Image Processing Technique for the Detection of Alberseem Leaves Diseases Based on Soft Computing

Abusham, Eimad Abdu

doi:10.52098/airdj.202127

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…The integration of these techniques leads to a noteworthy improvement of 5.86% in solution quality compared to individual segmentation approaches. Notably, this represents the first instance wherein image segmentation has been shown to significantly enhance the accuracy of the proposed model, aligning with previous works [62][63][64][65][66], which also affirm the efficacy of image segmentation in enhancing model accuracy across various applications.…”

Section: Advancing Leaf Disease Classification In Cau: a Meta-learner...supporting

confidence: 87%

Two-Stage Ensemble Deep Learning Model for Precise Leaf Abnormality Detection in Centella asiatica

Buakum,

Kosacka-Olejnik,

Pitakaso

et al. 2024

AgriEngineering

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Leaf abnormalities pose a significant threat to agricultural productivity, particularly in medicinal plants such as Centella asiatica (Linn.) Urban (CAU), where they can severely impact both the yield and the quality of leaf-derived substances. In this study, we focus on the early detection of such leaf diseases in CAU, a critical intervention for minimizing crop damage and ensuring plant health. We propose a novel parallel-Variable Neighborhood Strategy Adaptive Search (parallel-VaNSAS) ensemble deep learning method specifically designed for this purpose. Our approach is distinguished by a two-stage ensemble model, which combines the strengths of advanced image segmentation and Convolutional Neural Networks (CNNs) to detect leaf diseases with high accuracy and efficiency. In the first stage, we employ U-net, Mask-R-CNN, and DeepNetV3++ for the precise image segmentation of leaf abnormalities. This step is crucial for accurately identifying diseased regions, thereby facilitating a focused and effective analysis in the subsequent stage. The second stage utilizes ShuffleNetV2, SqueezeNetV2, and MobileNetV3, which are robust CNN architectures, to classify the segmented images into different categories of leaf diseases. This two-stage methodology significantly improves the quality of disease detection over traditional methods. By employing a combination of ensemble segmentation and diverse CNN models, we achieve a comprehensive and nuanced analysis of leaf diseases. Our model’s efficacy is further enhanced through the integration of four decision fusion strategies: unweighted average (UWA), differential evolution (DE), particle swarm optimization (PSO), and Variable Neighborhood Strategy Adaptive Search (VaNSAS). Through extensive evaluations of the ABL-1 and ABL-2 datasets, which include a total of 14,860 images encompassing eight types of leaf abnormalities, our model demonstrates its superiority. The ensemble segmentation method outperforms single-method approaches by 7.34%, and our heterogeneous ensemble model excels by 8.43% and 14.59% compared to the homogeneous ensemble and single models, respectively. Additionally, image augmentation contributes to a 5.37% improvement in model performance, and the VaNSAS strategy enhances solution quality significantly over other decision fusion methods. Overall, our novel parallel-VaNSAS ensemble deep learning method represents a significant advancement in the detection of leaf diseases in CAU, promising a more effective approach to maintaining crop health and productivity.

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Section: Advancing Leaf Disease Classification In Cau: a Meta-learner...supporting

confidence: 87%

Two-Stage Ensemble Deep Learning Model for Precise Leaf Abnormality Detection in Centella asiatica

Buakum,

Kosacka-Olejnik,

Pitakaso

et al. 2024

AgriEngineering

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“…From the proposed work in [22], the results show that the system recognized Alfalfa diseases effectively by up to 90% which was achieved using K-Means Clustering, KNN algorithm, and Local Binary Pattern (LBP). Fig.…”

Section: Review Based On Resultsmentioning

confidence: 99%

“…Soft Computing. K-means clustering is utilized in this technique to identify diseased leaf tissue [22]. This study will utilize KNN to identify diseased leaves, classify them by disease kind, and display the results.…”

Section: Knnmentioning

confidence: 99%

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Plant Leaf Disease Detection Using Image Processing: A Comprehensive Review

Hasan

Mustavi

Jubaer

et al. 2022

Malaysian J. Sci. Adv. Tech.

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In this review paper, previous and current works for plant leaf disease detection have been studied. The traditional manual visual quality inspection cannot be defined systematically as this method is unpredictable and inconsistent. Moreover, it involves a remarkable amount of expertise in the field of plant disease diagnostics (phytopathology) in addition to the disproportionate processing times. Hence, image processing has been applied for the recognition of plant diseases. This paper has been divided into three main parts. In the first part, a comprehensive review based on algorithms is provided were the major algorithms and works conducted using image processing and artificial intelligence algorithms have been compared. The second part discusses the frameworks and compared the previous works. Then, a comprehensive discussion based on the accuracy of the results was provided. Based on the review conducted, a detailed explanation of the illnesses detection and classification performance is provided. Finally, the findings and challenges in plant leaf detection using image processing are summarized and discussed.

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“…The KNN method will then be used to classify the various types of diseases that are prevalent in this area. These include the fungus and pest diseases, red spider, and leaf minor [19,20]. A system was developed that uses image processing to identify plant diseases.…”

Section: Literature Workmentioning

confidence: 99%

Automated Detection and Classification of Rice Crop Diseases Using Advanced Image Processing and Machine Learning Techniques

Chaudhary,

Kumar

2024

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Agriculture, a pivotal sector in the Indian economy, plays a crucial role in national development. A significant challenge within this domain is the detection of crop diseases, with brown spot, leaf blast, and bacterial blight being prevalent afflictions in rice crops. This study presents an innovative approach, integrating Gray-level Co-occurrence Matrix (GLCM) and Intensity-Level Based Multi-Fractal Dimension (ILMFD) for feature extraction in disease identification. The efficacy of this integrated technique was evaluated through a comparison with various classifiers. Specifically, the Artificial Neural Network (ANN), Support Vector Machine (SVM), and Neuro-Genetic Algorithm (Neuro-GA) were employed to ascertain their precision in disease detection. It was observed that the combination of GLCM and ILMFD with the Neuro-GA classifier achieved an accuracy exceeding 90%. Remarkably, when paired with the SVM classifier, this integrated approach yielded a precise accuracy of 96.7% in detecting brown spot disease in rice. These findings not only validate the effectiveness of the GLCM and ILMFD methods in feature extraction but also highlight the superior performance of the SVM classifier in crop disease detection. This research contributes significantly to the field, offering a robust solution for accurate disease diagnosis in rice crops, thereby aiding in the sustainable management of agricultural practices.

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Image Processing Technique for the Detection of Alberseem Leaves Diseases Based on Soft Computing

Cited by 5 publications

References 14 publications

Two-Stage Ensemble Deep Learning Model for Precise Leaf Abnormality Detection in Centella asiatica

Two-Stage Ensemble Deep Learning Model for Precise Leaf Abnormality Detection in Centella asiatica

Plant Leaf Disease Detection Using Image Processing: A Comprehensive Review

Automated Detection and Classification of Rice Crop Diseases Using Advanced Image Processing and Machine Learning Techniques

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