• Ashmita R. Ghongade
• Dr. Gurudev Sawarkar
• Prof. Rupali Dasarwar

Plant diseases can have significant impacts on agricultural production, leading to significant losses in yield and quality. Early detection and diagnosis of crop diseases is essential for effective control and management. In this study, this project presents a novel approach for the automated detection of plant diseases using deep learning-based Convolutional Neural Networks (CNNs) coupled with Transfer Learning algorithms. Our study focuses on developing a robust and adaptable system capable of accurately identifying plant diseases from image data. By leveraging a comprehensive dataset comprising diverse plant species and disease types, our goal is to train the model to achieve high accuracy and real-time detection capabilities. The potential significance of this research lies in its potential to significantly improve agricultural practices by offering farmers a valuable tool for prompt disease diagnosis and management, ultimately leading to increased crop yields and sustainable farming practices.

CNN, Deep Learning, Plant Diseases detection, Disease Diagnosis, Agriculture.

"Plant Disease Detection Using Deep Learning-Based Convolutional Neural Networks with Transfer Learning Algorithms.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.d762-d766, May-2024, Available :http://www.jetir.org/papers/JETIR2405387.pdf

"Plant Disease Detection Using Deep Learning-Based Convolutional Neural Networks with Transfer Learning Algorithms.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppd762-d766, May-2024, Available at : http://www.jetir.org/papers/JETIR2405387.pdf