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Salman Z, Muhammad A, Piran MJ, Han D. Crop-saving with AI: latest trends in deep learning techniques for plant pathology. FRONTIERS IN PLANT SCIENCE 2023; 14:1224709. [PMID: 37600194 PMCID: PMC10433211 DOI: 10.3389/fpls.2023.1224709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 06/12/2023] [Indexed: 08/22/2023]
Abstract
Plant diseases pose a major threat to agricultural production and the food supply chain, as they expose plants to potentially disruptive pathogens that can affect the lives of those who are associated with it. Deep learning has been applied in a range of fields such as object detection, autonomous vehicles, fraud detection etc. Several researchers have tried to implement deep learning techniques in precision agriculture. However, there are pros and cons to the approaches they have opted for disease detection and identification. In this survey, we have made an attempt to capture the significant advancements in machine-learning based disease detection. We have discussed prevalent datasets and techniques that have been employed as well as highlighted emerging approaches being used for plant disease detection. By exploring these advancements, we aim to present a comprehensive overview of the prominent approaches in precision agriculture, along with their associated challenges and potential improvements. This paper delves into the challenges associated with the implementation and briefly discusses the future trends. Overall, this paper presents a bird's eye view of plant disease datasets, deep learning techniques, their accuracies and the challenges associated with them. Our insights will serve as a valuable resource for researchers and practitioners in the field. We hope that this survey will inform and inspire future research efforts, ultimately leading to improved precision agriculture practices and enhanced crop health management.
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Affiliation(s)
| | | | | | - Dongil Han
- Department of Computer Science and Engineering, Sejong University, Seoul, Republic of Korea
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Zhang J, Feng X, Wu Q, Yang G, Tao M, Yang Y, He Y. Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning. PLANT METHODS 2022; 18:49. [PMID: 35428329 PMCID: PMC9013134 DOI: 10.1186/s13007-022-00882-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 03/31/2022] [Indexed: 05/10/2023]
Abstract
BACKGROUND Rice bacterial blight (BB) has caused serious damage in rice yield and quality leading to huge economic loss and food safety problems. Breeding disease resistant cultivar becomes the eco-friendliest and most effective alternative to regulate its outburst, since the propagation of pathogenic bacteria is restrained. However, the BB resistance cultivar selection suffers tremendous labor cost, low efficiency, and subjective human error. And dynamic rice BB phenotyping study is absent from exploring the pattern of BB growth with different genotypes. RESULTS In this paper, with the aim of alleviating the labor burden of plant breeding experts in the resistant cultivar screening processing and exploring the disease resistance phenotyping variation pattern, visible/near-infrared (VIS-NIR) hyperspectral images of rice leaves from three varieties after inoculation were collected and sent into a self-built deep learning model LPnet for disease severity assessment. The growth status of BB lesion at the time scale was fully revealed. On the strength of the attention mechanism inside LPnet, the most informative spectral features related to lesion proportion were further extracted and combined into a novel and refined leaf spectral index. The effectiveness and feasibility of the proposed wavelength combination were verified by identifying the resistant cultivar, assessing the resistant ability, and spectral image visualization. CONCLUSIONS This study illustrated that informative VIS-NIR spectrums coupled with attention deep learning had great potential to not only directly assess disease severity but also excavate spectral characteristics for rapid screening disease resistant cultivars in high-throughput phenotyping.
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Affiliation(s)
- Jinnuo Zhang
- College of Biosystems Engineering and Food Science, Key Laboratory of Spectroscopy, Ministry of Agriculture and Rural Affairs, Zhejiang University, Hangzhou, 310058, China
| | - Xuping Feng
- College of Biosystems Engineering and Food Science, Key Laboratory of Spectroscopy, Ministry of Agriculture and Rural Affairs, Zhejiang University, Hangzhou, 310058, China
| | - Qingguan Wu
- College of Biosystems Engineering and Food Science, Key Laboratory of Spectroscopy, Ministry of Agriculture and Rural Affairs, Zhejiang University, Hangzhou, 310058, China
| | - Guofeng Yang
- College of Biosystems Engineering and Food Science, Key Laboratory of Spectroscopy, Ministry of Agriculture and Rural Affairs, Zhejiang University, Hangzhou, 310058, China
| | - Mingzhu Tao
- College of Biosystems Engineering and Food Science, Key Laboratory of Spectroscopy, Ministry of Agriculture and Rural Affairs, Zhejiang University, Hangzhou, 310058, China
| | - Yong Yang
- State Key Laboratory for Managing Biotic and Chemical Treats to the Quality and Safety of Agro-Products, Key Laboratory of Biotechnology for Plant Protection, Ministry of Agriculture, and Rural Affairs, Zhejiang Provincial Key Laboratory of Biotechnology for Plant Protection, Institute of Virology and Biotechnology, Zhejiang Academy of Agricultural Science, Hangzhou, 310021, China.
| | - Yong He
- College of Biosystems Engineering and Food Science, Key Laboratory of Spectroscopy, Ministry of Agriculture and Rural Affairs, Zhejiang University, Hangzhou, 310058, China.
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