Leaf Disease Detection System Through Deep Learning Using CNN Model
Author(s):
P.Sridevi , K.SREELEKHA, D.JYOTHIKA, K.SRAVIKA, A.APOORVA
Keywords:
Deep Learning, Convolutional Neural Networks (CNN), ReLU, Region of Interest (ROI), feature extraction, Adam optimizer.
Abstract
Agriculture can be stated as a sector that significantly affects human existence and economic situation. If resources are not used effectively, crop yield might drop significantly. Leaf diseases are harmful to any type of crop since they might attack the leaves and thus, the plant at different stages. Its development and harvest are significantly impacted by this. To ensure a low loss, it is crucial to keep an eye on the crop's development. A subset of deep learning called convolutional neural networks is extensively used for picture segmentation and classification. The primary goal of the proposed model is to build a solution to identify 15 distinct classes of leaf diseases that extracts the region of interest (ROI) through minimum computational resource usage. Rectified Linear Unit (ReLU) is being used as the activation function to classify the input picture into the appropriate disease(s), and neural network models for automating feature extraction. The obtained accuracy is 94.53% through which it can be deduced that the above said technique is applicable under typical and challenging circumstances.
Article Details
Unique Paper ID: 158648

Publication Volume & Issue: Volume 9, Issue 10

Page(s): 295 - 300
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