Plant Leaf Disease Classification using Deep Learning: A Survey

Agarwal, Deeksha; Chawla, Meenu; Tiwari, Namita

doi:10.1109/icirca51532.2021.9544640

Cited by 7 publications

(1 citation statement)

References 23 publications

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“…where true positive (TP) is the model predicts the correct sample as correct; true negative (TN) is the model predicts the incorrect sample as incorrect; false positive (FP) is the model predicts the incorrect sample as correct; and false negative (FN) is the model predicts the correct sample as incorrect. The class-wise TP, TN, FP, and FN are determined by using ( 5) to (8):…”

Section: Resultsmentioning

confidence: 99%

Citrus leaf disease detection through deep learning approach

Islam,

Chakrabarty,

Uddin

2024

Bulletin EEI

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The majority of people in the world directly or indirectly depend on agriculture. Plant diseases are a significant threat to agricultural production and food security. Due to its high nutritional value, citrus fruit is one of the most abundant fruits in the world. However, different diseases are responsible for degraded citrus production as well as financial losses to the farmers. Traditionally, visual observation by experts has been attended to diagnose plant diseases. Usually, plant leaf disease recognition methods mainly rely on expert experiences to manually extract the colour, composition, and other features of diseased leaf images. Black spot, greening, canker, and melanoses are four common citrus leaf diseases. Rapid and accurate diagnosis of these diseases is a demand of time. Deep learning is a promising solution to these problems. There are different types of deep learning architecture like ImageNet, GoogleNet, VGG16, ResNet50, and InceptionV3, which show promising results in different object detection. Though most of these benchmark models give almost similar accuracy. However, this paper uses two deep learning models to find the better ones for the detection of citrus leaf disease detection. Hence, InceptionV3 outperforms VGG16 in terms of accuracy.

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Section: Resultsmentioning

confidence: 99%

Citrus leaf disease detection through deep learning approach

Islam,

Chakrabarty,

Uddin

2024

Bulletin EEI

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Unsupervised deep metric learning algorithm for crop disease images based on knowledge distillation networks

Zeng,

Li,

Wang

et al. 2024

Multimedia Systems

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Machine learning for leaf disease classification: data, techniques and applications

Yao,

Tran,

Sawyer

et al. 2023

Artif Intell Rev

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The growing demand for sustainable development brings a series of information technologies to help agriculture production. Especially, the emergence of machine learning applications, a branch of artificial intelligence, has shown multiple breakthroughs which can enhance and revolutionize plant pathology approaches. In recent years, machine learning has been adopted for leaf disease classification in both academic research and industrial applications. Therefore, it is enormously beneficial for researchers, engineers, managers, and entrepreneurs to have a comprehensive view about the recent development of machine learning technologies and applications for leaf disease detection. This study will provide a survey in different aspects of the topic including data, techniques, and applications. The paper will start with publicly available datasets. After that, we summarize common machine learning techniques, including traditional (shallow) learning, deep learning, and augmented learning. Finally, we discuss related applications. This paper would provide useful resources for future study and application of machine learning for smart agriculture in general and leaf disease classification in particular.

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Plant Leaf Disease Classification using Deep Learning: A Survey

Cited by 7 publications

References 23 publications

Citrus leaf disease detection through deep learning approach

Citrus leaf disease detection through deep learning approach

Unsupervised deep metric learning algorithm for crop disease images based on knowledge distillation networks

Machine learning for leaf disease classification: data, techniques and applications

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