Glen C. Rains

Several studies on robotic cotton harvesters have designed their end-effectors and harvesting algorithms based on the approach of harvesting a single cotton boll at a time. These robotic cotton harvesting systems often have slow harvesting times per boll due to limited computational speed and the extended time taken by actuators to approach and retract for picking individual cotton bolls. This study modified the design of the previous version of the end-effector with the aim of improving the picking ratio and picking time per boll. This study designed and fabricated a pullback reel to pull the cotton plants backward while the rover harvested and moved down the row. Additionally, a YOLOv4 cotton detection model and hierarchical agglomerative clustering algorithm were implemented to detect cotton bolls and cluster them. A harvesting algorithm was then developed to harvest the cotton bolls in clusters. The modified end-effector, pullback reel, vacuum conveying system, cotton detection model, clustering algorithm, and straight-line path planning algorithm were integrated into a small red rover, and both lab and field tests were conducted. In lab tests, the robot achieved a picking ratio of 57.1% with an average picking time of 2.5 s per boll. In field tests, picking ratio was 56.0%, and it took an average of 3.0 s per boll. Although there was no improvement in the lab setting over the previous design, the robot’s field performance was significantly better, with a 16% higher picking ratio and a 46% reduction in picking time per boll compared to the previous end-effector version tested in 2022.

Using artificial intelligence (AI) and the IoT (Internet of Things) is a primary focus of applied engineering research to improve agricultural efficiency. This review paper summarizes the engagement of artificial intelligence models and IoT techniques in detecting, classifying, and counting cotton insect pests and corresponding beneficial insects. The effectiveness and limitations of AI and IoT techniques in various cotton agricultural settings were comprehensively reviewed. This review indicates that insects can be detected with an accuracy of between 70 and 98% using camera/microphone sensors and enhanced deep learning algorithms. However, despite the numerous pests and beneficial insects, only a few species were targeted for detection and classification by AI and IoT systems. Not surprisingly, due to the challenges of identifying immature and predatory insects, few studies have designed systems to detect and characterize them. The location of the insects, sufficient data size, concentrated insects on the image, and similarity in species appearance are major obstacles when implementing AI. Similarly, IoT is constrained by a lack of effective field distance between sensors when targeting insects according to their estimated population size. Based on this study, the number of pest species monitored by AI and IoT technologies should be increased while improving the system’s detection accuracy.