Overview / Usage

In the agriculture sector, one of the major problems in the plants is its diseases. The plant diseases can be caused by various factors such as viruses, bacteria, fungus etc. Most of the farmers are unaware of such diseases. That's why the detection of various diseases of plants is very essential to prevent the damages that it can make to the plants itself as well as to the farmers and the whole agriculture ecosystem. This research aims to classify and detect the plant's diseases automatically especially for the tomato plant. For hardware requirements, **Raspberry Pi4 **will be used to run inference on a Movidius Neural Compute Stick. Image processing is the key process of the project which includes image acquisition, adjusting image ROI, feature extraction and convolution neural network (CNN) based classification. Here, Python programming language, OpenCV library is used to manipulate raw input image.

Methodology / Approach

Training

Training images will be collected from authenticated sources such as research sites, and open online databases. The images collected will be appropriately split into training, validation and test sets. These images shall be uploaded to Intel AI DevCloud where the neural network will run several epochs and data fed into the network in batches to reduce memory footprint. Choice of the Intel AI DevCloud was due to its robust compute architecture which meets the deep learning training and inference compute needs for this project.
I shall then use the validation set to calibrate the network’s hyperparameters.

Hardware

A raspberry pi4 will be used to run Tensorflow and any other libraries in use. The Movidius Neural Compute Stick** **will be used for real-time fast detection by running on it the model used for inference. The OpenVINO toolkit will be crucial in extending CV workloads across Intel® hardware, maximizing performance.

Technologies Used

• OpenVINO toolkit
• Movidius Neural Compute Stick
• TensorFlow
• Intel AI DevCloud