Real Time Implementation of Convolutional Neural Network to Detect Plant Diseases Using Internet of Things
In this paper, we have described the real time implementation of machine learning on identification of plant disease. This is done by using the concept of transfer learning with Convolutional Neural Networks (CNNs) on self designed drone. In order to achieve high crop yield, the time management and robust diagnosis of the plant disease is a crucial factor along with the inspection of their nutrient deficiencies. We have designed and trained a novel real time system using machine learning with deep convolutional networks to identify the plant diseases for the diagnosis. This is based on a concept of leaf image classification. The trained system is processed on an open source development board and it is mounted on self design automated drone and uses Internet of Things (IoT) to transfer the captured images to a local database which is connected to a global Server.
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