SMART AGRICULTURAL MONITORING SOLUTION FOR CHILLI LEAF DISEASES USING A LOW-COST KINECT CAMERA AND AN IMPROVED CNN ALGORITHM

Chyntia Jaby ak Entuni; Tengku Mohd Afendi Zulcaffle; Kismet Hong Ping; Amit Baran Sharangi; Tarun Kumar  Upadhyay; Mohd Saeed

doi:10.11113/jurnalteknologi.v85.19884

Authors

Chyntia Jaby ak Entuni Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, Malaysia https://orcid.org/0000-0002-3971-2123
Tengku Mohd Afendi Zulcaffle Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, Malaysia
Kismet Hong Ping Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, Malaysia
Amit Baran Sharangi Department of Plantation Spices, Medicinal and Aromatic Crops, Bidhan Chandra Agricultural University, West Bengal, India
Tarun Kumar Upadhyay Department of Biotechnology, Parul Institute of Applied Sciences and Animal Cell Culture and Immunobiochemistry Lab, Centre for Research and Development, Parul University, Vadodara 391760 Gujarat, India
Mohd Saeed Department of Biology, College of Sciences, University of Hail, Hail 4464, Saudi Arabia

DOI:

https://doi.org/10.11113/jurnalteknologi.v85.19884

Keywords:

Chilli, leaf disease, Machine Learning, Convolutional Neural Network, ShuffleNet

Abstract

Chilli is extensively grown all over the globe and is particularly important as a food. One of the most difficult issues confronting chilli cultivation is the requirement for accurate identification of leaf diseases. Leaf diseases have a negative impact on chilli production quality, resulting in significant losses for farmers. Numerous Machine Learning (ML) and Convolution Neural Network (CNN) models have been developed for classifying chilli leaf diseases under uniform background and uncomplicated leaf conditions, with an average classification accuracy achieved. However, a diseased leaf usually grows alongside a cluster of other leaves, making it difficult to classify the disease. It will be easier for farmers if there is a reliable model that can classify a chilli leaf disease in a cluster of leaves. The aim of this study was to propose a model for classifying chilli leaf disease from both a uniform background and a complex cluster of leaves. Images of diseased chilli leaves are acquired using a low-cost Kinect camera, which include discoloration, grey spots, and leaf curling. The different types of chilli leaf disease are then classified using an improved ShuffleNet CNN model. With a classification accuracy of 99.82%, the proposed model outperformed the other existing models.

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