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Currently, greenhouses are widely applied for plant growth, and environmental parameters can also be controlled in the modern greenhouse to guarantee the maximum crop yield. In order to optimally control greenhouses’ environmental parameters, one indispensable requirement is to accurately predict crop yields based on given environmental parameter settings. In addition, crop yield forecasting in greenhouses plays an important role in greenhouse farming planning and management, which allows cultivators and farmers to utilize the yield prediction results to make knowledgeable management and financial decisions. It is thus important to accurately predict the crop yield in a greenhouse considering the benefits that can be brought by accurate greenhouse crop yield prediction. In this work, we have developed a new greenhouse crop yield prediction technique, by combining two state-of-the-arts networks for temporal sequence processing—temporal convolutional network (TCN) and recurrent neural network (RNN). Comprehensive evaluations of the proposed algorithm have been made on multiple datasets obtained from multiple real greenhouse sites for tomato growing. Based on a statistical analysis of the root mean square errors (RMSEs) between the predicted and actual crop yields, it is shown that the proposed approach achieves more accurate yield prediction performance than both traditional machine learning methods and other classical deep neural networks. Moreover, the experimental study also shows that the historical yield information is the most important factor for accurately predicting future crop yields.
Liyun Gong; Miao Yu; Shouyong Jiang; Vassilis Cutsuridis; Simon Pearson. Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN. Sensors 2021, 21, 4537 .
AMA StyleLiyun Gong, Miao Yu, Shouyong Jiang, Vassilis Cutsuridis, Simon Pearson. Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN. Sensors. 2021; 21 (13):4537.
Chicago/Turabian StyleLiyun Gong; Miao Yu; Shouyong Jiang; Vassilis Cutsuridis; Simon Pearson. 2021. "Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN." Sensors 21, no. 13: 4537.
Cosmic-Ray Neutron Probes (CRNP) have found application in soil moisture (SM) estimation due to their conveniently large (>100 m) footprints. Here, we explore the possibility of using high-density polyethylene (HDPE) moderator to limit the field of view, and hence, the footprint of a SM sensor formed of 12 CRNP mounted on to a mobile robotic platform (Thorvald) for better in-field localization of moisture variation. Ultra Rapid Adaptable Neutron-Only Simulation neutron scattering simulations are used to show that 5 cm of additional HDPE moderator (used to shield the upper surface and sides of the detector) is sufficient to (a) reduce the footprint of the detector considerably, (b) approximately double the percentage of neutrons detected from within 5 m of the detector, and (c) does not affect the shape of the curve used to convert neutron counts into SM. Simulation and rover measurements for a transect crossing between grass and concrete additionally suggest that (d) SM changes can be sensed over a length scales of tens of meters or less (roughly an order of magnitude smaller than commonly used footprint distances), and (e) the additional moderator does not reduce the detected neutron count rate (and hence increase noise) as much as might be expected given the extent of the additional moderator. The detector with additional HDPE moderator was also used to conduct measurements on a stubble field over three weeks to test the rover system in measuring spatial and temporal SM variation.
Amir Badiee; John R. Wallbank; Jaime Pulido Fentanes; Emily Trill; Peter Scarlet; Yongchao Zhu; Grzegorz Cielniak; Hollie Cooper; James R. Blake; Jonathan G. Evans; Marek Zreda; Markus Köhli; Simon Pearson. Using Additional Moderator to Control the Footprint of a COSMOS Rover for Soil Moisture Measurement. Water Resources Research 2021, 57, 1 .
AMA StyleAmir Badiee, John R. Wallbank, Jaime Pulido Fentanes, Emily Trill, Peter Scarlet, Yongchao Zhu, Grzegorz Cielniak, Hollie Cooper, James R. Blake, Jonathan G. Evans, Marek Zreda, Markus Köhli, Simon Pearson. Using Additional Moderator to Control the Footprint of a COSMOS Rover for Soil Moisture Measurement. Water Resources Research. 2021; 57 (6):1.
Chicago/Turabian StyleAmir Badiee; John R. Wallbank; Jaime Pulido Fentanes; Emily Trill; Peter Scarlet; Yongchao Zhu; Grzegorz Cielniak; Hollie Cooper; James R. Blake; Jonathan G. Evans; Marek Zreda; Markus Köhli; Simon Pearson. 2021. "Using Additional Moderator to Control the Footprint of a COSMOS Rover for Soil Moisture Measurement." Water Resources Research 57, no. 6: 1.
This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach.
Athanasios Anagnostis; Aristotelis Tagarakis; Dimitrios Kateris; Vasileios Moysiadis; Claus Sørensen; Simon Pearson; Dionysis Bochtis. Orchard Mapping with Deep Learning Semantic Segmentation. Sensors 2021, 21, 3813 .
