Plant Disease Identification Using Transfer Learning

Arshad, Muhammad; Rehman, Usman Abdur; Fraz, Muhammad Moazam

doi:10.1109/icodt252288.2021.9441512

Cited by 30 publications

(10 citation statements)

References 22 publications

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“…As per this review work, CNN is the state-of-the-art algorithm [ 3 , 65 ] which is used in numerous problems and major competitions. Unlike other traditional machine learning algorithms, CNN automatically extracts features and classifies them.…”

Section: Discussionmentioning

confidence: 99%

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Applications of Computer Vision on Automatic Potato Plant Disease Detection: A Systematic Literature Review

Sinshaw

Assefa

Mohapatra

et al. 2022

Computational Intelligence and Neuroscience

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In most developing countries, the contribution of agriculture to gross domestic product is significant. Plant disease is one of the major factors that adversely affect crop yield. Traditional plant disease detection techniques are time-consuming, biased, and ineffective. Potato is among the top consumed plants in the world, in general, and in developing countries, in particular. However, potato is affected by different kinds of diseases which minimize their yield and quantity. The advancement in AI and machine learning has paved the way for new methods of tackling plant disease detection. This study presents a comprehensive systematic literature review on the major diseases that harm potato crops. In this effort, computer vision-based techniques are employed to identify potato diseases, and types of machine learning algorithms used are surveyed. In this review, 39 primary studies that have provided useful information about the research questions are chosen. Accordingly, the most common potato diseases are found to be late blight, early blight, and bacterial wilt. Furthermore, the review discovered that deep learning algorithms were more frequently used to detect crop diseases than classical machine learning algorithms. Finally, the review categorized the state-of-the-art algorithms and identifies open research problems in the area.

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Section: Discussionmentioning

confidence: 99%

“…CV can be used in a variety of applications [3]. Te following are the most common CV applications in agriculture: sorting and grading of fruits and vegetables [1,4,5], plant disease detection [6][7][8][9], plant disease classifcation [10][11][12], and quality inspection of fruits and vegetables [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Applications of Computer Vision on Automatic Potato Plant Disease Detection: A Systematic Literature Review

Sinshaw

Assefa

Mohapatra

et al. 2022

Computational Intelligence and Neuroscience

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“…Transfer learning aims to solve problems in the target domain using the knowledge that has been learned in the relevant domain. Many representative transfer learning methods have been put into practical applications, covering areas including text classification [1][2][3][4][5], medicine [6][7][8][9], transportation [10][11][12], recommendation systems [13][14][15][16], etc. Previous studies have shown that the model trained using the original features directly for cross-domain text classification is not ideal.…”

Section: Abbreviationsmentioning

confidence: 99%

Dual-Space Transfer Learning Based on an Indirect Mutual Promotion Strategy

Cui

Pan

et al. 2022

Int J Comput Intell Syst

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Transfer learning is designed to leverage knowledge in the source domain with labels to help build classification models in the target domain where labels are scarce or even unavailable. Previous studies have shown that high-level concepts extracted from original features are more suitable for cross-domain classification tasks, so many transfer learning methods transfer knowledge by modeling high-level concepts on the original feature space. However, there are two limitations to this method: First, learning high-level concepts directly on the original feature space will reduce the proportion of shared information contained in common features in the process of knowledge transfer bridge construction. Second, only learning multiple high-level concepts on the original feature space, the latent shared information contained in the domain-specific features cannot be targeted learned, so the latent shared information in the domain-specific features cannot be effectively used. To overcome these limitations, this paper proposes a novel method named Dual-Space Transfer Learning based on an Indirect Mutual Promotion Strategy (DSTL). The DSTL method is formalized as an optimization problem based on non-negative matrix tri-factorization. DSTL first extracts the common features between domains and constructs the common feature space. Then, the learning of the high-level concepts of the common feature space and the original feature space is integrated through an indirect promotion strategy, which can enhance the learning effect of common features and domain-specific features through the mutual help of the two feature spaces. The system test on benchmark data sets shows the superiority of the DSTL method.

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“…The maximum precision was 96.85%.with the 90 percent train -10% test model, while the lowest was 83.21 percent with the 10 percent train -90% test model. Arshad et al [23] demonstrated plant disease identification using ResNet50 with Transfer Learning for tomato, corn and potato. Total 16 classes can be identified of different plant diseases.…”

Section: Literature Reviewmentioning

confidence: 99%

A Survey on the State of Art Approaches for Disease Detection in Plants

Sharma

Yadav²,

Saini

2022

IJRITCC

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Agriculture is the main factor for economy and contributes to GDP. The growth of the economy of many countries is based on agriculture. As a result, the yield factor, quality and volume of agricultural products, play a critical role in economic development. Plant diseases and pests have become a major determinant of crop yields throughout the years, as such illnesses in plants offer a serious threat and impediment to higher yields or production in the agriculture industry. As a result, From the outset, it becomes the major duty to correctly monitor the plants, to detect diseases thoroughly, and to determine methods of controlling or monitoring these plant diseases pests in order to achieve a higher rate of production growth and minimal crop damage. Using machine vision, deep learning methods and tools for extracting and classifying features, It could be possible to build a reliable disease detection system. Numerous researchers have created and deployed various ways for detecting plant diseases and pests. The potential of these methods has been examined in this work.

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Plant Disease Identification Using Transfer Learning

Cited by 30 publications

References 22 publications

Applications of Computer Vision on Automatic Potato Plant Disease Detection: A Systematic Literature Review

Applications of Computer Vision on Automatic Potato Plant Disease Detection: A Systematic Literature Review

Dual-Space Transfer Learning Based on an Indirect Mutual Promotion Strategy

A Survey on the State of Art Approaches for Disease Detection in Plants

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