The Digitalisation of African Agriculture Report 2018-2019

An inclusive, digitally-enabled agricultural transformation could help achieve meaningful livelihood improvements for Africa’s smallholder farmers and pastoralists. It could drive greater engagement in agriculture from women and youth and create employment opportunities along the value chain. At CTA we staked a claim on this power of digitalisation to more systematically transform agriculture early on. Digitalisation, focusing on not individual ICTs but the application of these technologies to entire value chains, is a theme that cuts across all of our work. In youth entrepreneurship, we are fostering a new breed of young ICT ‘agripreneurs’. In climate-smart agriculture multiple projects provide information that can help towards building resilience for smallholder farmers. And in women empowerment we are supporting digital platforms to drive greater inclusion for women entrepreneurs in agricultural value chains

Utilization of Internet of Things and wireless sensor networks for sustainable smallholder agriculture

Agriculture is the economy’s backbone for most developing countries. Most of these countries suffer from insufficient agricultural production. The availability of real-time, reliable and farm-specific information may significantly contribute to more sufficient and sustained production. Typically, such information is usually fragmented and often does fit one-on-one with the farm or farm plot. Automated, precise and affordable data collection and dissemination tools are vital to bring such information to these levels. The tools must address details of spatial and temporal variability. The Internet of Things (IoT) and wireless sensor networks (WSNs) are useful technology in this respect. This paper investigates the usability of IoT and WSN for smallholder agriculture applications. An in-depth qualitative and quantitative analysis of relevant work over the past decade was conducted. We explore the type and purpose of agricultural parameters, study and describe available resources, needed skills and technological requirements that allow sustained deployment of IoT and WSN technology. Our findings reveal significant gaps in utilization of the technology in the context of smallholder farm practices caused by social, economic, infrastructural and technological barriers. We also identify a significant future opportunity to design and implement affordable and reliable data acquisition tools and frameworks, with a possible integration of citizen science

Complementary Use of Ground-Based Proximal Sensing and Airborne/Spaceborne Remote Sensing Techniques in Precision Agriculture: A Systematic Review

As the global population continues to increase, projected to reach an estimated 9.7 billion people by 2050, there will be a growing demand for food production and agricultural resources. Transition toward Agriculture 4.0 is expected to enhance agricultural productivity through the integration of advanced technologies, increase resource efficiency, ensure long-term food security by applying more sustainable farming practices, and enhance resilience and climate change adaptation. By integrating technologies such as ground IoT sensing and remote sensing, via both satellite and Unmanned Aerial Vehicles (UAVs), and exploiting data fusion and data analytics, farming can make the transition to a more efficient, productive, and sustainable paradigm. The present work performs a systematic literature review (SLR), identifying the challenges associated with UAV, Satellite, and Ground Sensing in their application in agriculture, comparing them and discussing their complementary use to facilitate Precision Agriculture (PA) and transition to Agriculture 4.0

EIP-AGRI Focus Group : Precision farming

Final report201

The technological translation from Industry 4.0 to Precision Agriculture: adoption and perception of Italian farmers

Purpose: This research aims to identify the rate of knowledge, adoption and perceptions of Italian farmers towards Precision Agriculture technologies. Methodology: An online survey was carried out, using the Snowball sampling method, among 755 Italian farmers and involving the main Italian trade associations. Findings: The findings showed that among Italian farmers the technologies related to Monitoring appear to be the best known, adopted and perceived as the most useful; followed by technologies related to Automation and IoT. Managerial implications: Considering the results that emerged from this research, it seems necessary to undertake models of training development paths so that farmers can deepen the themes of technological integration with an orientation towards sustainability. Research limitations: The present research, not being able to be considered exhaustive for the understanding of the phenomenon, aims to be the starting point for future research aimed at a further analysis on the models of diffusion and technological integration. Originality: The models of technological integration for agricultural cultivation techniques are constantly evolving. Through the analysis of knowledge, use and perception of farmers it could be possible to detect new models for the diffusion of technology