AMA StyleAthanasios Anagnostis, Aristotelis Tagarakis, Dimitrios Kateris, Vasileios Moysiadis, Claus Sørensen, Simon Pearson, Dionysis Bochtis. Orchard Mapping with Deep Learning Semantic Segmentation. Sensors. 2021; 21 (11):3813.
Chicago/Turabian StyleAthanasios Anagnostis; Aristotelis Tagarakis; Dimitrios Kateris; Vasileios Moysiadis; Claus Sørensen; Simon Pearson; Dionysis Bochtis. 2021. "Orchard Mapping with Deep Learning Semantic Segmentation." Sensors 21, no. 11: 3813.
Despite the potential contributions of autonomous robots to agricultural sustainability, social, legal and ethical issues threaten adoption. We discuss how responsible innovation principles can be embedded into the user-centred design of autonomous robots and identify areas for further empirical research.
David Christian Rose; Jessica Lyon; Auvikki de Boon; Marc Hanheide; Simon Pearson. Responsible development of autonomous robotics in agriculture. Nature Food 2021, 1 -4.
AMA StyleDavid Christian Rose, Jessica Lyon, Auvikki de Boon, Marc Hanheide, Simon Pearson. Responsible development of autonomous robotics in agriculture. Nature Food. 2021; ():1-4.
Chicago/Turabian StyleDavid Christian Rose; Jessica Lyon; Auvikki de Boon; Marc Hanheide; Simon Pearson. 2021. "Responsible development of autonomous robotics in agriculture." Nature Food , no. : 1-4.
COVID-19 and the restrictive measures towards containing the spread of its infections have seriously affected the agricultural workforce and jeopardized food security. The present study aims at assessing the COVID-19 pandemic impacts on agricultural labor and suggesting strategies to mitigate them. To this end, after an introduction to the pandemic background, the negative consequences on agriculture and the existing mitigation policies, risks to the agricultural workers were benchmarked across the United States’ Standard Occupational Classification system. The individual tasks associated with each occupation in agricultural production were evaluated on the basis of potential COVID-19 infection risk. As criteria, the most prevalent virus transmission mechanisms were considered, namely the possibility of touching contaminated surfaces and the close proximity of workers. The higher risk occupations within the sector were identified, which facilitates the allocation of worker protection resources to the occupations where they are most needed. In particular, the results demonstrated that 50% of the agricultural workforce and 54% of the workers’ annual income are at moderate to high risk. As a consequence, a series of control measures need to be adopted so as to enhance the resilience and sustainability of the sector as well as protect farmers including physical distancing, hygiene practices, and personal protection equipment.
Dionysis Bochtis; Lefteris Benos; Maria Lampridi; Vasso Marinoudi; Simon Pearson; Claus Sørensen. Agricultural Workforce Crisis in Light of the COVID-19 Pandemic. Sustainability 2020, 12, 8212 .
AMA StyleDionysis Bochtis, Lefteris Benos, Maria Lampridi, Vasso Marinoudi, Simon Pearson, Claus Sørensen. Agricultural Workforce Crisis in Light of the COVID-19 Pandemic. Sustainability. 2020; 12 (19):8212.
Chicago/Turabian StyleDionysis Bochtis; Lefteris Benos; Maria Lampridi; Vasso Marinoudi; Simon Pearson; Claus Sørensen. 2020. "Agricultural Workforce Crisis in Light of the COVID-19 Pandemic." Sustainability 12, no. 19: 8212.
The advent of mobile robots in agriculture has signaled a digital transformation with new automation technologies optimize a range of labor-intensive, resources-demanding, and time-consuming agri-field operations. To that end a generally accepted technical lexicon for mobile robots is lacking as pertinent terms are often used interchangeably. This creates confusion among research and practice stakeholders. In addition, a consistent definition of planning attributes in automated agricultural operations is still missing as relevant research is sparse. In this regard, a “narrative” review was adopted (1) to provide the basic terminology over technical aspects of mobile robots used in autonomous operations and (2) assess fundamental planning aspects of mobile robots in agricultural environments. Based on the synthesized evidence from extant studies, seven planning attributes have been included: (i) high-level control-specific attributes, which include reasoning architecture, the world model, and planning level, (ii) operation-specific attributes, which include locomotion–task connection and capacity constraints, and (iii) physical robot-specific attributes, which include vehicle configuration and vehicle kinematics.
