Identification and Classification of Diseases in Climbers using ML Algorithms

Climber plants like grapes grow while spreading across the field and it is a time-consuming process to check the whole field for identification of disease and its infection among the grown plants. Artificial Intelligence is a resourceful tool for supporting these difficult tasks. Dataset collection plays a significant role for the efficient working of Machine Learning Algorithms. The usage of drones with camera to collect the datasets from the real agricultural fields and using the captured dataset for training the different ML algorithms have enabled the identification and classification of diseases in Grapes on a real time basis. As a part of this work, three different Machine Learning Algorithms are compared in terms of accuracy and the results portray that ResNet-50 architecture & algorithm provides the best and optimum accuracy for a specific chosen system configuration.