From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management

[EN] The information that crops offer is turned into profitable decisions only when efficiently managed. Current advances in data management are making Smart Farming grow exponentially as data have become the key element in modern agriculture to help producers with critical decision-making. Valuable advantages appear with objective information acquired through sensors with the aim of maximizing productivity and sustainability. This kind of data-based managed farms rely on data that can increase efficiency by avoiding the misuse of resources and the pollution of the environment. Data-driven agriculture, with the help of robotic solutions incorporating artificial intelligent techniques, sets the grounds for the sustainable agriculture of the future. This paper reviews the current status of advanced farm management systems by revisiting each crucial step, from data acquisition in crop fields to variable rate applications, so that growers can make optimized decisions to save money while protecting the environment and transforming how food will be produced to sustainably match the forthcoming population growth.This research article is part of a project that has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 737669.Sáiz Rubio, V.; Rovira Más, F. (2020). From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management. Agronomy. 10(2):1-21. https://doi.org/10.3390/agronomy10020207S121102Himesh, S. (2018). Digital revolution and Big Data: a new revolution in agriculture. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, 13(021). doi:10.1079/pavsnnr201813021Digital Agriculture: Improving Profitabilityhttps://www.accenture.com/_acnmedia/accenture/conversion-assets/dotcom/documents/global/pdf/digital_3/accenture-digital-agriculture-point-of-view.pdfDigital Farming: What Does It Really Mean?http://www.cema-agri.org/publication/digital-farming-what-does-it-really-meanAgriculture Needs to Attract More Young Peoplehttp://www.gainhealth.org/knowledge-centre/worlds-farmers-age-new-blood-neededGenerational Renewalhttps://enrd.ec.europa.eu/enrd-thematic-work/generational-renewal_enWhat is IoT in Agriculture? Farmers Aren’t Quite Sure Despite $4bn US Opportunity—Reporthttps://agfundernews.com/iot-agriculture-farmers-arent-quite-sure-despite-4bn-us-opportunity.htmlPrecision Agriculture Yields Higher Profits, Lower Riskshttps://www.hpe.com/us/en/insights/articles/precision-agriculture-yields-higher-profits-lower-risks-1806.htmlTzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of Things in agriculture, recent advances and future challenges. Biosystems Engineering, 164, 31-48. doi:10.1016/j.biosystemseng.2017.09.007From Dirt to Data: The Second Green Revolution and IoT. Deloitte insightshttps://www2.deloitte.com/insights/us/en/deloitte-review/issue-18/second-green-revolution-and-internet-of-things.html#endnote-sup-9Big Data: The Next Frontier for Innovation, Competition, and Productivity | McKinseyhttps://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/big-data-the-next-frontier-for-innovationWolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big Data in Smart Farming – A review. Agricultural Systems, 153, 69-80. doi:10.1016/j.agsy.2017.01.023Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23-37. doi:10.1016/j.compag.2017.09.037How Big Data Will Change Agriculturehttps://proagrica.com/news/how-big-data-will-change-agriculture/Big Data Coordination Platform. Proposal to the CGIAR Fund Councilhttps://cgspace.cgiar.org/handle/10947/4303Zambon, I., Cecchini, M., Egidi, G., Saporito, M. G., & Colantoni, A. (2019). Revolution 4.0: Industry vs. Agriculture in a Future Development for SMEs. Processes, 7(1), 36. doi:10.3390/pr7010036How AI Is Transforming Agriculturehttps://www.forbes.com/sites/cognitiveworld/2019/07/05/how-ai-is-transforming-agriculture/Bechar, A., & Vigneault, C. (2016). Agricultural robots for field operations: Concepts and components. Biosystems Engineering, 149, 94-111. doi:10.1016/j.biosystemseng.2016.06.014Bechar, A., & Vigneault, C. (2017). Agricultural robots for field operations. Part 2: Operations and systems. Biosystems Engineering, 153, 110-128. doi:10.1016/j.biosystemseng.2016.11.004Ramin Shamshiri, R., Weltzien, C., A. Hameed, I., J. Yule, I., … E. Grift, T. (2018). Research and development in agricultural robotics: A perspective of digital farming. International Journal of Agricultural and Biological Engineering, 11(4), 1-11. doi:10.25165/j.ijabe.20181104.4278Farming 4.0: The Future of Agriculture?https://www.euractiv.com/section/agriculture-food/infographic/farming-4-0-the-future-of-agriculture/Ag Tech Deal Activity More Than Tripleshttps://www.cbinsights.com/research/agriculture-farm-tech-startup-funding-trends/AI, Robotics, And the Future of Precision Agriculturehttps://www.cbinsights.com/research/ai-robotics-agriculture-tech-startups-future/VineScout European Projectwww.vinescout.euPrecision Farming: A New Approach to Crop Managementhttp://agpublications.tamu.edu/pubs/eng/l5177.pdfZhang, N., Wang, M., & Wang, N. (2002). Precision agriculture—a worldwide overview. Computers and Electronics in Agriculture, 36(2-3), 113-132. doi:10.1016/s0168-1699(02)00096-0MIAO, Y., MULLA, D. J., & ROBERT, P. C. (2018). An