Vasileios Moisiadis; Naoum Tsolakis; Dimitris Katikaridis; Claus G. Sørensen; Simon Pearson; Dionysis Bochtis. Mobile Robotics in Agricultural Operations: A Narrative Review on Planning Aspects. Applied Sciences 2020, 10, 3453 .
AMA StyleVasileios Moisiadis, Naoum Tsolakis, Dimitris Katikaridis, Claus G. Sørensen, Simon Pearson, Dionysis Bochtis. Mobile Robotics in Agricultural Operations: A Narrative Review on Planning Aspects. Applied Sciences. 2020; 10 (10):3453.
Chicago/Turabian StyleVasileios Moisiadis; Naoum Tsolakis; Dimitris Katikaridis; Claus G. Sørensen; Simon Pearson; Dionysis Bochtis. 2020. "Mobile Robotics in Agricultural Operations: A Narrative Review on Planning Aspects." Applied Sciences 10, no. 10: 3453.
The application of global indices of nutrition and food sustainability in public health and the improvement of product profiles has facilitated effective actions that increase food security. In the research reported here we develop index measurements further so that they can be applied to food categories and be used by food processors and manufacturers for specific food supply chains. This research considers how they can be used to assess the sustainability of supply chain operations by stimulating more incisive food loss and waste reduction planning. The research demonstrates how an index driven approach focussed on improving both nutritional delivery and reducing food waste will result in improved food security and sustainability. Nutritional improvements are focussed on protein supply and reduction of food waste on supply chain losses and the methods are tested using the food systems of Kenya and India where the current research is being deployed. Innovative practices will emerge when nutritional improvement and waste reduction actions demonstrate market success, and this will result in the co-development of food manufacturing infrastructure and innovation programmes. The use of established indices of sustainability and security enable comparisons that encourage knowledge transfer and the establishment of cross-functional indices that quantify national food nutrition, security and sustainability. The research presented in this initial study is focussed on applying these indices to specific food supply chains for food processors and manufacturers.
Wayne Martindale; Isobel Wright; Lilian Korir; Arnold M. Opiyo; Benard Karanja; Samuel Nyalala; Mahesh Kumar; Simon Pearson; Mark Swainson. Framing food security and food loss statistics for incisive supply chain improvement and knowledge transfer between Kenyan, Indian and United Kingdom food manufacturers. Emerald Open Research 2020, 2, 12 .
AMA StyleWayne Martindale, Isobel Wright, Lilian Korir, Arnold M. Opiyo, Benard Karanja, Samuel Nyalala, Mahesh Kumar, Simon Pearson, Mark Swainson. Framing food security and food loss statistics for incisive supply chain improvement and knowledge transfer between Kenyan, Indian and United Kingdom food manufacturers. Emerald Open Research. 2020; 2 ():12.
Chicago/Turabian StyleWayne Martindale; Isobel Wright; Lilian Korir; Arnold M. Opiyo; Benard Karanja; Samuel Nyalala; Mahesh Kumar; Simon Pearson; Mark Swainson. 2020. "Framing food security and food loss statistics for incisive supply chain improvement and knowledge transfer between Kenyan, Indian and United Kingdom food manufacturers." Emerald Open Research 2, no. : 12.
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Piotr Chudzik; Arthur Mitchell; Mohammad Alkaseem; Yingie Wu; Shibo Fang; Taghread Hudaib; Simon Pearson; Bashir Al-Diri. Author Correction: Mobile Real-Time Grasshopper Detection and Data Aggregation Framework. Scientific Reports 2020, 10, 4644 -1.
AMA StylePiotr Chudzik, Arthur Mitchell, Mohammad Alkaseem, Yingie Wu, Shibo Fang, Taghread Hudaib, Simon Pearson, Bashir Al-Diri. Author Correction: Mobile Real-Time Grasshopper Detection and Data Aggregation Framework. Scientific Reports. 2020; 10 (1):4644-1.
Chicago/Turabian StylePiotr Chudzik; Arthur Mitchell; Mohammad Alkaseem; Yingie Wu; Shibo Fang; Taghread Hudaib; Simon Pearson; Bashir Al-Diri. 2020. "Author Correction: Mobile Real-Time Grasshopper Detection and Data Aggregation Framework." Scientific Reports 10, no. 1: 4644-1.
