Potato Leaf Disease Prediction
Saalim Shaikh, Hassan Khan, Daanyal Parbulkar, Khan Mohemmed Danish, Ashfaque Shaikh
The reason behind building this project is to detect or identify potato leaf diseases, having a variety of illnesses. Because our naked eyes can’t classify them, but Convolutional Neural Network can easily. You won’t believe it when I tell you that the error of some pre-trained Neural Network Architectures is approximately 3%, which is even less than the top 5% error of human vision. On large-scale images, the human top-5 error has been reported to be 5.1%, which is higher than pre-trained networks. Farmers who grow potatoes suffer from serious financial standpoint losses each year which cause several diseases that affect potato plants. The diseases Early Blight and Late Blight are the most frequent. Early blight is caused by fungus and late blight is caused by specific micro-organisms and if farmers detect this disease early and apply appropriate treatment then it can save a lot of waste and prevent economical loss. The treatments for early blight and late blight are a little different so it’s important that you accurately identify what kind of disease is there in that potato plant. Behind the scene, we are going to use Convolutional Neural Network – Deep Learning to diagnose plant diseases. Here, we’ll develop an end-to-end Deep Learning project in the field of agriculture. We will create a simple Image Classification Model that will categorize Potato Leaf Disease using a simple and classic Convolutional Neural Network Architecture. We’ll start with collecting the data, then model building, and finally, we deploy that model in cloud.
Article Details
Unique Paper ID: 159386

Publication Volume & Issue: Volume 9, Issue 11

Page(s): 1145 - 1148
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