Survey on Plant Leaf Disease Detection Approach Using Convolution Neural Networks
Nidhi Chaudhary, Preeti Kumari, Pooja Yadav, Deeksha Srivastava, Dr. H K Patel
As India's population grows and there is a greater need for food, plant diseases become a serious threat to agricultural output and have a serious impact on farmers. Plant diseases can be anonymously detected, helping to ensure food preservation and limit Monetary Impairment. Images of sick plants can be used to illustrate the illnesses. Publications conclude a graph on additional categorization strategies for plant leaf diseases Assortment. For farmers, spotting symptoms of illness with the naked eye is difficult. Crop defense in a big frame Using digital image processing technology that can identify sickness, the work is done. Diseases can significantly diminish crop origin, which poses a serious threat to food security. Accurately identifying plant diseases is so crucial and essential. Convolutional neural networks (CNN), which are frequently used to categorize plant diseases, are the foundation of this system. They represent traditional categorization techniques and have either fully or partially addressed the problems with contemporary technology in this area. In this study, we looked at the most current CNN networks that were pertinent to categorizing plant leaf diseases. Plant diseases are one of the main drivers for boosting food production and lowering production-related harm. Rapid diagnosis and detection of crop diseases are inevitable. Recently created deep learning algorithms have shown to be helpful in recognising plant diseases, giving a practical tool with incredible accuracy.
Article Details
Unique Paper ID: 157478

Publication Volume & Issue: Volume 9, Issue 7

Page(s): 527 - 532
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