Plant Diseases Recognition Based on Image Processing Technology

Sun, Greg; Jia, Xinglong; Geng, Tianyu

doi:10.1155/2018/6070129

Cited by 73 publications

(21 citation statements)

References 9 publications

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“…Once the identification of the infective agent is completed, the system provides instant diagnosis, feedback and solutions with preventative actions to farmers. Various AI-based recognition systems have been studied and developed to identity and classify weeds [71,72], insect pests [73] and crop diseases [74][75][76][77].…”

Section: Crop Monitoringmentioning

confidence: 99%

Characterising the Agriculture 4.0 Landscape—Emerging Trends, Challenges and Opportunities

et al. 2021

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Investment in technological research is imperative to stimulate the development of sustainable solutions for the agricultural sector. Advances in Internet of Things, sensors and sensor networks, robotics, artificial intelligence, big data, cloud computing, etc. foster the transition towards the Agriculture 4.0 era. This fourth revolution is currently seen as a possible solution for improving agricultural growth, ensuring the future needs of the global population in a fair, resilient and sustainable way. In this context, this article aims at characterising the current Agriculture 4.0 landscape. Emerging trends were compiled using a semi-automated process by analysing relevant scientific publications published in the past ten years. Subsequently, a literature review focusing these trends was conducted, with a particular emphasis on their applications in real environments. From the results of the study, some challenges are discussed, as well as opportunities for future research. Finally, a high-level cloud-based IoT architecture is presented, serving as foundation for designing future smart agricultural systems. It is expected that this work will positively impact the research around Agriculture 4.0 systems, providing a clear characterisation of the concept along with guidelines to assist the actors in a successful transition towards the digitalisation of the sector.

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Section: Crop Monitoringmentioning

confidence: 99%

Characterising the Agriculture 4.0 Landscape—Emerging Trends, Challenges and Opportunities

et al. 2021

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“…There are some digital imagery softwares to combine multiple high-resolution images into a single, in-focus specimen image. Various image processing techniques have been used for identification of pathogens (Barbedo, 2016;Sun et al, 2018), insects (Keszthely et al, 2020) and weeds (Lin et al, 2017).…”

Section: Imaging Devicesmentioning

confidence: 99%

Applications of Biotechnology for Characterization of Plants and Pests as the Key Components of Plant Protection and Production Strategies: A Review

Alemu

2020

Int J Appl Sci Biotechnol

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The second Sustainable Developmental Goals (SDG), among the seventeen SDG, is concerned with the pursuit of global food security and agricultural sustainability, which become the key to the success of the entire SDG. Whereas agricultural production and productivity are heavily threatened by the incidence of pests that inflict huge losses in various forms. This calls for prompt applications of biotechnology for the fast, accurate and reliable means for characterization of plant generic resources and pests as it is the pre-requisite and gateway for designing appropriate plant protection and production strategies. It is imperative that pests be identified properly so that judicious use of the literature can be made and sustainable management strategies can be implemented at the right stage. To this end, the application of biotechnology has made significant advances for reliable characterization of plant genetic resources as well as accurate diagnosis of pests, study of their genetic diversity and variability, detailed mechanisms of their transmission and evolutionary relationships. Accordingly, this review article covers the comprehensive account of the various molecular techniques, genome mapping and OMICS sciences utilized for characterization plants and pests that ultimately allow the detailed study of the biology and epidemiology of pests at any stage of their life cycle. The resulting data are eventually employed for enhancing successful implementation of sustainable plant protection and production strategies. In conclusion, the increasing projections of transboundary pests, environmental and abiotic factors together with the continuous scientific advancements and breakthroughs have made biotechnology to be an important engine of bioeconomy for generating invaluable products, processes and services. Int. J. Appl. Sci. Biotechnol. Vol 8(3): 247-288

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“…Detailed surveys of established image processing techniques used for automated detection and classification of lesions have been reported [18,21,[24][25][26]. Considering damage, those approaches employ an array of lesion segmentation and classification techniques such as thresholding [27,28], edge detection [29,30], watershed [31], fuzzy c-means [32], superpixel clustering [33], color transformation [17], pixel classification [22], improved histogram segmentation method [34], and genetic algorithms [14]. Popularly used classification techniques for plant lesion identification are K-means [35], K-nearest neighbor [36], Artificial Neural Networks [37,38], Support Vector Machine [39][40][41], and Deep Learning [42][43][44][45][46][47] as a new standard in digital image analysis.…”

Section: Introductionmentioning

confidence: 99%

High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis

et al. 2020

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Background: Field-grown leafy vegetables can be damaged by biotic and abiotic factors, or mechanically damaged by farming practices. Available methods to evaluate leaf tissue damage mainly rely on colour differentiation between healthy and damaged tissues. Alternatively, sophisticated equipment such as microscopy and hyperspectral cameras can be employed. Depending on the causal factor, colour change in the wounded area is not always induced and, by the time symptoms become visible, a plant can already be severely affected. To accurately detect and quantify damage on leaf scale, including microlesions, reliable differentiation between healthy and damaged tissue is essential. We stained whole leaves with trypan blue dye, which traverses compromised cell membranes but is not absorbed in viable cells, followed by automated quantification of damage on leaf scale. Results: We present a robust, fast and sensitive method for leaf-scale visualisation, accurate automated extraction and measurement of damaged area on leaves of leafy vegetables. The image analysis pipeline we developed automatically identifies leaf area and individual stained (lesion) areas down to cell level. As proof of principle, we tested the methodology for damage detection and quantification on two field-grown leafy vegetable species, spinach and Swiss chard. Conclusions: Our novel lesion quantification method can be used for detection of large (macro) or single-cell (micro) lesions on leaf scale, enabling quantification of lesions at any stage and without requiring symptoms to be in the visible spectrum. Quantifying the wounded area on leaf scale is necessary for generating prediction models for economic losses and produce shelf-life. In addition, risk assessments are based on accurate prediction of the relationship between leaf damage and infection rates by opportunistic pathogens and our method helps determine the severity of leaf damage at fine resolution.

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Plant Diseases Recognition Based on Image Processing Technology

Cited by 73 publications

References 9 publications

Characterising the Agriculture 4.0 Landscape—Emerging Trends, Challenges and Opportunities

Characterising the Agriculture 4.0 Landscape—Emerging Trends, Challenges and Opportunities

Applications of Biotechnology for Characterization of Plants and Pests as the Key Components of Plant Protection and Production Strategies: A Review

High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis

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