An intelligent approach using boosted support vector machine based arithmetic optimization algorithm for accurate detection of plant leaf disease

Prabu, M.; Chelliah, Balika J.

doi:10.1007/s10044-022-01086-z

Cited by 18 publications

(7 citation statements)

References 23 publications

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“…By analyzing various parameters such as leaf shape, color, texture, and size, these algorithms can identify the presence of a specific disease [71]. The ML models can learn to differentiate between diseases that display similar symptoms by training on a dataset of tomato leaves with known diseases [83].…”

Section: Discussion On Challenges and Trendsmentioning

confidence: 99%

Transformative Role of Artificial Intelligence in Advancing Sustainable Tomato (Solanum lycopersicum) Disease Management for Global Food Security: A Comprehensive Review

Sundararaman,

Jagdev,

Khatri

2023

Sustainability

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The growing global population and accompanying increase in food demand has put pressure on agriculture to produce higher yields in the face of numerous challenges, including plant diseases. Tomato is a widely cultivated and essential food crop that is particularly susceptible to disease, resulting in significant economic losses and hindrances to food security. Recently, Artificial Intelligence (AI) has emerged as a promising tool for detecting and classifying tomato leaf diseases with exceptional accuracy and efficiency, empowering farmers to take proactive measures to prevent crop damage and production loss. AI algorithms are capable of processing vast amounts of data objectively and without human bias, making them a potent tool for detecting even subtle variations in plant diseases that traditional techniques might miss. This paper provides a comprehensive overview of the most recent advancements in tomato leaf disease classification using Machine Learning (ML) and Deep Learning (DL) techniques, with an emphasis on how these approaches can enhance the accuracy and effectiveness of disease classification. Several ML and DL models, including convolutional neural networks (CNN), are evaluated for tomato leaf disease classification. This review paper highlights the various features and techniques used in data acquisition as well as evaluation metrics employed to assess the performance of these models. Moreover, this paper emphasizes how AI techniques can address the limitations of traditional techniques in tomato leaf disease classification, leading to improved crop yields and more efficient management techniques, ultimately contributing to global food security. This review paper concludes by outlining the limitations of recent research and proposing new research directions in the field of AI-assisted tomato leaf disease classification. These insights will be of significant value to researchers and professionals interested in utilizing ML and DL techniques for tomato leaf disease classification and ultimately contribute to sustainable food production (SDG-3).

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Section: Discussion On Challenges and Trendsmentioning

confidence: 99%

Transformative Role of Artificial Intelligence in Advancing Sustainable Tomato (Solanum lycopersicum) Disease Management for Global Food Security: A Comprehensive Review

Sundararaman,

Jagdev,

Khatri

2023

Sustainability

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“…The algorithm's most basic form yields a single intensity level that separates pixels into the background and foreground classes [30,33].…”

Section: Otsu Thresholdmentioning

confidence: 99%

Early detection and control of anthracnose disease in cashew leaves to improve crop yield using image processing and machine learning techniques

Sudha¹,

Kumaran²

2023

SIViP

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Agriculture is one of the primary pillars powering India's economy. It is alarming to note that India's agriculture rate is declining steeply. Climate change, environmental pollution, and soil erosion are well-known factors affecting crop productivity. The increasing prevalence of plant diseases is also a signi cant contributing factor affecting agriculture.Early disease detection and mitigation actions based on identi ed diseases in the plants are critical in increasing crop productivity. This study considers a machine-learning model for detecting disease in cashew leaves. This work concentrates on Anthracnose disease, which leads to severe yield loss when it affects the cashew plant. In this regard, cashew leaves are collected and used to train various machine learning classi ers to identify and classify the disease. This work focuses on the segmentation and classi cation of leaves using various Machine Learning models. For this,

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“…For precisely identifying plant leaf disease, the Boosted support vector machine-based Arithmetic optimization algorithm (BSVM-AOA) has been presented [18]. In this instance, the greyscale co-occurrence matrix is employed for feature extraction, and the vector value active contour model is used for picture segmentation.…”

Section: Related Workmentioning

confidence: 99%

A Neural Network-based Approach for Apple Leaf Diseases Detection in Smart Agriculture Application

Gan,

Zhou,

Cui

et al. 2023

IJACSA

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Plant diseases significantly harm agriculture, which has an impact on nations' economies and levels of food security. Early plant disease detection is essential in smart agriculture. For the diagnosis of plant diseases, a number of methods, including imaging, have been used recently. Some of the existing methods for plant disease detection using imaging have limitations as firstly, high computational cost, some methods require complex image processing algorithms or manual design of features that can increase the time and resources needed for the detection. Secondly, low accuracy, most of the methods rely on simple classifiers or handcrafted features that may not capture the subtle differences between different diseases or healthy leaves. Thirdly, dependency on expert knowledge, some methods need human intervention or prior knowledge of the diseases and pests to perform the detection. These limitations are not suitable for the problem at hand because they can affect the efficiency of the detection system. In this study, three apple tree leaf diseases-apple black spot, Alternaria, and Minoz blightare detected using a neural network (NN) and a digital image processing technique. The sample images are prepared, processed, and used to extract attributes using a digital image processing approach, and the NN is used to classify the diseases. An evaluation of the proposed system's performance in identifying illnesses in apple trees shows satisfactory accuracy and strong overall performance. Additionally, when compared to other techniques already in use, this strategy is more effective at diagnosing.

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An intelligent approach using boosted support vector machine based arithmetic optimization algorithm for accurate detection of plant leaf disease

Cited by 18 publications

References 23 publications

Transformative Role of Artificial Intelligence in Advancing Sustainable Tomato (Solanum lycopersicum) Disease Management for Global Food Security: A Comprehensive Review

Transformative Role of Artificial Intelligence in Advancing Sustainable Tomato (Solanum lycopersicum) Disease Management for Global Food Security: A Comprehensive Review

Early detection and control of anthracnose disease in cashew leaves to improve crop yield using image processing and machine learning techniques

A Neural Network-based Approach for Apple Leaf Diseases Detection in Smart Agriculture Application

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