Tomato Leaf Disease Detection Using Multiple Classifier System

Shetty, Pallavi; Kumar, Arhath; Rajesh, B.; Balipa, Mamatha; Kanchan, Gauthami; Kamath, C. Swathi

doi:10.1109/aide57180.2022.10059795

Cited by 1 publication

(1 citation statement)

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“…al. [38], focused on classifying diseases in tomato plant leaves using image processing and machine learning classifiers. They aimed to classify four diseases: Leaf mold, Late blight, Bacterial spot, and Early blight using Linear discrimination analysis, Logistic regression, KNN, Decision tree, SVM, Naïve Bayes, and Random Forest.…”

Section: Related Workmentioning

confidence: 99%

Plant Leaf Disease Detection Using Efficient Image Processing and Machine Learning Algorithms

Kiran,

Chandrappa

2023

Journal of Robotics and Control

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India is often described as a country of villages, where a majority of the population depends on agriculture for their livelihood. The landscape of Indian agriculture is approximately 159.7 million hectares. Agriculture plays a pivotal role in India's Gross Domestic Product (GDP), accounting for about 18% of the nation's economic output. Diseases and pests can have detrimental effects on crops, leading to reduced yields. These challenges can include the spread of plant diseases, infestations by insects or other pests, and the overall degradation of crop health. Early detection of diseases in crops is crucial for several reasons. Detecting diseases at an early stage allows for prompt intervention, such as applying appropriate pesticides or taking preventive measures. The main aim of this study is to develop a highly effective method for plant leaf disease detection using computer vision techniques. Here, leaf disease detection comprises histogram equalization, denoising, image color threshold masking, feature descriptors such as Haralick textures, Hu moments, and color histograms to extract the salient features of leaf images. These features are then used to classify the images by training Logistic Regression, Linear Discriminant Analysis, K-nearest neighbor, decision tree, Random Forest, and Support Vector Machine algorithms using K-fold validation. K-fold validation is used to separate the validation samples from the training samples, and the K indicates the number of times this is repeated for the generalization. The training and validation processes are performed in two approaches. The first approach uses default hyperparameters with segmented and non-segmented images. In the second approach, all hyperparameters of the models are optimized to train segmented datasets. The classification accuracy improved by 2.19% by utilizing segmentation and hyperparameter tuning further improved by 0.48%. The highest average classification accuracy of 97.92% is achieved using the Random Forest classifier to classify 40 classes of 10 different plant species. Accurate detection of plant disease leads to the sustained growth of plants throughout the growing span of the plants.

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Section: Related Workmentioning

confidence: 99%