High-Precision Tomato Maturity Detection Using a Genetic Algorithm Optimized Swin-YOLO Network

Abstract

In the realm of precision agriculture, the determination of tomato maturity stages plays a crucial role in optimizing harvest timings, ensuring produce quality, and maximizing yields. Traditional approaches, largely reliant on manual inspection, are not only labor-intensive but also subject to human error, making them unsuitable for modern, large-scale agricultural operations. Addressing these challenges, this study pioneers the use of a Genetic Algorithm (GA) optimized Swin-YOLO (You Only Look Once) network, aiming to automate and enhance the precision of tomato maturity detection. By integrating the advanced capabilities of the Swin Transformer for feature extraction with the efficiency of the YOLO object detection framework, the proposed Swin-YOLO model is meticulously designed to identify varying stages of tomato maturity from complex agricultural imagery. The introduction of a GA facilitates the systematic optimization of the network's architecture and hyperparameters, focusing on improving accuracy and computational efficiency across diverse environmental conditions. Our research involved compiling a comprehensive dataset of annotated tomato images, reflecting a wide array of maturity stages under different lighting and occlusional challenges. The findings from our study indicate a significant leap in performance, with the GA-optimized Swin-YOLO network outshining existing deep learning models and conventional manual methods in terms of both detection accuracy and processing speed. Notably, the model exhibits exceptional adeptness at handling images affected by variable lighting and partial occlusions, marking a substantial advancement in automated tomato maturity detection. The implications of this research are profound, offering a scalable, efficient, and highly accurate solution to one of agriculture's longstanding challenges. This breakthrough not only stands to reduce labor costs and enhance operational efficiency but also contributes to the sustainability of tomato farming practices by facilitating more informed and timely harvesting decisions. Furthermore, the successful application of GA for network optimization underscores the potential of combining deep learning with evolutionary algorithms to tackle complex agricultural challenges, setting the stage for future innovations in precision agriculture and beyond.