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Paper Details

A Convolutional Neural Network Based Potato Leaf Diseases Detection Using Sequential Model
Choyon Chandra Bonik, Abdus Sattar, Flora Akter, Md. Harunur Rashid,

One of Bangladesh’s primary agricultural products is the potato. In recent decades, Bangladesh has seen a surge in the popularity of potato farms. Nonetheless, farmer’s expenses in potato production are rising as a result of a number of illnesses. Nonetheless, the high cost of potato production is mostly attributable to a number of illnesses that are affecting the crop. Which is wreaking havoc on the farmer’s schedule. In order to modernize the potato industry and speed up disease diagnosis, automation has been implemented. In spite of the claims to the contrary, potato leaf disease is a serious problem that can severely reduce crop yields. The leaves of diseased potato plants will show symptoms of early blight, Septoria blight, late blight, and other diseases. If such outbreaks are discovered at the initial level and enough intervention is done, the farmer will not be at risk of incurring significant economic losses. Based on the results of this study, a new model is presented for accurately identifying and detecting illnesses in potato leaf stands using image processing. While there are several methods that may be utilized in machine learning, the Convolutional Neural Network (CNN) model is what’s being employed here to identify the disease in potato leaf photos. This work implements a CNN based sequential model to predict the disease of potato leaves. This research achieved 94.2% model accuracy on this model. The presented model was tested on both typical and disordered potato leaves in an effort to distinguish between the two. Next, the algorithm is applied to the images, and the potato tree’s leaf is classified as either healthy or unhealthy.

"Potato leaf disease , Machine learning , Leaf disease detection , Image processing"
Journal or Conference Name
2023 International Conference for Advancement in Technology, ICONAT 2023
Publication Year