Deep Convolution Neural Network more precisely VGG-16 with Agro Bot made up from Arudino, Raspberry Pi and Friends communication with Cloud Server powered by Crome V8 base Node.js
Back-end Server is powered by Node.js and Machine Learning Model is Served with the Courtesy of Django following Principle of REST
Convolutional Neural Network is chosen having 13 convolution layers and 3 fully connected layer. The convolution layers were followed by Max-Pool operation and Batch Normalization was done after Max-Pool.
Arduino UNO as a Microcontroller device L298N motor driver and Pi Camera Module which is used to capture still images of leaves for the disease detection.
The basic objective of this project is to design a system that takes an image of the infected plant leaf and predicts that image being diseased or not along with its control measures if diseased. The core objectives that are fundamentals to the project are:
To detect diseases in the farm as early as possible.
To inform farmers about the nature of the diseases and provide possible remedies.
Deep Neural Network Based on the VGG16 Artitecture which predicts the Disease based on image of leaf
Arudino Powered Car with integrated Raspberry Pi Camera to communicate with Web API via HTTP Protocol and Google Drive
RESTFul Web Applications to Communicate with the Server, Machine Learning Model and Agro Bot
Today, modern technology allows us to grow crops in quantities necessary for a steady food supply for billions of people. But diseases remain a major threat to this supply, and a large fraction of crops are lost each year to diseases. The situation is particularly dire for the 500 million smallholder farmers around the globe, whose livelihoods depend on their crops doing well. If the smartphone could be turned into a disease diagnostics tool, recognizing diseases from processed image would have been easily available to every farmer in any global position.
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