An lstm based cnn model for pomegranate fruit classification with weight optimization using dragonfly technique

Abstract

Pomegranate is a widely grown plant in India. This highly beneficial fruit is infected by multiple pests and diseases which cause great economical losses. Different forms of pathogen diseases on leaf, stem and the fruits are present. Some of the diseases that affect pomegranate fruits are anthracnose, cercospora, heart rot and bacterial blight. There is a need for disease control strategies to incorporate timely action on the developed diseases. Thus, there is a need for intelligent and self-learning recognition systems to detect these diseases on time. This study is aiming to classify pomegranate fruits into two classes normal and abnormal using CNN LSTM technique. This research work uses a hybrid CNN-LSTM technique to detect four types of diseases present in the pomegranate fruits and classify them into four classes. The results obtained using CNN LSTM are then optimized using dragonfly algorithm. The features like colour, texture and shape of the fruits are collected and fed into the hybrid CNN-LSTM. The dataset for the classifier is given as an excel file which is initially pre-processed using map reduce technique and dimensionality reduction carried using Principal Component Analysis and Discriminant analysis. The CNN LSTM classifier identifies the 4 types of diseases and normal fruit. The classification is further optimized using dragonfly algorithm. The optimized weight and cost function has further explored to support the multi-class disease detection process. Experimental results have shown an accuracy of 92% in classification using CNN-LSTM technique and optimization using dragonfly techniques shows an improved classification accuracy of 97.1%.