Plant disease detection using GLCM feature extractor and voting classification approach

Mathew, Amal; Antony, Anson; Mahadeshwar, Yash; Khan, Tanisha; Kulkarni, Apeksha

doi:10.1016/j.matpr.2022.02.350

Cited by 16 publications

(8 citation statements)

References 14 publications

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“…Pandian et al proposed 14‐DCNN for plant disease detection, outperforming other models (Pandian, Kumar, et al, 2022). Mathew et al utilized Grey‐Level Co‐Occurrence Matrix (GLCM) for feature extraction, a K‐means algorithm for segmentation, and a voting classifier for disease prediction from plant leaf images (Mathew et al, 2022). Rajpoot et al utilized a combination of Brightness‐Preserving Bi‐Histogram Equalization (BBHE) (Wang & Ye, 2005) and CNN for early‐stage mango disease detection (Rajpoot et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

An analysis to investigate plant disease identification based on machine learning techniques

Duhan,

Gulia,

Gill

et al. 2024

Expert Systems

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In agriculture, crops are severely affected by illnesses, which reduce their production every year. The detection of plant diseases during their initial stages is critical and thus needs to be addressed. Researchers have been making significant progress in the development of automatic plant disease recognition techniques through the utilization of machine learning (ML), image processing, and deep learning (DL). This study analyses the recent advancements made by researchers in the field of ML techniques for identifying plant diseases. This study also examines various methods used by researchers to produce ML solutions, such as image preprocessing, segmentation, and feature extraction. This study highlights the challenges encountered while creating plant disease identification systems, such as small datasets, image capture conditions, and the generalizability of the models, and discusses possible solutions to cater to these problems. Still, the development of a solution that automatically detects various plant diseases for various plant species remains a big challenge. To address these challenges, there is a need to create a system that is trained on an extensive dataset that contains images of various types of diseases a plant can suffer from, and plant images should be taken at various stages of the disease's development. This study further presents an analysis of various methods used at different stages of plant disease identification.

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Section: Methodsmentioning

confidence: 99%

An analysis to investigate plant disease identification based on machine learning techniques

Duhan,

Gulia,

Gill

et al. 2024

Expert Systems

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“…Ensemble learning ( Ganaie et al., 2022 ) is an effective way to improve the generalization performance of the recognition method. The authors of refs ( Chaudhary et al., 2020 ) ( Mathew et al., 2022 ) ( Palanisamy and Sanjana, 2023 ). introduced the voting strategy of ensemble learning to crop disease recognition and observed improvements in recognition accuracy in the experiments.…”

Section: Related Workmentioning

confidence: 99%

A novel ensemble learning method for crop leaf disease recognition

He,

Zhang,

Gao

2024

Front. Plant Sci.

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Deep learning models have been widely applied in the field of crop disease recognition. There are various types of crops and diseases, each potentially possessing distinct and effective features. This brings a great challenge to the generalization performance of recognition models and makes it very difficult to build a unified model capable of achieving optimal recognition performance on all kinds of crops and diseases. In order to solve this problem, we have proposed a novel ensemble learning method for crop leaf disease recognition (named ELCDR). Unlike the traditional voting strategy of ensemble learning, ELCDR assigns different weights to the models based on their feature extraction performance during ensemble learning. In ELCDR, the models’ feature extraction performance is measured by the distribution of the feature vectors of the training set. If a model could distinguish more feature differences between different categories, then it receives a higher weight during ensemble learning. We conducted experiments on the disease images of four kinds of crops. The experimental results show that in comparison to the optimal single model recognition method, ELCDR improves by as much as 1.5 (apple), 0.88 (corn), 2.25 (grape), and 1.5 (rice) percentage points in accuracy. Compared with the voting strategy of ensemble learning, ELCDR improves by as much as 1.75 (apple), 1.25 (corn), 0.75 (grape), and 7 (rice) percentage points in accuracy in each case. Additionally, ELCDR also has improvements on precision, recall, and F1 measure metrics. These experiments provide evidence of the effectiveness of ELCDR in the realm of crop leaf disease recognition.

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“…On older leaves, it appears as on the underside of the leaf, there are very little brown specks that look like a bull's eye and have concentric rings. When the disease matures, the leaf turns yellow due to the disease spreading outward discussed by Mathew et al (14) . The upper surfaces of older leaves develop yellow to white patches due to soft mildew.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Convolutional Neural Network with Whale Optimization Algorithm (HCNNWO) Based Plant Leaf Diseases Detection

Banu,

Geetha

2023

Data and Metadata

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Plant diseases appear to be posing a serious danger to the production and availability of food globally. The main factor affecting the quality and productivity of agricultural products is the health of the plants. In this paper, we describe a modified plant disease detection using deep convolutional neural networks in real time. By employing image processing techniques to enlarge the plant illness photos, the plant disease sets of data were initially produced. To recognise plant illnesses, a system called Convolutional Neural Network combined with Wolf Optimisation algorithm (CNN-WO) was used. Finally, the Whale Optimization algorithm (WO) is used to maximise and optimizes getting input. And it is given to CNN's learning rate for classification process. This paper presents an image segmentation and classification technique to automatically identify plant leaf diseases. The suggested strategy increased accuracy, sensitivity, precision, F1 measure, and specificity of plant disease detection. According to this study, HCNNWO real detectors have improved, which would require deep learning. It would be an effective method for determining plant illnesses and other diseases within plants. According to the evaluation report, the suggested method offers good reliability. To evaluate how well the suggested algorithm performs in comparison to cutting-edge techniques such as SVM, BPNN and CNN, experiments are conducted on datasets that are openly accessible.

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Plant disease detection using GLCM feature extractor and voting classification approach

Cited by 16 publications

References 14 publications

An analysis to investigate plant disease identification based on machine learning techniques

An analysis to investigate plant disease identification based on machine learning techniques

A novel ensemble learning method for crop leaf disease recognition

Hybrid Convolutional Neural Network with Whale Optimization Algorithm (HCNNWO) Based Plant Leaf Diseases Detection

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