Diagnóstico de enfermedades en plantas en tiempo real basado en IA utilizando redes neuronales convolucionales...

Real-time plant disease diagnosis employs new technologies to identify and detect plant diseases while they occur, thus allowing a rapid response that reduces crop loss and improves healthier agricultural practices. This work improves plant health monitoring using early detection to maximize yield and reduce loss. Typical procedures for diagnosing plant diseases involve sampling or visual inspection and are slow, labor-intensive, and subject to human error. These procedures are not suitable for widespread adoption in the field of crop systems where the scale of diagnostics requires real-time, scalable, and accurate reporting of problems. The Plant Disease Diagnosis using Deep Learning (PDD-DL) framework, through Convolutional Neural Networks (CNNs), analyzes plant images to automatically diagnose plant diseases in real time. This model is faster, more trustworthy, and more scalable in diagnosis than traditional methods of diagnosis. The research presents the validation of the model based on common, popular crops; however, the application includes a wide array of crops. The system may be retrained for specific disease classes depending on agricultural requirements. CNNs will certainly provide effective image analysis, accurately differentiating healthy from sick plants, and permitting continuous monitoring for preemptive measures in the classification of plant diseases. The proposed model performed with an overall accuracy of 98.32%, precision score of 97.85%, recall value of 98.14%, F1-score of 97.99%, and real-time inference speed of 42.6 ms per image. As a result, the study's findings improve accuracy and speed in diagnosing plant disease, which aids in precision agriculture and sustainable plant health management.