Image-Based Plant Disease Detection Using IoT and Deep Learning

Abstract

Plant diseases act as a major threat to the both economy and food security of any nation. Despite being of such importance, the identification of plant diseases and approaches deployed to tackle them are mostly conventional/ traditional ones. Incubation of technology and advancement in computer vision and deep learning models have opened new ways for developing much better approaches to tackle such issues. In this work, the native plants of Jammu and Kashmir are taken into consideration. An IoT-based framework is designed for data collection and disease diagnosis. The data involves both diseased and healthy leaf images. A hybrid deep neural network is trained to identify the plant species as well as the diseases associated with it. The trained model achieves an overall accuracy of 96.35%. A comparison with other state of art approaches is also presented, along with suggestions for some related future developments. This approach can be deployed on a global scale to tackle plant diseases and to achieve global diagnosis.

Keywords: Computer vision (CV), Internet of things (IoT), Machine learning (ML), Neural networks (NN), Precision agriculture (PA), Rarefied flow.

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