Intelligent Strawberry Ripeness and Disease Detection Classification System Based on Deep Learning

In modern agricultural production, timely detection of crop diseases and accurate assessment of ripeness are crucial for reducing yield loss and enhancing product quality. Strawberries, being a widely popular and economically valuable fruit, are particularly sensitive to diseases and strict in ripeness requirements, making quality control during their production critical. This paper develops a classification system for strawberry ripeness and pest disease detection based on the YOLOv5 deep learning model and automated robotic arm technology. The YOLOv5 model, known for its high efficiency, accurately identifies strawberries and assesses their ripeness and pest conditions, thereby enhancing the precision and speed of detection. The automated robotic arm technology utilizes the detection results from the YOLOv5 model to perform efficient and accurate automated sorting and grading of strawberries, significantly reducing labor costs and human error. By integrating deep learning and automation technologies, this system substantially increases strawberry yield and quality, alleviates labor burdens, and enhances agricultural production efficiency.