Detection of Crop Disease Using Convolutional Neural Network
Farming lands are increasingly plentiful in developing countries, making it difficult for farmers to keep track of each and every plant in their fields on a regular basis. It is also impossible for farmers to be aware of all illnesses; therefore non-native diseases are frequently overlooked. Expert consultation for this is often time consuming and pricey. As a result, an automated method to identify and classify plant diseases the utilization of image processing is required. Deep learning and convolution neural networks are employed in this study to identify sickness in agricultural produce. Because convolution neural networks are specifically intended to analyze pixel data, they deliver higher accuracy and results when classifying photos into healthy and non-healthy categories. This technique includes two phases: the first requires training the modeling for healthy and diseased photos of crops; the second phase comprises crop monitoring and identification of specific disease in the plant, which leads to early disease diagnosis.
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