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Image-Based Crop Leaf Disease Identification Using Convolution Neural Network

Publication Type : Book

Publisher : IntechOpen

Source : UK, 2022. (DOI: 10.5772/intechopen.106989)

Url :

Campus : Chennai

School : School of Computing

Department : Computer Science and Engineering

Year : 2022

Abstract : Nowadays, agriculture plays a major role in the progress of our nation’s economy. However, the advent of various crop-related infections has a negative impact on agriculture productivity. Crop leaf disease identification plays a critical role in addressing this issue and educating farmers on how to prevent the spread of diseases in crops. Researchers have already used methodologies such as decision trees, random forests, deep neural networks, and support vector machines. In this chapter, we proposed a hybrid method using a combination of convolutional neural networks and an autoencoder for detecting crop leaf diseases. With the help of convolutional encoder networks, this chapter presents a unique methodology for detecting crop leaf infections. Using PlantVillage dataset, the model is trained to recognize crop infections based on leaf images and achieves an accuracy of 99.82%. When compared with existing work, this chapter achieves better results with a suitable selection of hyper tuning parameters of convolution neural networks.

Cite this Research Publication : B.Indira and S.Veeramani, Image-Based Crop Leaf Disease Identification Using Convolution Neural Network to IntechOpen publisher, UK, 2022. (DOI: 10.5772/intechopen.106989).

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