Machine learning approach for automatic diagnosis of Chlorosis in Vigna mungo leaves

Viral infection in crops is something that may lead to a huge loss in crop yield as there are no known recovery procedures. Also, at the onset of yellowing in a leaf, no observable changes occur in leaf structure and geometry. Therefore, the manual inspection and diagnosis of such diseases by the framers in agricultural fields are difficult on a large scale. The automatic artificial intelligence-based tool can be used for early-stage diagnosis of viral growth, where the symptoms may be available in certain parts like leaves. An automatic computer vision-based method is proposed for the identification of yellow disease, also called Chlorosis, in a prominent leguminous crop like Vigna mungo. The proposed method involves fully automatic partitioning of plant leaves, followed by feature extraction in the spatial domain and disease prediction using a support vector machine (SVM) learned upon several training samples. The method is entirely automatic and non-destructive which can predict the classification of plant health category with an accuracy rate of 95.69% with low computation complexity. This accuracy and computational complexity can be used in real-time situations for a large scale of Vigna mungo plantation using drones and remote camera.

Automated summary

Viral infection in crops can lead to significant losses without known recovery methods. Early diagnosis of viral growth is challenging for farmers, but can be identified using automatic tools based on artificial intelligence.