Mobile detection of crop diseases for agricultural yield management

In agricultural economies worldwide, plant diseases are a major cause of economic losses. In this paper we propose an automated method for real time crop monitoring and disease detection. Images were captured on a daily basis for a field of 8 acres of land. Image features are extracted using the Speeded-Up Robust Features (SURF) after the Maximally Stable External Regions (MSER) method find blobs. The features are used to classify the images using Kmeans Clustering in the training phase. The ground truth diseased crop images are stored in a database with the same features to act as prototypes and are compared to real time images for disease detection using nearest neighbor classification. The experimental dataset currently consists of rice crop and maize crop with 100 diseased images and approximately 1000 normal crop images. Results show 83.3% accuracy and provide information to farmers about their crop and if required alert them to disease, allowing for corrective action. There is scope to extend the classification and detection method to real-time platforms. Such applications would prove to be a valuable tool for agricultural yield management, especially since the field of interest covered may be very large and the diseases may not be uniformly distributed.