Drone Based Smart Agriculture Monitoring System for Chickpea Crop

As per Indian economic survey, agriculture employs over 50% of the Indian work force and contributes around 17-18% of the GDP. Plant diseases affect the quality and quantity of agricultural products which affect the food safety. However, automation of plant disease identification is a possible solution to increase the crop yield. In the present paper, a Convolutional Neural Network (CNN) based model has been proposed in order to monitor the health of chickpea crop. Drone has been used to capture the images of chickpea crop. Further, leaf properties using image data is analyzed that would help to detect disease. Furthermore, image augmentation i.e. rotation of image, zooming, horizontal flip and brightness enhancement have been used to increase the accuracy of the proposed system. CNN based model uses 80% of dataset to train the classifier, while 20% dataset is used to test the model. Finally, performance parameters of the classifier for the crop health monitoring have been compared.