Diagnosis of Neem Leaf Diseases Using Fuzzy-HOBINM and ANFIS Algorithms

Abstract

This paper proposes an approach to detecting diseases in neem leaf that uses a Fuzzy-Higher Order Biologically Inspired Neuron Model (F-HOBINM) and adaptive neuro classifier (ANFIS). India exports USD 0.28-million worth of neem leaf to the UK, USA, UAE, and Europe in the form of dried leaves and powder, both of which help reduce diabetes-related issues, cardiovascular problems, and eye disorders. Diagnosing neem leaf disease is difficult through visual interpretation, owing to similarity in their color and texture patterns. The most common diseases include bacterial blight, Colletotrichum and Alternaria leaf spot, blight, damping-off, powdery mildew, Pseudocercospora leaf spot, leaf web blight, and seedling wilt. However, traditional color and texture algorithms fail to identify leaf diseases due to irregular lumps and surfaces, and rough ridges, as the classification time involved takes as long as a week. The proposed F-HOBINM algorithm recognizes the leaf intensity through the leaky capacitor, and uses subjective intensity and physical stimulus to interpret the diagnosis. Further, the processed leaf images from the HOBINM algorithm are applied to the ANFIS classifier to identify neem leaf diseases. The experimental results show 92.18% accuracy from a database of 1,462 neem leaves.