Plant Disease Classification using Convolution Neural Network

Abstract: Today, agriculture is back bone of our country's economy. Agriculture is sometimes referred to as the art and science of raising crops and feeding domestic animals. Moreover, half of the country's GDP is contributed by the agricultural sector. The pace of output is influenced by the crops, fertilisers, and cultivation techniques. Unknown plant or crop diseases now have a significant impact on agricultural productivity. It may be difficult for a farmer to spot a plant disease, but it may also be difficult to do so without a microscope or even with our unaided eyes. To address this complex problem, we thus provide a methodology that uses machine learning and deep learning to identify the plant sickness. Convolution neural networks can be used in conjunction with deep learning and machine learning to identify plant diseases. Deep learning allows us to characterise the behaviour and symptoms of the plant in addition to detecting sickness. By employing various architectures, deep learning aids in the visualisation of the picture. There are several types of architecture, including AlexNet, VGG, ResNet, and CNN, among others. We have developed a model to identify plant diseases using the proper architecture. Finally, this work analyses and makes predictions on how image processing-based plant disease and pest detection may progress in the future