Insects of the family Orthoptera: Acrididae including grasshoppers and locust devastate crops and eco-systems around the globe. The effective control of these insects requires large numbers of trained extension agents who try to spot concentrations of the insects on the ground so that they can be destroyed before they take flight. This is a challenging and difficult task. No automatic detection system is yet available to increase scouting productivity, data scale and fidelity. Here we demonstrate MAESTRO, a novel grasshopper detection framework that deploys deep learning within RBG images to detect insects. MAESTRO uses a state-of-the-art two-stage training deep learning approach. The framework can be deployed not only on desktop computers but also on edge devices without internet connection such as smartphones. MAESTRO can gather data using cloud storage for further research and in-depth analysis. In addition, we provide a challenging new open dataset (GHCID) of highly variable grasshopper populations imaged in Inner Mongolia. The detection performance of the stationary method and the mobile App are 78 and 49 percent respectively; the stationary method requires around 1000 ms to analyze a single image, whereas the mobile app uses only around 400 ms per image. The algorithms are purely data-driven and can be used for other detection tasks in agriculture (e.g. plant disease detection) and beyond. This system can play a crucial role in the collection and analysis of data to enable more effective control of this critical global pest.
Piotr Chudzik; Arthur Mitchell; Mohammad Alkaseem; Yingie Wu; Shibo Fang; Taghread Hudaib; Simon Pearson; Bashir Al-Diri. Mobile Real-Time Grasshopper Detection and Data Aggregation Framework. Scientific Reports 2020, 10, 1 -10.
AMA StylePiotr Chudzik, Arthur Mitchell, Mohammad Alkaseem, Yingie Wu, Shibo Fang, Taghread Hudaib, Simon Pearson, Bashir Al-Diri. Mobile Real-Time Grasshopper Detection and Data Aggregation Framework. Scientific Reports. 2020; 10 (1):1-10.
Chicago/Turabian StylePiotr Chudzik; Arthur Mitchell; Mohammad Alkaseem; Yingie Wu; Shibo Fang; Taghread Hudaib; Simon Pearson; Bashir Al-Diri. 2020. "Mobile Real-Time Grasshopper Detection and Data Aggregation Framework." Scientific Reports 10, no. 1: 1-10.
The paper considers the impact of Demand Side Response events on the supply power profile and energy efficiency of widely distributed aggregated loads applied across commercial refrigeration systems. Responses to secondary grid frequency static DSR events are investigated. Experimental trials are conducted on a system of refrigerators representing a small retail store, and subsequently on the refrigerators of an operational superstore in the UK. Energy consumption and energy savings during 3 h of operation pre- and post-secondary DSR, are discussed. In addition, a simultaneous secondary DSR event is realised across three operational retail stores located in different geographical regions of the UK. A Simulink model for a 3Φ power network is used to investigate the impact of a synchronised return to normal operation of the aggregated refrigeration systems post DSR on the local power network. Results show ∼1% drop in line voltage due to the synchronised return to operation. Analysis of energy consumption shows that DSR events can facilitate energy savings of between 3.8% and 9.3% compared to normal operation. This is a result of the refrigerators operating more efficiently during and shortly after the DSR. The use of aggregated refrigeration loads can contribute to the necessary load-shed by 97.3% at the beginning of DSR and 27% during the 30 minutes of a DSR, based on results from a simultaneous DSR event carried out on three retail stores.
Ibrahim M. Albayati; Andrey Postnikov; Simon Pearson; Ronald Bickerton; Argyrios Zolotas; Chris Bingham. Power and energy analysis for a commercial retail refrigeration system responding to a static demand side response. International Journal of Electrical Power & Energy Systems 2019, 117, 105645 .
AMA StyleIbrahim M. Albayati, Andrey Postnikov, Simon Pearson, Ronald Bickerton, Argyrios Zolotas, Chris Bingham. Power and energy analysis for a commercial retail refrigeration system responding to a static demand side response. International Journal of Electrical Power & Energy Systems. 2019; 117 ():105645.
Chicago/Turabian StyleIbrahim M. Albayati; Andrey Postnikov; Simon Pearson; Ronald Bickerton; Argyrios Zolotas; Chris Bingham. 2019. "Power and energy analysis for a commercial retail refrigeration system responding to a static demand side response." International Journal of Electrical Power & Energy Systems 117, no. : 105645.
Aggregated electrical loads from massive numbers of distributed retail refrigeration systems could have a significant role in frequency balancing services. To date, no study has realised effective engineering applications of static firm frequency response to these aggregated networks. Here, the authors present a novel and validated approach that enables large scale control of distributed retail refrigeration assets. The authors show a validated model that simulates the operation of retail refrigerators comprising centralised compressor packs feeding multiple in-store display cases. The model was used to determine an optimal control strategy that both minimised the engineering risk to the pack during shut down and potential impacts to food safety. The authors show that following a load shedding frequency response trigger the pack should be allowed to maintain operation but with increased suction pressure set-point. This reduces compressor load whilst enabling a continuous flow of refrigerant to food cases. In addition, the authors simulated an aggregated response of up to three hundred compressor packs (over 2 MW capacity), with refrigeration cases on hysteresis and modulation control. Hysteresis control, compared to modulation, led to undesired load oscillations when the system recovers after a frequency balancing event. Transient responses of the system during the event showed significant fluctuations of active power when compressor network responds to both primary and secondary parts of a frequency balancing event. Enabling frequency response within this system is demonstrated by linking the aggregated refrigeration loads with a simplified power grid model that simulates a power loss incident.
