Abstract
The real-time detection of banana bunches and stalks in banana orchards is a key technology in the application of agricultural robots. The complex conditions of the orchard make accurate detection a difficult task, and the light weight of the deep learning network is an application trend. This study proposes and compares two improved YOLOv4 neural network detection models in a banana orchard. One is the YOLO-Banana detection model, which analyzes banana characteristics and network structure to prune the less important network layers; the other is the YOLO-Banana-l4 detection model, which, by adding a YOLO head layer to the pruned network structure, explores the impact of a four-scale prediction structure on the pruning network. The results show that YOLO-Banana and YOLO-Banana-l4 could reduce the network weight and shorten the detection time compared with YOLOv4. Furthermore, YOLO-Banana detection model has the best performance, with good detection accuracy for banana bunches and stalks in the natural environment. The average precision (AP) values of the YOLO-Banana detection model on banana bunches and stalks are 98.4% and 85.98%, and the mean average precision (mAP) of the detection model is 92.19%. The model weight is reduced from 244 to 137 MB, and the detection time is shortened from 44.96 to 35.33 ms. In short, the network is lightweight and has good real-time performance and application prospects in intelligent management and automatic harvesting in the banana orchard.
Highlights
Introduction published maps and institutional affilThere are more than 130 countries in the world that cultivate bananas
Some mechanized transportation equipment has been gradually put into use in the banana orchards, but it still lags behind other fruits and vegetables in banana orchards in the research of intelligent management and automatic picking
Comparing the detection results of banana bunches and stalks in sunny and cloudy conditions, respectively, the correct detection rate is very close, indicating that the improved models are robust to changes in illumination
Summary
Introduction published maps and institutional affilThere are more than 130 countries in the world that cultivate bananas. The harvesting of bananas in the orchard essentially relies on human labor [1]. Some mechanized transportation equipment has been gradually put into use in the banana orchards, but it still lags behind other fruits and vegetables in banana orchards in the research of intelligent management and automatic picking. In the complex environment of banana orchards, fast and accurate detection of banana bunches and stalks based on vision is the key task for the intelligent management of banana orchards. It provides solutions for saving labor and time costs, meeting high-quality fruit requirements, and reducing statistical errors. Solving the problems caused by occlusion, uneven illumination, and other unpredictable iations
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