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

IF 0.6 Q3 ENGINEERING, MULTIDISCIPLINARY
Chyntia Jaby Ak Entuni, T. Zulcaffle, Kismet Hong Ping, A. Sharangi, T. Upadhyay, Mohd Saeed
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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.
基于低成本KINECT相机和改进CNN算法的辣椒叶病智能农业监测解决方案
辣椒在全球范围内广泛种植,作为一种食物尤为重要。辣椒种植面临的最困难的问题之一是要求准确识别叶病。叶病对辣椒生产质量有负面影响,给农民带来重大损失。已经开发了许多机器学习(ML)和卷积神经网络(CNN)模型,用于在均匀背景和不复杂的叶片条件下对辣椒叶片病害进行分类,并达到了平均分类精度。然而,患病的叶子通常与一簇其他叶子并排生长,因此很难对疾病进行分类。如果有一个可靠的模型可以将辣椒叶病分类在一簇叶子中,对农民来说会更容易。本研究的目的是提出一个从均匀背景和复杂叶簇对辣椒叶病进行分类的模型。病辣椒叶的图像是使用低成本的Kinect相机获取的,包括变色、灰点和卷曲的叶子。然后使用改进的ShuffleNet CNN模型对不同类型的辣椒叶病进行分类。所提出的模型的分类准确率为99.82%,优于其他现有模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Jurnal Teknologi-Sciences & Engineering
Jurnal Teknologi-Sciences & Engineering ENGINEERING, MULTIDISCIPLINARY-
CiteScore
1.30
自引率
0.00%
发文量
96
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