Optimizing Maize Cultivation: A Vision-Based AI-Driven Methodology for Automated Seedling Thinning

To address the challenges of traditional manual maize seedling thinning, this article proposes an innovative approach that utilizes computer vision and deep learning for automated thinning. A zero-shot keypoint annotation algorithm, leveraging segment for anything model, is designed to label large datasets of maize seedling centers without requiring training samples. We also propose an improved hourglass network that significantly enhances seedling center positioning accuracy, enabling precise thinning decisions. Furthermore, a novel automatic thinning decision algorithm is devised to determine optimal removal strategies, ensuring ideal plant-to-plant spacing. The system's performance was evaluated against manually annotated data from 1020 images encompassing 2756 individual maize seedlings collected from farms. Impressively, the algorithm achieved a precision rate of 98.84%, confirming its ability to identify seedlings for removal while preserving healthy plants accurately. Evaluations of the keypoint detection network at a threshold of 0.2 yielded a percentage of correct keypoints of 97.66% and an object keypoint similarity of 0.87, surpassing existing methods and demonstrating the model's superior performance.