A. Postnikov; I.M. Albayati; S. Pearson; C. Bingham; R. Bickerton; A. Zolotas. Facilitating static firm frequency response with aggregated networks of commercial food refrigeration systems. Applied Energy 2019, 251, 113357 .
AMA StyleA. Postnikov, I.M. Albayati, S. Pearson, C. Bingham, R. Bickerton, A. Zolotas. Facilitating static firm frequency response with aggregated networks of commercial food refrigeration systems. Applied Energy. 2019; 251 ():113357.
Chicago/Turabian StyleA. Postnikov; I.M. Albayati; S. Pearson; C. Bingham; R. Bickerton; A. Zolotas. 2019. "Facilitating static firm frequency response with aggregated networks of commercial food refrigeration systems." Applied Energy 251, no. : 113357.
Threats to global food security from multiple sources, such as population growth, ageing farming populations, meat consumption trends, climate-change effects on abiotic and biotic stresses, the environmental impacts of agriculture are well publicised. In addition, with ever increasing tolerance of pest, diseases and weeds there is growing pressure on traditional crop genetic and protective chemistry technologies of the ‘Green Revolution’. To ease the burden of these challenges, there has been a move to automate and robotise aspects of the farming process. This drive has focussed typically on higher value sectors, such as horticulture and viticulture, that have relied on seasonal manual labour to maintain produce supply. In developed economies, and increasingly developing nations, pressure on labour supply has become unsustainable and forced the need for greater mechanisation and higher labour productivity. This paper creates the case that for broadacre crops, such as cereals, a wholly new approach is necessary, requiring the establishment of an integrated biology & physical engineering infrastructure, which can work in harmony with current breeding, chemistry and agronomic solutions. For broadacre crops the driving pressure is to sustainably intensify production; increase yields and/or productivity whilst reducing environmental impact. Additionally, our limited understanding of the complex interactions between the variations in pests, weeds, pathogens, soils, water, environment and crops is inhibiting growth in resource productivity and creating yield gaps. We argue that for agriculture to deliver knowledge based sustainable intensification requires a new generation of Smart Technologies, which combine sensors and robotics with localised and/or cloud-based Artificial Intelligence (AI).
Bruce Donaldson Grieve; Tom Duckett; Martin Collison; Lesley Boyd; Jon West; Hujun Yin; Farshad Arvin; Simon Pearson. The challenges posed by global broadacre crops in delivering smart agri-robotic solutions: A fundamental rethink is required. Global Food Security 2019, 23, 116 -124.
AMA StyleBruce Donaldson Grieve, Tom Duckett, Martin Collison, Lesley Boyd, Jon West, Hujun Yin, Farshad Arvin, Simon Pearson. The challenges posed by global broadacre crops in delivering smart agri-robotic solutions: A fundamental rethink is required. Global Food Security. 2019; 23 ():116-124.
Chicago/Turabian StyleBruce Donaldson Grieve; Tom Duckett; Martin Collison; Lesley Boyd; Jon West; Hujun Yin; Farshad Arvin; Simon Pearson. 2019. "The challenges posed by global broadacre crops in delivering smart agri-robotic solutions: A fundamental rethink is required." Global Food Security 23, no. : 116-124.
The need to intensify agriculture to meet increasing nutritional needs, in combination with the evolution of unmanned autonomous systems has led to the development of a series of “smart” farming technologies that are expected to replace or complement conventional machinery and human labor. This paper proposes a preliminary methodology for the economic analysis of the employment of robotic systems in arable farming. This methodology is based on the basic processes for estimating the use cost for agricultural machinery. However, for the case of robotic systems, no average norms for the majority of the operational parameters are available. Here, we propose a novel estimation process for these parameters in the case of robotic systems. As a case study, the operation of light cultivation has been selected due the technological readiness for this type of operation.
Maria G. Lampridi; Dimitrios Kateris; Giorgos Vasileiadis; Vasso Marinoudi; Simon Pearson; Claus G. Sørensen; Athanasios Balafoutis; Dionysis Bochtis. A Case-Based Economic Assessment of Robotics Employment in Precision Arable Farming. Agronomy 2019, 9, 175 .
