SP
Simon Pearson
cs.ROcs.LGdeep learningcs.AIcs.CVecon.GNprecision agricultureq-fin.ECstat.ML3D models
On Valency
published · living versionsW_whyyc35n·v1 · currentpublished
Agricultural Robotics: The Future of Robotic Agriculture
with Tom Duckett, Simon Blackmore, Bruce Grieve, Wen-Hua Chen +25
1 version
W_kh37uzm3·v1published
Preprints & journals
30 papers in the corpus · 1981–2026Field-scale robotic phenotyping of three-dimensional wheat canopy architectural traits.42459376 · Cho, Yoon J, Govindan, Velu, Lloyd, Rob et al.2026 · 1 citationFrontiers in plant science. 2026;17:1856006
Climate change impacts on tomato production in high-tech soilless greenhouses in Türki̇ye.40089667 · Kürklü, Ahmet, Pearson, Simon, Felek, Turgut2025 · 10 citationsBMC plant biology. 2025;25(1):339
Unsupervised Tomato Split Anomaly Detection using Hyperspectral Imaging and Variational Autoencoders2501.02921v2 · Mahmoud Abdulsalam, Usman Zahidi, Bradley Hurst et al.2025 · 6 citationsarXiv
Optimising robotic operation speed with edge computing over 5G networks: Insights from selective harvesting robots2407.01792v1 · Usman A. Zahidi, Arshad Khan, Tsvetan Zhivkov et al.2024 · 0 citationsarXiv
Digital Twins in Agriculture and Forestry: A Review.38793969 · Tagarakis, Aristotelis C, Benos, Lefteris, Kyriakarakos, George et al.2024 · 92 citationsSensors (Basel, Switzerland). 2024;24(10)
Human-Robot Interaction in Agriculture: A Systematic Review.37571559 · Benos, Lefteris, Moysiadis, Vasileios, Kateris, Dimitrios et al.2023 · 59 citationsSensors (Basel, Switzerland). 2023;23(15)
Tea Chrysanthemum Detection by Leveraging Generative Adversarial Networks and Edge Computing.35463441 · Qi, Chao, Gao, Junfeng, Chen, Kunjie et al.2022 · 9 citationsFrontiers in plant science. 2022;13:850606
Considering the ethical implications of digital collaboration in the Food Sector.34820642 · Jacobs, Naomi, Brewer, Steve, Craigon, Peter J et al.2021 · 17 citationsPatterns (New York, N.Y.). 2021;2(11):100335
Tea Chrysanthemum Detection under Unstructured Environments Using the TC-YOLO Model2111.02724v1 · Chao Qi, Junfeng Gao, Simon Pearson et al.2021 · 63 citationsarXiv
A trust framework for digital food systems.37118166 · Brewer, Steve, Pearson, Simon, Maull, Roger et al.2023 · 20 citationsNature food. 2021;2(8):543-545
Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN.34283083 · Gong, Liyun, Yu, Miao, Jiang, Shouyong et al.2021 · 140 citationsSensors (Basel, Switzerland). 2021;21(13)
Orchard Mapping with Deep Learning Semantic Segmentation.34072975 · Anagnostis, Athanasios, Tagarakis, Aristotelis C, Kateris, Dimitrios et al.2021 · 52 citationsSensors (Basel, Switzerland). 2021;21(11)
Responsible development of autonomous robotics in agriculture.37117726 · Rose, David Christian, Lyon, Jessica, de Boon, Auvikki et al.2023 · 137 citationsNature food. 2021;2(5):306-309
Current and Emergent Economic Impacts of Covid-19 and Brexit on UK Fresh Produce and Horticultural Businesses2101.11551v1 · Lilian Korir, Archie Drake, Martin Collison et al.2021 · 1 citationarXiv
An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth2012.04041v1 · Bashar Alhnaity, Stefanos Kollias, Georgios Leontidis et al.2020 · 46 citationsarXiv
The use of light spectrum blocking films to reduce populations of Drosophila suzukii Matsumura in fruit crops.32958797 · Fountain, Michelle T, Badiee, Amir, Hemer, Sebastian et al.2020 · 12 citationsScientific reports. 2020;10(1):15358
The impact of COVID-19 on the UK fresh food supply chain2006.00279v1 · Rebecca Mitchell, Roger Maull, Simon Pearson et al.2020 · 7 citationsarXiv
Author Correction: Mobile Real-Time Grasshopper Detection and Data Aggregation Framework.32157128 · Chudzik, Piotr, Mitchell, Arthur, Alkaseem, Mohammad et al.2020 · 1 citationScientific reports. 2020;10(1):4644
Mobile Real-Time Grasshopper Detection and Data Aggregation Framework.31980675 · Chudzik, Piotr, Mitchell, Arthur, Alkaseem, Mohammad et al.2020 · 36 citationsScientific reports. 2020;10(1):1150
Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments1907.00624v1 · Bashar Alhnaity, Simon Pearson, Georgios Leontidis et al.2019 · 113 citationsarXivon Valency
Nemesyst: A Hybrid Parallelism Deep Learning-Based Framework Applied for Internet of Things Enabled Food Retailing Refrigeration Systems1906.01600v2 · George Onoufriou, Ronald Bickerton, Simon Pearson et al.2019 · 30 citationsComputers in Industry, 2019
Kriging-Based Robotic Exploration for Soil Moisture Mapping Using a Cosmic-Ray Sensor1811.05384v1 · Jaime Pulido Fentanes, Amir Badiee, Tom Duckett et al.2018 · 35 citationsarXiv
Machine Learning in Agriculture: A Review.30110960 · Liakos, Konstantinos G, Busato, Patrizia, Moshou, Dimitrios et al.2018 · 3,426 citationsSensors (Basel, Switzerland). 2018;18(8)
Agricultural Robotics: The Future of Robotic Agriculture1806.06762v2 · Tom Duckett, Simon Pearson, Simon Blackmore et al.2018 · 410 citationsarXivon Valency
3D Soil Compaction Mapping through Kriging-based Exploration with a Mobile Robot1803.08069v1 · Jaime Pulido Fentanes, Iain Gould, Tom Duckett et al.2018 · 41 citationsarXiv
Development of real-time PCR (TaqMan) assays for the detection and quantification of Botrytis cinerea in planta.16198585 · Suarez, M Belen, Walsh, Kathryn, Boonham, Neil et al.2006 · 145 citationsPlant physiology and biochemistry : PPB. 2005;43(9):890-9
Light quality and temperature effects on antirrhinum growth and development.15633247 · Khattak, Abdul Mateen, Pearson, Simon2005 · 30 citationsJournal of Zhejiang University. Science. B. 2005;6(2):119-24
Improving quantitative flowering models through a better understanding of the phases of photoperiod sensitivity.11413201 · Adams, S R, Pearson, S, Hadley, P2001 · 45 citationsJournal of experimental botany. 2001;52(357):655-62
The influence of temperature and light on defoliation levels of elm by dutch elm disease.18945072 · Sutherland, M L, Pearson, S, Brasier, C M2012 · 49 citationsPhytopathology. 1997;87(6):576-81
Comparison of radionuclide and contrast ventriculography for detection and quantitation of regions of myocardial ischemia in dogs.7229030 · Kronenberg, M W, Born, M L, Smith, C W et al.1981 · 3 citationsThe Journal of clinical investigation. 1981;67(5):1370-82
Career total: 169 works. 30 are in this corpus.
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