Advancements in Imaging Sensors and AI for Plant Stress Detection: A Systematic Literature Review.

IF 7.6 1区 农林科学 Q1 AGRONOMY Plant Phenomics Pub Date : 2023-03-01 eCollection Date: 2024-01-01 DOI:10.34133/plantphenomics.0153
Jason John Walsh, Eleni Mangina, Sonia Negrão
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Abstract

Integrating imaging sensors and artificial intelligence (AI) have contributed to detecting plant stress symptoms, yet data analysis remains a key challenge. Data challenges include standardized data collection, analysis protocols, selection of imaging sensors and AI algorithms, and finally, data sharing. Here, we present a systematic literature review (SLR) scrutinizing plant imaging and AI for identifying stress responses. We performed a scoping review using specific keywords, namely abiotic and biotic stress, machine learning, plant imaging and deep learning. Next, we used programmable bots to retrieve relevant papers published since 2006. In total, 2,704 papers from 4 databases (Springer, ScienceDirect, PubMed, and Web of Science) were found, accomplished by using a second layer of keywords (e.g., hyperspectral imaging and supervised learning). To bypass the limitations of search engines, we selected OneSearch to unify keywords. We carefully reviewed 262 studies, summarizing key trends in AI algorithms and imaging sensors. We demonstrated that the increased availability of open-source imaging repositories such as PlantVillage or Kaggle has strongly contributed to a widespread shift to deep learning, requiring large datasets to train in stress symptom interpretation. Our review presents current trends in AI-applied algorithms to develop effective methods for plant stress detection using image-based phenotyping. For example, regression algorithms have seen substantial use since 2021. Ultimately, we offer an overview of the course ahead for AI and imaging technologies to predict stress responses. Altogether, this SLR highlights the potential of AI imaging in both biotic and abiotic stress detection to overcome challenges in plant data analysis.

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用于植物胁迫检测的成像传感器和人工智能的进步:系统性文献综述。
成像传感器与人工智能(AI)的结合为检测植物胁迫症状做出了贡献,但数据分析仍是一项关键挑战。数据挑战包括标准化数据收集、分析协议、成像传感器和人工智能算法的选择,以及最后的数据共享。在此,我们提交了一份系统性文献综述(SLR),仔细研究了用于识别胁迫反应的植物成像和人工智能。我们使用特定的关键词(即非生物和生物胁迫、机器学习、植物成像和深度学习)进行了范围审查。接下来,我们使用可编程机器人检索了自 2006 年以来发表的相关论文。通过使用第二层关键词(如高光谱成像和监督学习),我们总共从 4 个数据库(Springer、ScienceDirect、PubMed 和 Web of Science)中找到了 2704 篇论文。为了绕过搜索引擎的限制,我们选择了 OneSearch 来统一关键词。我们仔细查阅了 262 项研究,总结了人工智能算法和成像传感器的主要趋势。我们表明,PlantVillage 或 Kaggle 等开源成像库的可用性增加,有力地推动了向深度学习的广泛转变,这需要大量数据集来训练应激症状解释。我们的综述介绍了当前人工智能应用算法的发展趋势,以开发利用基于图像的表型技术进行植物胁迫检测的有效方法。例如,回归算法自 2021 年以来得到了大量应用。最后,我们概述了人工智能和成像技术在预测胁迫反应方面的未来发展方向。总之,本 SLR 强调了人工智能成像在生物和非生物胁迫检测方面的潜力,以克服植物数据分析方面的挑战。
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来源期刊
Plant Phenomics
Plant Phenomics Multiple-
CiteScore
8.60
自引率
9.20%
发文量
26
审稿时长
14 weeks
期刊介绍: Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics. The scope of the journal covers the latest technologies in plant phenotyping for data acquisition, data management, data interpretation, modeling, and their practical applications for crop cultivation, plant breeding, forestry, horticulture, ecology, and other plant-related domains.
期刊最新文献
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