Severity Estimation of Plant Leaf Diseases Using Segmentation Method

Entuni, Chyntia Jaby; Zulcaffle, Tengku Mohd Afendi; Kipli, Kuryati

doi:10.14416/j.asep.2020.11.004

Cited by 4 publications

(4 citation statements)

References 18 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: 86%

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: 86%

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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“…A maximum mean accuracy of 99% is achieved using the Bagged tree (BT) classifier using color features compared to the other classifiers (KNN, SVM, Complex Tree, and BT). Fuzzy C-Means clustering-based segmentation is used to segment the diseased location in corn leaves [ 17 ]. They have used the Plant Village image dataset for developing their model to identify three different types of diseases in corn leaf such as blight, gray spot, rust, and normal leaf.…”

Section: State-of-the-art Work Related To Leaf Disease Detectionmentioning

confidence: 99%

Brinjal leaf diseases detection based on discrete Shearlet transform and Deep Convolutional Neural Network

Abisha¹,

Mutawa

Murugappan

et al. 2023

PLoS ONE

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Different diseases are observed in vegetables, fruits, cereals, and commercial crops by farmers and agricultural experts. Nonetheless, this evaluation process is time-consuming, and initial symptoms are primarily visible at microscopic levels, limiting the possibility of an accurate diagnosis. This paper proposes an innovative method for identifying and classifying infected brinjal leaves using Deep Convolutional Neural Networks (DCNN) and Radial Basis Feed Forward Neural Networks (RBFNN). We collected 1100 images of brinjal leaf disease that were caused by five different species (Pseudomonas solanacearum, Cercospora solani, Alternaria melongenea, Pythium aphanidermatum, and Tobacco Mosaic Virus) and 400 images of healthy leaves from India’s agricultural form. First, the original plant leaf is preprocessed by a Gaussian filter to reduce the noise and improve the quality of the image through image enhancement. A segmentation method based on expectation and maximization (EM) is then utilized to segment the leaf’s-diseased regions. Next, the discrete Shearlet transform is used to extract the main features of the images such as texture, color, and structure, which are then merged to produce vectors. Lastly, DCNN and RBFNN are used to classify brinjal leaves based on their disease types. The DCNN achieved a mean accuracy of 93.30% (with fusion) and 76.70% (without fusion) compared to the RBFNN (82%—without fusion, 87%—with fusion) in classifying leaf diseases.

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“…Entuni et al [52] also computed the severity of the disease in plant leaves by combining Fuzzy C-Means and YCbCr colour space. It was concluded that YCbCr colour space has a greater detection rate when compared with RGB, HSV, 𝐿 * 𝑎 * 𝑏, as YCbCr can separate luminance from chrominance more effectively.…”

Section: -2-role Of Image Segmentationmentioning

confidence: 99%

“…The proposed method uses K-means clustering to separate the diseased lesion, from which the affected ratio is calculated to predict the severity. K-means algorithm performed on km-ADDS is as follows [52,[58][59][60][61]: K=3 is used in the proposed model. Figure 11 shows the segmentation results and the different steps involved in segmentation of the diseased spot from the cropped image using K-means via applying the algorithm.…”

Section: -1-2-image Segmentation and Severity Estimationmentioning

confidence: 99%

Smart Farm-Care using a Deep Learning Model on Mobile Phones

Francis¹,

Anbananthen

Deisy³

et al. 2023

Emerg Sci J

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Deep learning and its models have provided exciting solutions in various image processing applications like image segmentation, classification, labeling, etc., which paved the way to apply these models in agriculture to identify diseases in agricultural plants. The most visible symptoms of the disease initially appear on the leaves. To identify diseases found in leaf images, an accurate classification system with less size and complexity is developed using smartphones. A labeled dataset consisting of 3171 apple leaf images belonging to 4 different classes of diseases, including the healthy ones, is used for classification. In this work, four variants of MobileNet models - pre-trained on the ImageNet database, are retrained to diagnose diseases. The model’s variants differ based on their depth and resolution multiplier. The results show that the proposed model with 0.5 depth and 224 resolution performs well - achieving an accuracy of 99.6%. Later, the K-means algorithm is used to extract additional features, which helps improve the accuracy to 99.7% and also measures the number of pixels forming diseased spots, which helps in severity prediction. Doi: 10.28991/ESJ-2023-07-02-013 Full Text: PDF

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Severity Estimation of Plant Leaf Diseases Using Segmentation Method

Cited by 4 publications

References 18 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

Brinjal leaf diseases detection based on discrete Shearlet transform and Deep Convolutional Neural Network

Smart Farm-Care using a Deep Learning Model on Mobile Phones

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