Detection of diseases in tomato (Solanum lycopersicum) leaves using artificial intelligence
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Abstract
Context: The increasing losses in tomato crops due to foliar diseases has created an urgent need for rapid and accurate methods for early detection.
Objectives: To develop an automated system for identifying diseases in tomato leaves using artificial intelligence.
Methods: This study employed an experimental and quantitative methodological design with an applied, descriptive-predictive approach. A dataset of 11000 labeled images was generated using the GroundingDINO model, divided into 10000 images for training and 1000 for validation, covering nine common diseases and healthy leaves. The YOLOv11n model was trained due to its computational efficiency and high accuracy in object detection tasks.
Results: Exceptional performance was achieved, with an overall precision of 0.9967, a recall of 0.99604, and an mAP50 of 0.99446, outperforming conventional approaches in both accuracy and robustness.
Conclusions: This method is highly effective for the automated detection of diseases in tomato leaves, enabling more precise and timely field interventions. This technology can significantly contribute to sustainable agriculture by reducing unnecessary pesticide use. Future research should validate the model under real-field conditions and extend its application to other crop species.
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