Neem Leaf Disease Detection based on Enhanced Leaky Capacitor-Fired Neuron (LCFN) Model

In herbal products manufacturing field, Neem plays a vital role in India. Abiotic and biotic agents affects the leaves and growth of Neem tree. Viruses, fungus and bacteria are the well known biotic agents and humidity, temperature and water are the well know abiotic agents. High quality herbal product production and plant growth are reduced due to biotic active ingredients. Bacteria affects the neem leaves. Neem leaf disease symptoms can be easily identified by farmers. Laboratory tests like polymerase chain reaction (PCR) is also used for the identification and it is a costly one. In this work, Leaky Capacitor-Fired Neuron (LCFN) model is proposed to identify the neem diseases. One dimensional time sequences are extracted by LCFN from neem leaves. By combining pixels of neighborhood images, a continuous image sequence is created by LCFN. Optimized features from neem leaves are extracted using LCFN. An ensemble classifier is used to classify the leaves based on extracted features. Better performance are exhibited by LCFN as shown in experimental results.