Novel KNN with Differentiable Augmentation for Feature-Based Detection of Cassava Leaf Disease and Mitigation of Overfitting: An Innovative Memetic Algorithm

Many tropical countries depend on cassava, which is susceptible to deadly illnesses. These abnormalities can be diagnosed accurately and quickly to ensure food security. This study compares healthy and sick cassava leaves for four diseases: bacterial blight, brown streak, green mottle, and mosaic. Leaf images were systematically feature extracted to reveal color patterns, morphology, and textural qualities. Model learning methods use this extracted feature dataset. A new KNN+DA method may improve disease identification. Differentiable Augmentation uses data unpredictability to create alternative training samples to increase KNN performance. KNN+DA was compared to SVM, KNN, LR, and a memetic-tuned KNN to comprehend it better. We reached calculation speed, accuracy, recall, precision, and F1-score. KNN+DA outperformed older approaches in accuracy and resilience. KNN with differentiable augmentation improved classification accuracy and reduced overfitting, improving model generalizability for real-world use. Memetic algorithm-tuned KNN is another potential hybrid technique for disease diagnosis. Integrating current machine learning algorithms with cassava leaf photos can provide reliable early disease detection. More environmentally friendly agriculture would result