Recent advances in plant disease severity assessment using convolutional neural networks

Shi, Tingting; Liu, Yongmin; Zheng, Xinying; Hu, Kui; Hao, Huang; Liu, Hanlin; Huang, Hongxu

doi:10.1038/s41598-023-29230-7

Cited by 40 publications

(14 citation statements)

References 76 publications

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“…Shi et al ( 2023 ) reported that there are relatively few studies on disease severity assessment; thus, their aim is to tracing prevailing views of existing studies in order to provide its grading criteria. They also addressed the main obstacles that CNN-based plant disease severity assessment systems confront in practical applications and gave plausible research ideas and potential ways to address them.…”

Section: Review Of Related Workmentioning

confidence: 99%

Revolutionizing crop disease detection with computational deep learning: a comprehensive review

Ngugi,

Ezugwu,

Akinyelu

et al. 2024

Environ Monit Assess

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Digital image processing has witnessed a significant transformation, owing to the adoption of deep learning (DL) algorithms, which have proven to be vastly superior to conventional methods for crop detection. These DL algorithms have recently found successful applications across various domains, translating input data, such as images of afflicted plants, into valuable insights, like the identification of specific crop diseases. This innovation has spurred the development of cutting-edge techniques for early detection and diagnosis of crop diseases, leveraging tools such as convolutional neural networks (CNN), K-nearest neighbour (KNN), support vector machines (SVM), and artificial neural networks (ANN). This paper offers an all-encompassing exploration of the contemporary literature on methods for diagnosing, categorizing, and gauging the severity of crop diseases. The review examines the performance analysis of the latest machine learning (ML) and DL techniques outlined in these studies. It also scrutinizes the methodologies and datasets and outlines the prevalent recommendations and identified gaps within different research investigations. As a conclusion, the review offers insights into potential solutions and outlines the direction for future research in this field. The review underscores that while most studies have concentrated on traditional ML algorithms and CNN, there has been a noticeable dearth of focus on emerging DL algorithms like capsule neural networks and vision transformers. Furthermore, it sheds light on the fact that several datasets employed for training and evaluating DL models have been tailored to suit specific crop types, emphasizing the pressing need for a comprehensive and expansive image dataset encompassing a wider array of crop varieties. Moreover, the survey draws attention to the prevailing trend where the majority of research endeavours have concentrated on individual plant diseases, ML, or DL algorithms. In light of this, it advocates for the development of a unified framework that harnesses an ensemble of ML and DL algorithms to address the complexities of multiple plant diseases effectively.

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Section: Review Of Related Workmentioning

confidence: 99%

Revolutionizing crop disease detection with computational deep learning: a comprehensive review

Ngugi,

Ezugwu,

Akinyelu

et al. 2024

Environ Monit Assess

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“…Viral proteins involved in replication can phase separate to drive the formation of viral compartments [44]. α-Crystallin can form condensates through LLPS upon stimulation by several risk factors, such as ageing and diabetes, contributing to cataracts [45].…”

Section: Aberrant Llps May Be Involved In Disease Pathologymentioning

confidence: 99%

The Role of Liquid‒Liquid Phase Separation in the Accumulation of Pathological Proteins: New Perspectives on the Mechanism of Neurodegenerative Diseases

Lu,

et al. 2024

Aging and disease

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It is widely accepted that living organisms form highly dynamic membrane-less organelles (MLOS) with various functions through phase separation, and the indispensable role that phase separation plays in the mechanisms of normal physiological functions and pathogenesis is gradually becoming clearer. Pathological aggregates, regarded as hallmarks of neurodegenerative diseases, have been revealed to be closely related to aberrant phase separation. Specific proteins are assembled into condensates and transform into insoluble inclusions through aberrant phase separation, contributing to the development of diseases. In this review, we present an overview of the progress of phase separation research, involving its biological mechanisms and the status of research in neurodegenerative diseases, focusing on five main disease-specific proteins, tau, TDP-43, FUS, α-Syn and HTT, and how exactly these proteins reside within dynamic liquid-like compartments and thus turn into solid deposits. Further studies will yield new perspectives for understanding the aggregation mechanisms and potential therapeutic strategies, and future research directions are anticipated.

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“…It is indicated that Leaf Doctor is one program that, in subsequent upgrades, will employ the suggested methodology. Shi et al [39] outlined different studies on convolution neural network (CNN) based plant disease severity assessment in terms of classical CNN frameworks, improved CNN architectures, and CNN based segmentation networks, depending on the network architecture. The study also provided a detailed comparative analysis of the advantages and disadvantages of each approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Leaf disease severity classification with explainable artificial intelligence using transformer networks

2023

IJATEE

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The agricultural sector is considered India's most crucial sector. In India, farming is the most common occupation and a significant source of revenue [1]. India is second in the world in terms of population, with 70% of its people living in villages and relying primarily on agriculture for their subsistence. Farmers cultivate a wide variety of crops based on numerous factors such as crop conditions, the environment, soil conditions, local farming practices, new variants of pathogens and various illnesses, etc. *Author for correspondence Presently farmers are incurring losses due to changes in climatic conditions, plant disease is one of the main challenges to food security since it significantly lowers crop quality and output [2].Therefore, it has been difficult to diagnose diseases correctly and discover them early. A variety of methods, including soil management, crop rotation, irrigation, genetic modification, harvesting methods, precision farming, weed control, and pest and disease control, are used to boost the yield. So, it is crucial to identify pesticides and diseases early if you want to boost a crop's production. The traditional method of identifying plant diseases is by visual inspection [3]. This can result in incorrect disease identification, and

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Recent advances in plant disease severity assessment using convolutional neural networks

Cited by 40 publications

References 76 publications

Revolutionizing crop disease detection with computational deep learning: a comprehensive review

Revolutionizing crop disease detection with computational deep learning: a comprehensive review

The Role of Liquid‒Liquid Phase Separation in the Accumulation of Pathological Proteins: New Perspectives on the Mechanism of Neurodegenerative Diseases

Leaf disease severity classification with explainable artificial intelligence using transformer networks

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