Real-Time Monitoring Method of Strawberry Fruit Growth State Based on YOLO Improved Model

Abstract

A key challenge in automated orchard management robots is the fast and accurate identification of crop growth conditions and maturity for subsequent operations such as automatic pollination, fertilization and picking. In particular, strawberry fruits have a short ripening period and the fruits are heavily overlapped and shaded by each other, which is time-consuming and ineffective based on traditional detection methods. Therefore, we designed and developed a strawberry growth detection algorithm, SDNet (Strawberry Detect Net). The algorithm is based on the YOLOX model and replaces the original CSP block in the backbone network with a self-designed feature extraction module C3HB block to improve the spatial interaction capability and monitoring accuracy of the detection algorithm; Then, the normalized attention module (NAM) is embedded in the neck to improve the detection accuracy and attention weight of small target fruits; and we use the latest SIOU objective loss function to improve the prediction accuracy of the detection model, which finally achieves the monitoring of strawberry fruits under five growth states. The experimental results show that the precision, accuracy, and recall of SDNet are 94.26%, 93.15%, and 90.72%, respectively, and the monitoring speed is 30.5 ms. It is 4.08%, 3.64 and 2.04% higher than the precision, accuracy, and recall of YOLOX, respectively, and there is no significant change in the model size. The research results can effectively solve the problem of low accuracy of strawberry fruit growth state monitoring under complex environments, and provide important technical reference for realizing unmanned farm and precision agriculture.

Keywords

• Fruit detection
• digital agriculture
• deep learning
• real-time counting