Development Of Deep Learning Model for Wheat Disease Identification and Classification

Abstract

Plants play an essential role in climate change, agriculture industry and a country’s economy. There by taking care of plants is very crucial. Just like humans, plants are affected by several disease caused by bacteria, fungi and virus. Identification of these disease timely and curing them is essential to prevent whole plant from destruction. Identification of the plant leaf diseases is the key to preventing the losses in the yield and quantity of the agricultural product. Most of the countries depend upon agriculture. Due to the factors like diseases, pest attacks and sudden change in whether condition, the productivity of crop decreases. The studies of the plant diseases mean the studies of visually observable patterns seen on the plants. It takes long time and difficult to detect a disease in a plant manually. Hence, Deep Learning is used for detection of plant diseases. For this approach, Convolution neural networks will be used for classification based on learning with some training samples of Plant leaves like wheat. The algorithm and method that are used here is convolution neural network (CNN) by using EfficientnetB3 architecture using the Python programming. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path towards crop disease diagnosis on a massive global scale. Finally, the simulated result shows the disease of the plant and how much area it is affected.