Enhancing Disease Detection in Fruits and Leaves Through a Hybrid Deep Learning Approach

Detecting diseases in fruits and leaves is crucial in all sectors of agriculture, including small farms, large-scale commercial operations, and urban agriculture. Diseases in fruits and leaves can reduce crop yield, affect product quality, and spread to other plants, leading to significant economic losses. Traditional methods of disease detection involve visual inspection by trained personnel, which can be time-consuming, expensive, and prone to errors. The hybrid deep learning approach, which combines CNNs and RNNs, has shown promise in accurately detecting diseases in fruits and leaves. This approach has the potential to improve disease detection in all sectors of agriculture by providing accurate and efficient disease identification. Automated disease detection can also enable early detection and intervention, reducing the spread of diseases and preventing crop losses. This can result in increased crop yield, improved product quality, and reduced use of pesticides, making it a sustainable solution for disease detection in fruits and leaves in all sectors of agriculture.