Detecting diseases in plant leaves: an optimised deep-learning convolutional neural network approach Online publication date: Tue, 27-Apr-2021
by Saraansh Baranwal; Anuja Arora; Siddhant Khandelwal
International Journal of Environment and Sustainable Development (IJESD), Vol. 20, No. 2, 2021
Abstract: A country's economy relies heavily on its agricultural productivity. Most of the crops consumed daily by the population are prone to diseases. Identifying and preventing the disease at an early stage is a challenge even for the expert's eye. Therefore, an appropriate system is needed to detect plant disease in its initial stages. This paper employs the approach of convolutional neural networks to automatically detect and address the issue. Images for various plants covering 35 different classes of plant diseases and a total of 29,180 RGB images of diseased and healthy plant leaves has been used for the purpose. Image filtering, compression, and data generation techniques have been used to further increase the training dataset and achieve high accuracy across all classes. Net accuracy of the model reaches a 98.34% success rate on the entire dataset. The data has been sampled and generated from severely downsized images, with a significant focus on speed as well as accuracy.
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