Plant leaf disease detection using deep learning algorithms

Abstract

In the agricultural landscape of Bangladesh, where farming plays a crucial role in the country's economy, the well-being of plants is vital for ensuring food security. The process of photosynthesis, occurring in the leaves, is essential for food production. However, the occurrence of leaf diseases presents a considerable danger to food production, necessitating the implementation of early detection measures. This study examines the transformative domain of deep learning, particularly investigating the effectiveness of Convolutional Neural Networks (CNNs) such as VGG19, DenseNet201, CNN, and InceptionV3 in enhancing the preciseness of plant disease detection. In addition to highlighting the inadequacy of these superficial learning models compared to traditional methods, our research aims to acknowledge the constraints of manual observation and standard testing by opposing the integration of state-of-the-art technologies in the agricultural sector. The findings of this research extend beyond agriculture, offering potential solutions to nutritional deficiencies and the possibility of increased crop yields. This paper not only exhibits the potential of deep learning in revolutionizing plant disease recognition but also provides a roadmap for future research. It emphasizes the crucial role that deep learning plays in shaping sustainable farming practices and strengthening food production systems against challenges, presenting a comprehensive plan for the intersection of technology and agriculture in the pursuit of a resilient and well-nourished future.