Rice Autonomous Harvesting: Operation Framework

Abstract

This paper reports on an operation framework for autonomous rice harvesting. We developed an integrated algorithm for robotic operation and cooperation with farmworkers to automate each subsection of the harvesting and unloading process and of the processes that bridge them (homing and restarting). The algorithm was installed into a head‐feeding combine robot. The robot followed a target path based on its absolute position and orientation, planning a counterclockwise spiral path in a rectangular paddy field, and returned to a position close to a farm road when its grain tank was filled to a specified level. The grain unloading operation was automated using a machine vision system. As the restarting process (return to harvesting) was also automated, the combine robot was able to harvest a rectangular field autonomously by cyclically repeating the harvesting, homing, unloading, and restarting operations. Under field conditions, the robot was able to follow the target path within tolerable lateral and azimuth errors while harvesting rice successfully, and to unload the harvested grain into a wagon without spillage. The root mean square error of the lateral and azimuth errors during harvesting were 0.04 m and 2.6°, respectively. In the homing operation, the robot returned to a given line within ± 0.1 m and aligned its heading to the direction of the line within ± 4°. The robot recognized the arbitrarily parked wagon and positioned its auger spout at the target point with a respective horizontal and vertical accuracy of ± 0.2 m and ± 0.3 m. Harvesting time accounted for 50%–60% of the entire robotic operation. Homing scheduling and dispatch control for the wagon were found to be of importance for developing a more efficient robotic operation.

Development of Path Generation and Algorithm for Autonomous Combine Harvester Using Dual GPS Antenna

“…Since the distance between rows of rice is 30 cm, even if an error of about 5 cm occurs during driving, it is judged that it is within the allowable error because there is no non-working area. The results tested in the previous study showed that the average working path error was 0.03–0.04 m at a maximum speed of 3.6 km/h [ 17 ] and 0.07 m at a maximum speed of 2.3 km/h [ 19 ]. In this paper, the average work path error was 0.04 m, so there was no significant difference, but the work travel speed was 6 km/h and had a stable performance at a higher speed.…”

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

“…The sugarcane harvester machine combined with machine vision algorithm detects the damaged billets, consequently increasing the quality of the production [48]. The autonomous rice harvester with a combined robot performed harvesting, unloading and restarting with adequate accuracy [49]. The autonomous harvesting grippers with machine vision locate target like peduncles for various crops and remove the leaves and stems as obstacles to improve the harvesting system [50].…”

A field‐tested robotic harvesting system for iceberg lettuce

“…Many research projects have been performed, but little has filtered through into the commercial world. The more successful projects include a harvester for apples (Silwal et al, ) using a suction method, rice harvesting using custom harvesting systems (Kurita, Iida, Cho, & Suguri, ), and a sweet pepper harvesting system (Bac et al, ). There has also been significant work in the development of autonomous weeding or grading systems including a sugar beet classifying system (Lottes, Hörferlin, Sander, & Stachniss, ) and a grape pruning system (Botterill et al, ).…”