Published Paper Details:

Agriculture holds immense importance in India due to its burgeoning population and escalating food demands, necessitating increased crop yields. However, low crop yields are often attributed to diseases caused by bacteria, fungi, and viruses. Detecting and managing these diseases is crucial, and one effective approach is utilizing plant disease detection methods. Machine learning techniques are particularly promising for disease identification in plants, leveraging data-driven insights for accurate detection. Moreover, deep learning has emerged as a powerful tool in computer vision, offering superior performance in disease detection. This comprehensive review explores various AI-based machine learning and deep learning techniques for plant dis-ease detection. Deep learning, in particular, has shown remarkable success in enhancing performance outcomes across diverse domains. By comparing machine learning and deep learning techniques, researchers have demonstrated the effectiveness of deep learning models in detecting plant diseases from images. Implementing deep learning techniques holds significant potential in mitigating major crop losses by promptly identifying leaf diseases from captured images.