International Journal of Electronic Devices and Networking
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P-ISSN: 2708-4477, E-ISSN: 2708-4485

International Journal of Electronic Devices and Networking


2021, Vol. 2, Issue 2, Part A
Plant disease detection using deep learning and image processing


Author(s): Blessy YM, Prabhu VS, Neelagandan SV, Prasanna M and Nithish S

Abstract: Plant disease, especially crop plants, may be a major threat to global food security since many diseases directly affect the standard of the fruits, grains, and so on, resulting in a decrease in agricultural productivity. Farmers need to observe and determine whether a leaf was infected by naked eyes. This process is unreliable, inconsistent, and error prone. Several works on deep learning techniques for detecting leaf diseases had been proposed. Identification of disease is extremely difficult in agriculture field. If identification is wrong then there's an enormous loss on the assembly of crop and economical value of market. Leaf disease detection requires huge amount of labor, knowledge within the plant diseases, and also require the more time interval. Leaf disease detection requires huge amount of labor, knowledge within the plant diseases, and also require the more time interval. So, we will use image processing for identification of plant disease in MATLAB. Identification of disease follows the steps like loading the image, contrast enhancement, converting RGB to HSI, extracting of features and deep learning technique. Most of them built their models supported limited resolution images using convolutional neural networks (CNNs). In this research, we aim at detecting early disease on plant leaves with small disease blobs, which may only be detected with higher resolution images, by a man-made neural network (ANN) approach. After a preprocessing step using a contrast enhancement method, all the infested blobs are segmented for the whole dataset. A list of several measurement-based features that represents the blobs are chosen and then selected based on their influence on the model’s performance using a wrapper-based feature selection algorithm, which is made supported a hybrid metaheuristic. The chosen features are used as inputs for an ANN. We compare the results obtained using our methods with another approach using popular CNN models (Alex Net, VGG16, ResNet-50) enhanced with transfer learning. The main objective of the system is used to detect the disease in the leaf by using Image Processing. Experimental results obtain the Better Performance, when compared to other system.

Pages: 13-16 | Views: 888 | Downloads: 451

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International Journal of Electronic Devices and Networking
How to cite this article:
Blessy YM, Prabhu VS, Neelagandan SV, Prasanna M, Nithish S. Plant disease detection using deep learning and image processing. Int J Electron Devices Networking 2021;2(2):13-16.
International Journal of Electronic Devices and Networking
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