Efficient Lightweight Detection and Classification Method for Field-Grown Horticultural Crops

As the core carrier of human food supply and agricultural economy, manual management in large-scale crop cultivation faces bottlenecks such as low efficiency, high cost, and difficulty in standardization. There is an urgent need for computer vision technology to realize automated detection and growth stage classification. However, most existing algorithms rely on high-performance GPUs for operation, resulting in high hardware costs, which makes it difficult to popularize them in low-end agricultural edge devices (e.g., embedded controllers, low-cost industrial computers). This study proposes a lightweight crop detection and classification model, Lite-CropNet. It builds a neural network architecture based on the CSPDarknet backbone network, designs a concise decoder, and adopts four-scale detection heads to adapt to crop targets of different sizes, balancing high accuracy and lightweight characteristics. Using tomatoes as the experimental object, tests on the TomatOD dataset (simulating real greenhouse environments) show that Lite-CropNet outperforms advanced methods, with a mean Average Precision (mAP)@0.5 of 85.7%. Under the conditions of the GTX 1650 GPU and 640×640 resolution, the Frame Per Second (FPS) reaches 76.9, and the model size is only 4.4M. This neural network model can efficiently complete tomato detection and maturity classification, and its architecture and design can also be transferred to crops such as potatoes and strawberries, providing a cost-effective and highly universal automated solution for agricultural production.