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Machine Learning for Leaf Disease Classification: Data, Techniques and Applications
Computer Vision and Pattern Recognition · Submitted 19 Oct 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The growing demand for sustainable development brings a series of information technologies to help agriculture production. | background 1.00 | 0.03 | 0.02 | asserted 1.00 |
| s1 | Especially, the emergence of machine learning applications, a branch of artificial intelligence, has shown multiple breakthroughs which can enhance and revolutionize plant pathology approaches. | background 1.00 | 0.04 | 0.05 | asserted 1.00 |
| s2 | In recent years, machine learning has been adopted for leaf disease classification in both academic research and industrial applications. | background 1.00 | 0.05 | 0.04 | asserted 1.00 |
| s3 | 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. | background 0.66 | 0.05 | 0.11 | asserted 1.00 |
| s4 | This study will provide a survey in different aspects of the topic including data, techniques, and applications. | contribution 0.99 | 0.59 | 0.07 | asserted 0.99 |
| s5 | The paper will start with publicly available datasets. | method 0.70 | 0.25 | 0.02 | asserted 0.93 |
| s6 | After that, we summarize common machine learning techniques, including traditional (shallow) learning, deep learning, and augmented learning. | contribution 0.66 | 0.61 | 0.03 | asserted 0.96 |
| s7 | Finally, we discuss related applications. | contribution 0.64 | 0.48 | 0.03 | asserted 0.74 |
| s8 | 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. | future work 0.50 | 0.22 | 0.46 | hedged possibility 0.75 |
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Original abstract
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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