A Convolution Neural Network based approach to detect the disease in Corn Crop

The agricultural production is affected by the climate changes i.e. humidity, rain, extremes of temperature etc. Additionally, abiotic stresses are causative element to the etiology of disease as well as pest on crops. The production of the crops can be improved by diagnosis as well as detecting the accurate disease on time or in early stage. Moreover, it is very difficult for accurately detecting and treatment based on the technique which used in disease and insect pests diagnosis. Few researchers have made efforts on predicting disease as well as pest crops using machine learning algorithms. Therefore, this paper presents disease identification in corn crops by analyzing the leaves in the very early stage. We have used PlantVillage dataset for experiments and analysis. The validity of the results has been cheeked on various performance metrics such as precision, accuracy, recall, storage space, running time of the model and AUC-RoC. The obtained results shows the proposed technique outperform in comparison with the traditional machine learning algorithms. Developed model is able to achieve the accuracy of 94%.