Potato Leaf Disease Detection Using Convolution Neural Network Model


  • S. K. Bharadwaj, V. K. Jadon, Amit Sharma, Jitendra Kumar, D. K. Mishra, Ashish Shukla


Leaf Disease, CNN, Potato, Accuracy, Agriculture, Deep Learning.


Potatoes, being one of the most widely consumed vegetables globally, have increasingly become a focus for agricultural departments worldwide. However, despite their popularity, potato leaf diseases pose a significant threat to potato crops. A range of diseases, including early blight, late blight, and Septoria blight, can affect potato plants, manifesting symptoms in their leaves. Detecting and addressing these outbreaks early on is crucial to prevent major economic losses for farmers.

In this research paper, we propose a model that utilizes image processing techniques to identify and detect diseases in potato leaves. Our approach relies on a Convolution Neural Network (CNN), chosen for its effectiveness in image classification tasks. By leveraging CNN technology, we aim to provide accurate and efficient detection of potato leaf diseases, thereby enabling timely intervention and mitigation measures


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How to Cite

S. K. Bharadwaj. (2024). Potato Leaf Disease Detection Using Convolution Neural Network Model. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3259–3267. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6015



Research Article