AMA StyleMaria G. Lampridi, Dimitrios Kateris, Giorgos Vasileiadis, Vasso Marinoudi, Simon Pearson, Claus G. Sørensen, Athanasios Balafoutis, Dionysis Bochtis. A Case-Based Economic Assessment of Robotics Employment in Precision Arable Farming. Agronomy. 2019; 9 (4):175.
Chicago/Turabian StyleMaria G. Lampridi; Dimitrios Kateris; Giorgos Vasileiadis; Vasso Marinoudi; Simon Pearson; Claus G. Sørensen; Athanasios Balafoutis; Dionysis Bochtis. 2019. "A Case-Based Economic Assessment of Robotics Employment in Precision Arable Farming." Agronomy 9, no. 4: 175.
Distributed Ledger Technology (DLT), such as blockchain, has the potential to transform supply chains. It can provide a cryptographically secure and immutable record of transactions and associated metadata (origin, contracts, process steps, environmental variations, microbial records, etc.) linked across whole supply chains. The ability to trace food items within and along a supply chain is legally required by all actors within the chain. It is critical to food safety, underpins trust and global food trade. However, current food traceability systems are not linked between all actors within the supply chain. Key metadata on the age and process history of a food is rarely transferred when a product is bought and sold through multiple steps within the chain. Herein, we examine the potential of massively scalable DLT to securely link the entire food supply chain, from producer to end user. Under such a paradigm, should a food safety or quality issue ever arise, authorized end users could instantly and accurately trace the origin and history of any particular food item. This novel and unparalleled technology could help underpin trust for the safety of all food, a critical component of global food security. In this paper, we investigate the (i) data requirements to develop DLT technology across whole supply chains, (ii) key challenges and barriers to optimizing the complete system, and (iii) potential impacts on production efficiency, legal compliance, access to global food markets and the safety of food. Our conclusion is that while DLT has the potential to transform food systems, this can only be fully realized through the global development and agreement on suitable data standards and governance. In addition, key technical issues need to be resolved including challenges with DLT scalability, privacy and data architectures.
Simon Pearson; David May; Georgios Leontidis; Mark Swainson; Steve Brewer; Luc Bidaut; Jeremy G. Frey; Gerard Parr; Roger Maull; Andrea Zisman. Are Distributed Ledger Technologies the panacea for food traceability? Global Food Security 2019, 20, 145 -149.
AMA StyleSimon Pearson, David May, Georgios Leontidis, Mark Swainson, Steve Brewer, Luc Bidaut, Jeremy G. Frey, Gerard Parr, Roger Maull, Andrea Zisman. Are Distributed Ledger Technologies the panacea for food traceability? Global Food Security. 2019; 20 ():145-149.
Chicago/Turabian StyleSimon Pearson; David May; Georgios Leontidis; Mark Swainson; Steve Brewer; Luc Bidaut; Jeremy G. Frey; Gerard Parr; Roger Maull; Andrea Zisman. 2019. "Are Distributed Ledger Technologies the panacea for food traceability?" Global Food Security 20, no. : 145-149.
Soil moisture (SM) products derived from passive satellite missions are playing an increasingly important role in agricultural applications, especially crop monitoring and disaster warning. Evaluating the dependability of satellite-derived soil moisture products on a large scale is crucial. In this study, we assessed the level 2 (L2) SM product from the Chinese Fengyun-3C (FY-3C) radiometer against in-situ measurements collected from the Chinese Automatic Soil Moisture Observation Stations (CASMOS) during a one-year period from 1 January 2016 to 31 December 2016 across Henan in China. In contrast, we also investigated the skill of the Advanced Microwave Scanning Radiometer 2 (AMSR2) and Soil Moisture Active/Passive (SMAP) SM products simultaneously. Four statistical parameters were used to evaluate these products’ reliability: mean difference, root-mean-square error (RMSE), unbiased RMSE (ubRMSE), and the correlation coefficient. Our assessment results revealed that the FY-3C L2 SM product generally showed a poor correlation with the in-situ SM data from CASMOS on both temporal and spatial scales. The AMSR2 L3 SM product of JAXA (Japan Aerospace Exploration Agency) algorithm had a similar level of skill as FY-3C in the study area. The SMAP L3 SM product outperformed the FY-3C temporally but showed lower performance in capturing the SM spatial variation. A time-series analysis indicated that the correlations and estimated error varied systematically through the growing periods of the key crops in our study area. FY-3C L2 SM data tended to overestimate soil moisture during May, August, and September when the crops reached maximum vegetation density and tended to underestimate the soil moisture content during the rest of the year. The comparison between the statistical parameters and the ground vegetation water content (VWC) further showed that the FY-3C SM product performed much better under a low VWC condition (0.3 kg/m2), and the performance generally decreased with increased VWC. To improve the accuracy of the FY-3C SM product, an improved algorithm that can better characterize the variations of the ground VWC should be applied in the future.
