1m, speed 3.2 km/h) and performed the worst in the case of alfalfa cut-crop edge where the RMSE was 16.7 cm (sensor path offset = .30 m and speed 9.6 km/h)