AGROVISOR: Real-Time Leaf Disease Detection for Rice and Potato Crops

In the pursuit of sustainable agriculture and food security, this paper introduces an innovative paradigm for the early detection and differentiation of leaf diseases in rice and potato crops. Our approach, named “AGROVISOR” employs a sophisticated fusion of image processing and machine learning techniques to meticulously scrutinize plant foliage for signs of pathogenic intrusion. It unveils an integrated approach, harmonizing convolutional neural networks (CNNs) and advanced image processing techniques, culminating in the development of a robust framework. Our proposed model, intricately crafted with CNN architecture, exhibits unparalleled proficiency in deciphering intricate patterns inherent in plant foliage imagery. Leveraging the power of deep learning, AGROVISOR insight is fine-tuned with an extensive dataset, ensuring exceptional accuracy in disease classification.