Yongchao Zhu; Xuan Li; Simon Pearson; Dongli Wu; Ruijing Sun; Sarah Johnson; James Wheeler; Shibo Fang. Evaluation of Fengyun-3C Soil Moisture Products Using In-Situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China. Water 2019, 11, 248 .
AMA StyleYongchao Zhu, Xuan Li, Simon Pearson, Dongli Wu, Ruijing Sun, Sarah Johnson, James Wheeler, Shibo Fang. Evaluation of Fengyun-3C Soil Moisture Products Using In-Situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China. Water. 2019; 11 (2):248.
Chicago/Turabian StyleYongchao Zhu; Xuan Li; Simon Pearson; Dongli Wu; Ruijing Sun; Sarah Johnson; James Wheeler; Shibo Fang. 2019. "Evaluation of Fengyun-3C Soil Moisture Products Using In-Situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China." Water 11, no. 2: 248.
Soil moisture (SM) products derived from passive satellite missions are playing an increasingly important role in agricultural applications, especially in crop monitoring and disaster warning. Evaluating the dependability of those products before they can be used on a large scale is crucial. In this study, we assessed the level 2 (L2) SM product from the Chinese Fengyun-3C (FY-3C) radiometer against in situ measurements collected from the Chinese Automatic Soil Moisture Observation Stations (CASMOS) during a one-year period from January 1 to December 31, 2016 in Henan, which is an agricultural province in China. Four statistical parameters were used to evaluate the products’ reliability: mean difference, root-mean-square error (RMSE), unbiased RMSE (ubRMSE), and the correlation coefficient. These statistical indicators revealed that the FY-3C L2 SM product generally did not agree with the in situ SM data from CASMOS. The time-series analysis further indicated that the correlations and estimated error were highly related to the growing periods of the crops in our study area. FY-3C L2 SM data tended to overestimate soil moisture during May, August, and September, when the crops reach their maximum vegetation density, and tended to underestimate the soil moisture content during the rest of the year. The averaged correlation coefficient between FY-3C SM and the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index was 0.55, which demonstrates that the vegetation water content of the crops considerably influences the SM product. To improve the accuracy of the FY-3C SM product, an improved algorithm that can filter out the influences of the crops should be applied in the future.
Yongchao Zhu; Simon Pearson; Dongli Wu; Ruijing Sun; Shibo Fang. Evaluation of Fengyun-3C Soil Moisture Products Using In situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China. 2018, 1 .
AMA StyleYongchao Zhu, Simon Pearson, Dongli Wu, Ruijing Sun, Shibo Fang. Evaluation of Fengyun-3C Soil Moisture Products Using In situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China. . 2018; ():1.
Chicago/Turabian StyleYongchao Zhu; Simon Pearson; Dongli Wu; Ruijing Sun; Shibo Fang. 2018. "Evaluation of Fengyun-3C Soil Moisture Products Using In situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China." , no. : 1.
Compared with other remote observations, brightness temperatures (TB) derived from microwave emission measurements provide a unique means to characterize the physical properties of the lunar surface. Using Chang’E-2 microwave radiometer data, we produced 12 global TB images of the lunar surface during a diurnal cycle with different local times separated by approximately 2 hours. There are two types of remarkable TB units on the lunar surface, the “hot regions” occurring during the lunar day in the lunar Maria regions and the “microwave cold spots” occurring during the nighttime typically related to young craters. Compared with their surroundings, the hot regions are much warmer during the lunar day and slightly colder at night, while the microwave cold spots are much colder during the lunar night and slightly warmer in the daytime. Moreover, the TB heating and cooling rates of these two units are larger than others at the same average latitude where they are located during the lunar day, especially after sunrise and before sunset. The hot regions have a good agreement with the mare regions with high TiO2 abundance. Besides, brightness temperatures in the lunar Maria correlate closely with their TiO2 abundance. For most microwave cold spots, they agree with the young craters, and their brightness temperature distributions have a significant negative correlation with the lunar surface nighttime temperature and rock abundance.
Yongchao Zhu; Yongchun Zheng; Shibo Fang; Yongliao Zou; Simon Pearson. Analysis of the brightness temperature features of the lunar surface using 37 GHz channel data from the Chang'E-2 microwave radiometer. Advances in Space Research 2018, 63, 750 -765.
