Early Detection of Plant Leaf Disease Using Deep Learning

Early Detection of Plant Leaf Disease Using Deep Learning

Shivam Ardalkar 1, Aishwarya Waghmode 2, Aniket Jadhav 3, Parimal Bhosale 4,

 Prof. Afsha Akkalkot 5

12345Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra, India

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Abstract - Farmers face the challenge of preventing and controlling the spread of crop diseases to improve productivity and yield. The impact of plant diseases extends beyond the plants themselves, affecting market access and agricultural production. To address this issue, researchers have recognized the potential of using leaf images to detect and classify diseases at an early stage. In this study, a dataset comprising diverse leaf diseases, including Yellow Curved, Late Blight, Leaf Spot, and Bacterial Leaf Spot, was collected from the internet. These images were utilized to train a Convolutional Neural Network (CNN), which is considered one of the most effective models for image classification. The trained CNN weights were then applied to test new leaf images. By employing CNNs and deep learning techniques, a system was developed to accurately classify plant leaf diseases. To ensure the best model was selected, performance analysis was conducted, focusing on accuracy and finding effective solutions to the problem at hand. By adopting a hybrid approach, the proposed model demonstrates its capability to accurately identify and classify plant leaf diseases.

Key Words:  Plant Leaf Disease, Deep Learning, CNN Algorithm, Image processing, Neural Network, Agriculture