Editorial: AI, sensors and robotics in plant phenotyping and precision agriculture, volume II

Su, Daobilige; Qiao, Yongliang; Yu, Jiang; Valente, João; Zhang, Zhao; He, Dongjian

doi:10.3389/fpls.2023.1215899

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…Advanced techniques such as imaging technologies, sensor-based measurements, and high-throughput screening platforms are increasingly being incorporated into plant phenotyping, paving the way towards more efficient and precise data collection (Figure 1 and Table 1). However, these techniques invariably generate vast and complex datasets, necessitating robust data analysis strategies [21][22][23]. Multi-omics methodologies promise a holistic view of the plant system by integrating genomic, transcriptomic, proteomic, and metabolomic data, among others.…”

Section: Need For Advanced Techniques In Plant Phenotypingmentioning

confidence: 99%

An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture

Cembrowska-Lech,

Krzemińska,

Miller

et al. 2023

Biology

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This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.

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Section: Need For Advanced Techniques In Plant Phenotypingmentioning

confidence: 99%

An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture

Cembrowska-Lech,

Krzemińska,

Miller

et al. 2023

Biology

View full text Add to dashboard Cite

show abstract

Development of plant phenotyping system using Pan Tilt Zoom camera and verification of its validity

Pham,

Amin,

Yasutake

et al. 2024

Computers and Electronics in Agriculture

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Algorithm for UAV path planning in high obstacle density environments: RFA-star

Zhang,

Li,

et al. 2024

Front. Plant Sci.

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Path planning is one of the key elements for achieving rapid and stable flight when unmanned aerial vehicles (UAVs) are conducting monitoring and inspection tasks at ultra-low altitudes or in orchard environments. It involves finding the optimal and safe route between a given starting point and a target point. Achieving rapid and stable flight in complex environments is paramount. In environments characterized by high-density obstacles, the stability of UAVs remains a focal point in the research of path planning algorithms. This study, utilizing a feature attention mechanism, systematically identifies distinctive points on the obstacles, leading to the development of the RFA-Star (R5DOS Feature Attention A-star) path planning algorithm. In MATLAB, random maps were generated to assess the performance of the RFA-Star algorithm. The analysis focused on evaluating the effectiveness of the RFA-Star algorithm under varying obstacle density conditions and different map sizes. Additionally, comparative analyses juxtaposed the performance of the RFA-Star algorithm against three other algorithms. Experimental results indicate that the RFA-Star algorithm demonstrates the shortest computation time, approximately 84%-94% faster than the RJA-Star algorithm and 51%-96% faster than the Improved A-Star. The flight distance is comparable to the RJA-Star algorithm, with slightly more searched nodes. Considering these factors collectively, the RFA-Star algorithm exhibits a relatively superior balance between computational efficiency and path quality. It consistently demonstrates efficient and stable performance across diverse complex environments. However, for comprehensive performance enhancement, further optimization is necessary.

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Editorial: AI, sensors and robotics in plant phenotyping and precision agriculture, volume II

Cited by 4 publications

References 15 publications

An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture

An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture

Development of plant phenotyping system using Pan Tilt Zoom camera and verification of its validity

Algorithm for UAV path planning in high obstacle density environments: RFA-star

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