An AI-Based Horticultural Plant Fruit Visual Detection Algorithm for Apple Fruits

• Published in: Horticulturae Vol. 11; no. 5; p. 541
• Main Authors: Yan, Bin, Li, Xiameng, Yan, Rongshan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2025
• Subjects: Accuracy; Agricultural production; Agricultural technology; Algorithms; Apple; apple fruits; Apples; Artificial intelligence; Automation; Comparative analysis; Convolution; Datasets; Deep learning; Design; Efficiency; Feature extraction; Fruit trees; Fruits; horticulture; Identification and classification; Machine learning; Machine vision; Modules; Network management systems; Neural networks; Object recognition; Parameters; Physical characteristics; Physical properties; plants; Precision agriculture; Real time; Robots; Sustainable development; Target recognition; Trees; China; Shaanxi China; United States > US
• ISSN: 2311-7524; 2311-7524
• DOI: 10.3390/horticulturae11050541

In order to improve the perception accuracy of the apple tree fruit recognition model and to reduce the model size, a lightweight apple target recognition method based on an improved YOLOv5s artificial intelligence algorithm was proposed, and relevant experiments were designed. The Depthwise Separable Convolution (DWConv) module has many advantages: (1) It has high computational efficiency, reducing the number of parameters and calculations in the model; (2) It makes the model lightweight and easy to deploy in hardware; (3) DWConv can be combined with other modules to enhance the multi-scale feature extraction capability of the detection network and improve the ability to capture multi-scale information; (4) It balances the detection accuracy and speed of the model; (5) DWConv can flexibly adapt to different network structures. Because of its efficient computing modes, lightweight design, and flexible structural adaptation, the DWConv module has significant advantages in multi-scale feature extraction, real-time performance improvement, and small-object detection. Therefore, this method improves the original YOLOv5s network architecture by replacing the embedded Depthwise Separable Convolution in its Backbone network, which reduces the size and parameter count of the model while ensuring detection accuracy. The experimental results show that for the test-set images, the proposed improved model has an average recognition accuracy of 92.3% for apple targets, a recognition time of 0.033 s for a single image, and a model volume of 11.1 MB. Compared with the original YOLOv5s model, the average recognition accuracy was increased by 0.8%, the recognition speed was increased by 23.3%, and the model volume was compressed by 20.7%, effectively achieving lightweight improvement of the apple detection model and improving the accuracy and speed of detection. The detection algorithm proposed in the study can be extended to the intelligent measurement of apple biological and physical characteristics, including for size measurement, shape analysis, and color analysis. The proposed method can improve the intelligence level of orchard management and horticultural technology, reduce labor costs, assist precision agriculture technology, and promote the transformation of the horticultural industry toward sustainable development.