Development of Agriculture Monitoring System for Eggplant Crop Using Unmanned Aerial Vehicle

In the current scenario, different types of plant is main source to meet the food needs of the world. However, unfavourable environmental conditions affect the crop health and crop production. Therefore, state of the art methods are required for the early detection of crop diseases to raise the production. Under the current work, a convolutional neural concept based ResNet-50 classifier is developed for the early prediction of various diseases in eggplant crop. Image dataset of unhealthy/healthy crop leaves were collected by using Unmanned Aerial Vehicle (UAV). Four diseases of the eggplant crop are considered during the development of classifier i.e. cercospora melongenae, lace bug, leaf curl and pest. Further, health condition of the crop is examined by using first classifier. While, second classifier is developed to detect the diseases of eggplant crop. Finally, accuracy and precision of the developed classifiers have been estimated on the basis of several performance metrics.