Detection of the viral disease on the potato foliar and tubers using a machine learning approach

Raigonda, Megha Rani; Terdal, Sujatha P.; Raigond, Baswaraj

doi:10.53730/ijhs.v6ns4.10740

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…In recent years, there has been a growing emphasis on the use of machine learning for leaf disease detection. M. R. Raigonda et al [31], implemented a preprocessing and image segmentation approach to accurately identify leaf diseases in potato plants. Image sharpening through contrast enhancement is focused initially, and denoising techniques using median and Gaussian filters are applied at a later stage.…”

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%

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

Kiran,

Chandrappa

2023

Journal of Robotics and Control

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“…In [10] By adopting the new technology and continuous monitoring, the diseases can be identified at the initial stage that can be avoided and limit the yield loss to a greater extent.…”

Section: International Journal Of Engineering Research In Computer Sc...mentioning

confidence: 99%

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2022

IJERCSE

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The symptoms of asthma can alter. There is no existing cure for asthma. Asthma affects more than 25 million Americans today. This number includes more than a million children. Patients with asthma risk dying if they don't obtain therapy. A long-term symptom of asthma is a sudden worsening of symptoms that can be unpredictable and even fatal. In this work, we use supervised machine learning and the Random Forest method to achieve better outcomes in the self-management of asthma. Machine learning typically uses this random forest approach for classification and regression problems. In our research, we found that both probabilistic and discriminant classifiers were capable of producing outstanding accuracy (AUC>0.87) for early warning.

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Detection of the viral disease on the potato foliar and tubers using a machine learning approach

Cited by 2 publications

References 17 publications

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

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

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