Fast recognition of ripe tomato fruits in complex environment based on improved YOLOv3

Aiming at the problem of fast recognition of ripe tomato fruits, an image dataset of ripe tomato fruit is collected and labeled for deep neural network model training. In addition, the target detection algorithm of the classic algorithm YOLOv3 is improved in light weight based on practical applications, so that it can be easily deployed on the embedded controller of tomato-picking robot. The activation function, clustering of anchor frame, non-maximum suppression and loss function are also optimized, the efficiency and the stability of the algorithm is also improved. Through verification of the test set, the proposed improved YOLOv3 target detection algorithm can effectively recognize tomato ripe fruit in complex environment, including different density, different lighting conditions and even different occlusion degree. The final detection accuracy was 92.11%, recall rate was 86.21%, F₁ score was 89%, mAP was 84.58%. Thus the experimental results demonstrate the feasibility, accuracy and robustness of the proposed method.