Application of logistic regression model for predicting the association of climate change resilient cultural practices with early blight of tomato (<i>Alternaria solani</i>) epidemics in the East Shewa, Central Ethiopia

Wato, Tamirat; Tadelo, Meseret; Negash, Tilahun

doi:10.1080/17429145.2021.2009581

Cited by 4 publications

(1 citation statement)

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“…The logistic regression model converts the continuous output of the linear regression function into categorical values by applying a sigmoid function. This sigmoid function maps any set of real-valued independent variables as input to a value ranging from 0 to 1 [48]. Additionally, extensions such as one-vs-rest enable logistic regression to handle multiclass classification problems.…”

Section: Classificationmentioning

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: Classificationmentioning

confidence: 99%