Real-time automatic detection and classification of groundnut leaf disease using hybrid machine learning techniques

Suresh, Vignesh; Seetharaman, K.

doi:10.1007/s11042-022-12893-1

Cited by 14 publications

(4 citation statements)

References 39 publications

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“…Disease classification was performed by determining the disease area using hybrid machine learning techniques, optimal feature selection and deep neural network based on moth optimization. The results obtained confirm that the proposed method is effective [14].…”

Section: Related Worksupporting

confidence: 68%

The Effectiveness of Deep Learning Methods on Groundnut Disease Detection

Kursun,

Yasin,

Koklu

2023

icat

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Early detection of plant diseases in the agricultural sector is considered an important goal to increase productivity and minimize damage. This study deals with the use of deep learning methods to realize the automatic detection of leaf diseases in peanut plants and the explicability of the model with heatmap visualizations formed during the detection of diseases. In the study, a dataset containing 3058 images with 5 classes enriched with diseased and healthy samples of peanut leaves was used. The explainability property has also been studied to understand why the models detect a particular disease. The decision processes of deep learning models, which are usually described as the "magic box", were visualized with the heatmap method in this study. By highlighting the pixels that are effective in detecting diseased leaves with heatmap visualization, the decision-making process of the model has been tried to be made understandable. The results show that deep learning models have high performance in detecting peanut leaf diseases, and the explainability obtained by heatmap visualization is a reliable tool for agricultural specialists and producers. Thanks to the visual explanations provided by the model, the level of confidence in the detection of diseases has been increased and confidence in the decision processes of the model has been provided. This study constitutes an important step towards increasing efficiency in agricultural applications and providing a more efficient approach to disease management by investigating the impact and explicability of deep learning methods in the field of disease detection in peanut plants.

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Section: Related Worksupporting

confidence: 68%

The Effectiveness of Deep Learning Methods on Groundnut Disease Detection

Kursun,

Yasin,

Koklu

2023

icat

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“…Unpredictable natural environments with changing light conditions, varying shadows, and inconsistent background textures posed significant challenges. The dynamic nature of these environmental factors made traditional methods resource-intensive and less effective in ensuring accurate pest detection despite their focus on plant pathologies for classification [ 13 ]. Automation in agriculture has advanced significantly in recent years because of robotic devices and artificial intelligence.…”

Section: Basic Preliminaries and Literature Workmentioning

confidence: 99%

GNViT- An enhanced image-based groundnut pest classification using Vision Transformer (ViT) model

P.,

2024

PLoS ONE

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Crop losses caused by diseases and pests present substantial challenges to global agriculture, with groundnut crops particularly vulnerable to their detrimental effects. This study introduces the Groundnut Vision Transformer (GNViT) model, a novel approach that harnesses a pre-trained Vision Transformer (ViT) on the ImageNet dataset. The primary goal is to detect and classify various pests affecting groundnut crops. Rigorous training and evaluation were conducted using a comprehensive dataset from IP102, encompassing pests such as Thrips, Aphids, Armyworms, and Wireworms. The GNViT model’s effectiveness was assessed using reliability metrics, including the F1-score, recall, and overall accuracy. Data augmentation with GNViT resulted in a significant increase in training accuracy, achieving 99.52%. Comparative analysis highlighted the GNViT model’s superior performance, particularly in accuracy, compared to state-of-the-art methodologies. These findings underscore the potential of deep learning models, such as GNViT, in providing reliable pest classification solutions for groundnut crops. The deployment of advanced technological solutions brings us closer to the overarching goal of reducing crop losses and enhancing global food security for the growing population.

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“…The research work in [13] proposed new model which is based on recognition and classification of groundnut crop leaf diseases. ICS algorithm is used for segmenting the leaves which are affected by diseases.…”

Section: B Classification Of Leaf Diseasesmentioning

confidence: 99%

Leaf Diseases Identification and Classification of Self-Collected Dataset on Groundnut Crop using Progressive Convolutional Neural Network (PGCNN)

Anbumozhi¹,

A²

2023

IJACSA

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A healthy crop is required to provide high-quality food for daily consumption. Crop leaf diseases have more influence on agronomic production and our country. Earlier, many scholars relied on traditional techniques to detect and classify leaf diseases. Furthermore, classification at an early stage is impossible when there are not enough experts and inadequate research facilities. As technology progresses into our day to day life, an Artificial Intelligence subset called Deep Learning (DL) models plays a vital role in the automatic identification of groundnut leaf diseases. The essential for controlling diseases that are spread to the healthy development of groundnut farming. Deep Learning can resolve the issues of finding leaf diseases early and effectively. Most of the researchers concentrate on detecting leaf diseases by doing research in Machine Learning (ML) approaches, which leads to low accuracy and high loss. To achieve better accuracy and decreases the loss in the DL model by identifying the leaf diseases of groundnut crops at an early stage, we propose the Progressive Groundnut Convolutional Neural Network (PGCNN) model. This paper mainly focuses on identifying and classifying groundnut leaf diseases with a self-collected dataset which is collected from the various climatic conditions around the village located nearby Pudukkottai district, Tamil Nadu, India. The common diseases that occurred in those areas were gathered namely early spot, late spot, rust, and rosette. Model Performance metrics analysis was done to evaluate the model performance and also compared with the various DL architectures like AlexNet, VGG11, VGG13, VGG16, and VGG19. The proposed models have achieved a training accuracy of 99.39% and a validation accuracy of 97.58%, continuing with an overall accuracy of 97.58%.

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Real-time automatic detection and classification of groundnut leaf disease using hybrid machine learning techniques

Cited by 14 publications

References 39 publications

The Effectiveness of Deep Learning Methods on Groundnut Disease Detection

The Effectiveness of Deep Learning Methods on Groundnut Disease Detection

GNViT- An enhanced image-based groundnut pest classification using Vision Transformer (ViT) model

Leaf Diseases Identification and Classification of Self-Collected Dataset on Groundnut Crop using Progressive Convolutional Neural Network (PGCNN)

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