An Effective Pomegranate Fruit Classification Based On CNN-LSTM Deep Learning Models

M T Vasumathi,M Kamarasan

doi:10.17485/ijst/v14i16.432

Abstract

Objectives: To employ a deep learning technique that would sort the fruits into normal and abnormal based on the features such as fruit colour, number of fruit spots, and shape of the fruit. Methods: A combined CNN LSTM deep learning model is applied to classify a set of 6519 fruits into two classes namely normal and abnormal. The dataset is an excel file with each record consisting of 9 features. Convolutional Neural Networks (CNN) are applied for deep feature extraction and Long-Short Term Memory (LSTM) is used to detect the class based on extracted features. Findings: The proposed system achieved an accuracy of 98.17%, specificity of 98.65%, sensitivity of 97.77%, and an F1-score of 98.39%. Novelty: The sensitivity of disease detection was less with lesser availability of enhanced detection methods for detecting disease in earlier stages. The issue with these various existing algorithms is that the accuracy was reduced since some sources of errors were not eliminated. Deep Learning delivers methodologies, approaches, and functionalities that can help to resolve analytic and predictive analysis accurately. Keywords: Deep Learning; CNN LSTM; Classification; Hyperparameters

Highlights

Pomegranate (1) is one of the major fruits produced in India
There are several studies carried out to detect diseases in fruits using Convolutional Neural Network Some of the work related to the application of Convolutional Neural Networks (CNN) are given below
Fruit recognition from images using deep learning(13) had recognized different types of fruits such as apple pomegranate etc. with an accuracy of 98%Fruit recognition using images is carried out using CNN(14) and obtained an accuracy of 100%

Summary

Introduction

Pomegranate (1) is one of the major fruits produced in India. According to International Trade Centre India stands first in the production of pomegranates worldwide. 5 percent of the fruits produced in our country are exported every year. Pomegranate export earns a considerable foreign exchange for our country but not much research found to be carried out on pomegranate fruit quality classification. It is essential to classify the fruits into normal and abnormal accurately post-yield given marketing and export. The presence of disease in the fruit can be detected by external features like the colour of the fruit, the lesions or black spots, the weight of the fruit, the plant stand and so on. There are several studies carried out to detect diseases in fruits using Convolutional Neural Network Some of the work related to the application of CNN are given below

Objectives

Methods

Findings

Conclusion