A deep learning-based method for detecting granular fertilizer deposition distribution patterns in centrifugal variable-rate spreader fertilization

Abstract

Beginning with the problems of high cost, low efficiency and high working intensity of traditional granular fertilizer deposition distribution detection methods, this study proposes an automatic detection system of granular fertilizer deposition distribution pattern based on deep learning method. The system analyzes the distribution pattern of granular fertilizer deposition on the ground and allows for parameter calibration, performance evaluation and structural optimization of fertilizer spreading devices. In this study, hardware and software parts of the proposed system were designed. The hardware part primarily includes a customized fertilizer calibration mat, an image acquisition device, a system control terminal, and a processing core to realize image acquisition, storage, data processing, and system control functions. For the software part, a granular fertilizer identification algorithm was designed; in addition, the granular fertilizer instance segmentation network model F-TPP (Fertilizer-Swin Transformer PANet partition) was proposed that integrated the Swin Transformer network and path aggregation network (PANet) to address the problems associated with extremely small and relatively dense targets in the detection of granular fertilizers. Moreover, the F-TPP network model optimized the Anchor generation scale for appropriate segmentation detection of granular fertilizers. The experimental results show that the F-TPP network model outperformed comparison conventional network models, such as Mask R-CNN, SOLO, and YOLOv5. Indeed, the detection efficiency of the proposed model was higher than that of conventional models for small and dense granular fertilizers. The proposed model yielded a detection precision, recall rate, and F1 score of 93.8%, 96.3%, and 0.95, respectively. The F-TPP network model improves the segmentation precision of granular fertilizer; thus, it improves the accuracy of automatically-deposited fertilizers. According to the experimental results, the proposed detection method yielded maximum absolute and relative errors of 0.59 g and 5.25%, respectively.