AMA StyleYongchao Zhu, Yongchun Zheng, Shibo Fang, Yongliao Zou, Simon Pearson. Analysis of the brightness temperature features of the lunar surface using 37 GHz channel data from the Chang'E-2 microwave radiometer. Advances in Space Research. 2018; 63 (1):750-765.
Chicago/Turabian StyleYongchao Zhu; Yongchun Zheng; Shibo Fang; Yongliao Zou; Simon Pearson. 2018. "Analysis of the brightness temperature features of the lunar surface using 37 GHz channel data from the Chang'E-2 microwave radiometer." Advances in Space Research 63, no. 1: 750-765.
Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.
Konstantinos G Liakos; Patrizia Busato; Dimitrios Moshou; Simon Pearson; Dionysis Bochtis. Machine Learning in Agriculture: A Review. Sensors 2018, 18, 2674 .
AMA StyleKonstantinos G Liakos, Patrizia Busato, Dimitrios Moshou, Simon Pearson, Dionysis Bochtis. Machine Learning in Agriculture: A Review. Sensors. 2018; 18 (8):2674.
Chicago/Turabian StyleKonstantinos G Liakos; Patrizia Busato; Dimitrios Moshou; Simon Pearson; Dionysis Bochtis. 2018. "Machine Learning in Agriculture: A Review." Sensors 18, no. 8: 2674.
This paper presents an automated method for creating spatial maps of soil condition with an outdoor mobile robot. Effective soil mapping on farms can enhance yields, reduce inputs and help protect the environment. Traditionally, data are collected manually at an arbitrary set of locations, then soil maps are constructed offline using Kriging, a form of Gaussian process regression. This process is laborious and costly, limiting the quality and resolution of the resulting information. Instead, we propose to use an outdoor mobile robot for automatic collection of soil condition data, building soil maps online and also adapting the robot's exploration strategy on-the-fly based on the current quality of the map. We show how using Kriging variance as a reward function for robotic exploration allows for both more efficient data collection and better soil models. This work presents the theoretical foundations for our proposal and an experimental comparison of exploration strategies using soil compaction data from a field generated with a mobile robot.
Jaime Pulido Fentanes; Iain Gould; Tom Duckett; Simon Pearson; Grzegorz Cielniak. 3-D Soil Compaction Mapping Through Kriging-Based Exploration With a Mobile Robot. IEEE Robotics and Automation Letters 2018, 3, 3066 -3072.
AMA StyleJaime Pulido Fentanes, Iain Gould, Tom Duckett, Simon Pearson, Grzegorz Cielniak. 3-D Soil Compaction Mapping Through Kriging-Based Exploration With a Mobile Robot. IEEE Robotics and Automation Letters. 2018; 3 (4):3066-3072.
Chicago/Turabian StyleJaime Pulido Fentanes; Iain Gould; Tom Duckett; Simon Pearson; Grzegorz Cielniak. 2018. "3-D Soil Compaction Mapping Through Kriging-Based Exploration With a Mobile Robot." IEEE Robotics and Automation Letters 3, no. 4: 3066-3072.
The soft fruit industry is facing unprecedented challenges due to its reliance of manual labour. We are presenting a newly launched robotics initiative which will help to address the issues faced by the industry and enable automation of the main processes involved in soft fruit production. The RASberry project (Robotics and Autonomous Systems for Berry Production) aims to develop autonomous fleets of robots for horticultural industry. To achieve this goal, the project will bridge several current technological gaps including the development of a mobile platform suitable for the strawberry fields, software components for fleet management, in-field navigation and mapping, long-term operation, and safe human-robot collaboration. In this paper, we provide a general overview of the project, describe the main system components, highlight interesting challenges from a control point of view and then present three specific applications of the robotic fleets in soft fruit production. The applications demonstrate how robotic fleets can benefit the soft fruit industry by significantly decreasing production costs, addressing labour shortages and being the first step towards fully autonomous robotic systems for agriculture.
Pål Johan From; Lars Grimstad; Marc Hanheide; Simon Pearson; Grzegorz Cielniak. RASberry - Robotic and Autonomous Systems for Berry Production. Mechanical Engineering 2018, 140, S14 -S18.
AMA StylePål Johan From, Lars Grimstad, Marc Hanheide, Simon Pearson, Grzegorz Cielniak. RASberry - Robotic and Autonomous Systems for Berry Production. Mechanical Engineering. 2018; 140 (6):S14-S18.
Chicago/Turabian StylePål Johan From; Lars Grimstad; Marc Hanheide; Simon Pearson; Grzegorz Cielniak. 2018. "RASberry - Robotic and Autonomous Systems for Berry Production." Mechanical Engineering 140, no. 6: S14-S18.