House Plant Leaf Disease Detection and Classification Using Machine Learning

Usharani, Bhimavarapu

doi:10.4018/978-1-7998-8161-2.ch002

Cited by 1 publication

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References 9 publications

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“…Barbedo [5] has experimented on the effect of the size of the dataset and image background for effectively analysing the plant disease clategorization. Usharani [6] has worked on the house plant leaf (Hibiscus herb) disease detection and classification using K-nearest neighbor (KNN) algorithm. Sabrol and Satish [7] have used a classification tree for the purpose of tomato plant disease classification based on five types of tomato disease.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Machine Learning Models for Plant Disease Classification Using Modified GLCM and Wavelet Based Statistical Features

Tabbakh¹,

Barpanda²

2022

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In this paper, different types of plant diseases in the PlantVillage dataset are getting focused for classification. In the realm of machine vision, plant disease identification is one of the most crucial tasks in the agricultural sector. It is a technique that employs equipment to capture images to detect and classify different types of diseases in plants. However, nakedeye monitoring of plants is impractical due to long processing times and a lack of specialists on farms in remote locations. Hence, combining image processing techniques with machine learning provides a solution to the problem of agricultural production while also ensuring food security. The plant features are extracted using a modified gray-level co-occurrence matrix (GLCM) technique and based on various statistical features. Both of these approaches were applied on original images with background and segmented images without background. Wavelet transform is also used with segmented images to decompose the image into sub-bands. All the features obtained are combined and SMOTE technique is used to balance the dataset prior to classification. For the purpose of classification, six machine learning models were compared, namely Light Gradient Boosting Machine (LGBM), Random Forest (RF), Decision Trees (DT), Logistic Regression (LG), AdaBoost, and Support Vector Machine (SVM). Further, different combinations of features were experimented and the experimental results prove that employing LGBM and SVM models resulted in attaining higher accuracy values i.e. 94.39% and 93.15%, respectively.

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Section: Related Workmentioning

confidence: 99%

Evaluation of Machine Learning Models for Plant Disease Classification Using Modified GLCM and Wavelet Based Statistical Features

Tabbakh¹,

Barpanda²

2022

View full text Add to dashboard Cite

show abstract

House Plant Leaf Disease Detection and Classification Using Machine Learning

Cited by 1 publication

References 9 publications

Evaluation of Machine Learning Models for Plant Disease Classification Using Modified GLCM and Wavelet Based Statistical Features

Evaluation of Machine Learning Models for Plant Disease Classification Using Modified GLCM and Wavelet Based Statistical Features

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