Plant Leaf Disease Detection using Machine Learning

Tulshan, Amrita S.; Raul, Nataasha

doi:10.1109/icccnt45670.2019.8944556

Cited by 61 publications

(19 citation statements)

References 7 publications

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“…Finally, apply the K Nearest Neighbour classifier to detect the diseases of the leaf. The proposed implementation predicts the 98% accuracy for disease detection [6].…”

Section: Literatures Surveymentioning

confidence: 99%

Leaf Diseases Detection of Medicinal Plants Based on Support Vector Machine Classification Algorithm

Bose

Dutta²,

Goyal

et al. 2021

JPRI

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On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. The different species of plants have different diseases. Therefore, it is required to identify the plants as well as their diseases correctly. It is difficult and also time consuming to identify the plants and their diseases manually. In this research an automatic disease detection system of plant is proposed. High-quality leaf images are used for training and testing. For detecting the healthy area and diseased area in a leaf, region-based and color-based region thresholding techniques are used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally, for classification two-class and multi-class Support Vector Machine (SVM) were used. It is found that both feature selection processes with SVM give 99% accuracy. An user oriented graphical user interface is created for understanding the automated system.

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“…Finally, apply the K Nearest Neighbour classifier to detect the diseases of the leaf. The proposed implementation predicts the 98% accuracy for disease detection [6].…”

Section: Literatures Surveymentioning

confidence: 99%

Leaf Diseases Detection of Medicinal Plants Based on Support Vector Machine Classification Algorithm

Bose

Dutta²,

Goyal

et al. 2021

JPRI

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“…In recent times, a lot of methods are proposed in the classification of plant diseases (Warne and Ganorkar, 2015; Kadir et al, 2013;Shergill et al, 2015;Sumathi and Kumar, 2012;Khirade and Patil, 2015;Beghin et al, 2010;Sladojevic et al, 2016;Tulshan and Raul, 2019;Sibiya and Sumbwanyambe, 2019). Warne and Ganorkar (2015) proposed a machine vision approach for recognition of three types collected with one type characterized by the damages of a tormentor insect; green stink and two types visualized by symptoms of 2 pathogens; Bacteria angular and Ascochyta blight.…”

Section: Related Workmentioning

confidence: 99%

“…In this case, the achieved results on the proposed approach provide an average accuracy of 94.60%. Tulshan and Raul (2019) applied a plant leaf disease detection technique to detect a disease from the input images. This technique includes many steps as, image segmentation, feature extraction.…”

Section: Related Workmentioning

confidence: 99%

“…To situate the effectiveness of our proposed approach in comparison with the most relevant classification approach proposed in the literature, Table 8 presents a classification accuracy comparative study where our proposed approach is compared with Deep Learning-based approaches for plant diseases. Concerning these approaches (Sladojevic et al, 2016;Tulshan and Raul, 2019;Sibiya and Sumbwanyambe, 2019) are presenting before in related work section. Although our approach is based on machine learning using handcrafted features, the classification accuracy obtained exceeds recent approaches based on Deep Learning.…”

Section: Classificationmentioning

confidence: 99%

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Early Detection and Classification Approach for Plant Diseases based on MultiScale Image Decomposition

Ennouni¹,

Sihamman²,

Sabri³

et al. 2021

Journal of Computer Science

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This paper presents a new and powerful approach for detecting and classifying leaf diseases for plant diagnosis with high accuracy. The main contribution of this paper is that a hybrid approach is proposed by using the combination of Partial Differential Equations (PDE) based image decomposition, segmentation, feature extraction, features selection and classification aiming to improve the classification accuracy and provide an excellent diagnosis. The TV-L 1 Total variation model is adopted to separate the original image into texture and object components. Segmentation will be done only on the object component. Then texture, color, vein and shape features are extracted and merged in a feature vector using the codebook method. Moreover, features are selected by the RelieF feature selection algorithm to keep only relevant ones. In the classification, only selected features will be used and passed to the Multiclass Support Vector Machine algorithm SVM. The proposed approach is implemented and tested on the PV Plant Village dataset and provided a good and greater classification accuracy compared with the existing approaches from the literature. The obtained results proved that the use of PDE influences on the segmentation, which in turn, allowed us to identify correctly the leaves and provide new and optimal features, those features improves the classification accuracy rate to 95.9%.

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“…To detect the apple leaf disease authors proposed a deep neural base improved convolution neural network model. The proposed implementation predicts the 98% accuracy for disease detection [6].…”

Section: Literature Surveymentioning

confidence: 99%

Leaf Diseases Detection of Medicinal Plants based on Image Processing and Machine Learning Processes<strong> </strong>

Bose¹,

Dutta²,

Goyal³

et al. 2021

Preprint

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: On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. Different plants have different diseases. Therefore it is needed to identify the plants and their diseases to prevent loss. Now to identify the plants and their diseases manually is very time consuming. In this research an automatic plant and their disease detection system is proposed. For experimental purposes, high-quality leaf images are accepted for training and testing. For detecting the healthy and diseased area in a leaf, region-based and color-based region thresholding techniques were used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally for classification two-class and multi-class Support Vector Machine (SVM) was used. It is observed that both feature selection processes with SVM give 99% accuracy. Finally to understand the automated system a graphical user interface was created for all users.

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Plant Leaf Disease Detection using Machine Learning

Cited by 61 publications

References 7 publications

Leaf Diseases Detection of Medicinal Plants Based on Support Vector Machine Classification Algorithm

Leaf Diseases Detection of Medicinal Plants Based on Support Vector Machine Classification Algorithm

Early Detection and Classification Approach for Plant Diseases based on MultiScale Image Decomposition

Leaf Diseases Detection of Medicinal Plants based on Image Processing and Machine Learning Processes<strong> </strong>

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