integrated approach to site-specific management zone delineation. Frontiers of Agricultural Science and Engineering, 0(0), 0. doi:10.15302/j-fase-2018230Klassen, S. P., Villa, J., Adamchuk, V., & Serraj, R. (2014). Soil mapping for improved phenotyping of drought resistance in lowland rice fields. Field Crops Research, 167, 112-118. doi:10.1016/j.fcr.2014.07.007Khanal, S., Fulton, J., & Shearer, S. (2017). An overview of current and potential applications of thermal remote sensing in precision agriculture. Computers and Electronics in Agriculture, 139, 22-32. doi:10.1016/j.compag.2017.05.001Aravind, K. R., Raja, P., & Pérez-Ruiz, M. (2017). Task-based agricultural mobile robots in arable farming: A review. Spanish Journal of Agricultural Research, 15(1), e02R01. doi:10.5424/sjar/2017151-9573Roldán, J. J., Cerro, J. del, Garzón‐Ramos, D., Garcia‐Aunon, P., Garzón, M., León, J. de, & Barrientos, A. (2018). Robots in Agriculture: State of Art and Practical Experiences. Service Robots. doi:10.5772/intechopen.69874Gonzalez-de-Santos, P., Ribeiro, A., Fernandez-Quintanilla, C., Lopez-Granados, F., Brandstoetter, M., Tomic, S., … Debilde, B. (2016). Fleets of robots for environmentally-safe pest control in agriculture. Precision Agriculture, 18(4), 574-614. doi:10.1007/s11119-016-9476-3What’s Slowing the Use of Robots in the Ag Industry?https://www.therobotreport.com/whats-slowing-the-use-of-robots-in-the-ag-industry/Bogue, R. (2016). Robots poised to revolutionise agriculture. Industrial Robot: An International Journal, 43(5), 450-456. doi:10.1108/ir-05-2016-0142Features & Benefits OZ Weeding Robothttps://www.naio-technologies.com/en/agricultural-equipment/weeding-robot-oz/Robotics for Sustainable Broad-Acre Agriculturehttps://www.researchgate.net/publication/283722961_Robotics_for_Sustainable_Broad-Acre_AgricultureThe Ultimate Guide to Agricultural Roboticshttps://www.roboticsbusinessreview.com/agriculture/the_ultimate_guide_to_agricultural_robotics/Kweon, G., Lund, E., & Maxton, C. (2013). Soil organic matter and cation-exchange capacity sensing with on-the-go electrical conductivity and optical sensors. Geoderma, 199, 80-89. doi:10.1016/j.geoderma.2012.11.001Agricultural Robots—Present and Future Applications (Videos Included)https://emerj.com/ai-sector-overviews/agricultural-robots-present-future-applications/Köksal, Ö., & Tekinerdogan, B. (2018). Architecture design approach for IoT-based farm management information systems. Precision Agriculture, 20(5), 926-958. doi:10.1007/s11119-018-09624-8Rovira-Más, F., & Sáiz-Rubio, V. (2013). Crop Biometric Maps: The Key to Prediction. Sensors, 13(9), 12698-12743. doi:10.3390/s130912698Oliver, M. A., & Webster, R. (2014). A tutorial guide to geostatistics: Computing and modelling variograms and kriging. CATENA, 113, 56-69. doi:10.1016/j.catena.2013.09.006Adamchuk, V. ., Hummel, J. ., Morgan, M. ., & Upadhyaya, S. . (2004). On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture, 44(1), 71-91. doi:10.1016/j.compag.2004.03.002Cossell, S., Whitty, M., Liu, S., & Tang, J. (2016). Spatial Map Generation from Low Cost Ground Vehicle Mounted Monocular Camera. IFAC-PapersOnLine, 49(16), 231-236. doi:10.1016/j.ifacol.2016.10.043N. Zhang, & R. K. Taylor. (2001). APPLICATIONS OF A FIELD LEVEL GEOGRAPHIC INFORMATION SYSTEM (FIS) IN PRECISION AGRICULTURE. Applied Engineering in Agriculture, 17(6). doi:10.13031/2013.6829Runquist, S., Zhang, N., & Taylor, R. K. (2001). Development of a field-level geographic information system. Computers and Electronics in Agriculture, 31(2), 201-209. doi:10.1016/s0168-1699(00)00155-1Granular Farm Management Software, Precision Agriculture, Agricultural Softwarehttps://granular.ag/Capterra. Farm Management Softwarewww.capterra.comTop 9 Farm Management Software—Compare Reviews, Features, Pricing in 2019https://www.predictiveanalyticstoday.com/top-farm-management-software/Srivastava, P. K., & Singh, R. M. (2016). GIS based integrated modelling framework for agricultural canal system simulation and management in Indo-Gangetic plains of India. Agricultural Water Management, 163, 37-47. doi:10.1016/j.agwat.2015.08.025Giusti, E., & Marsili-Libelli, S. (2015). A Fuzzy Decision Support System for irrigation and water conservation in agriculture. Environmental Modelling & Software, 63, 73-86. doi:10.1016/j.envsoft.2014.09.020Asfaw, D., Black, E., Brown, M., Nicklin, K. J., Otu-Larbi, F., Pinnington, E., … Quaife, T. (2018). TAMSAT-ALERT v1: a new framework for agricultural decision support. Geoscientific Model Development, 11(6), 2353-2371. doi:10.5194/gmd-11-2353-2018https://dssat.netNavarro-Hellín, H., Martínez-del-Rincon, J., Domingo-Miguel, R., Soto-Valles, F., & Torres-Sánchez, R. (2016). A decision support system for managing irrigation in agriculture. Computers and Electronics in Agriculture, 124, 121-131. doi:10.1016/j.compag.2016.04.003Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., & Bansal, R. C. (2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews, 69, 596-609. doi:10.1016/j.rser.2016.11.191Rupnik, R., Kukar, M., Vračar, P., Košir, D., Pevec, D., & Bosnić, Z. (2019). AgroDSS: A decision support system for agriculture and farming. Computers and Electronics in Agriculture, 161, 260-271. doi:10.1016/j.compag.2018.04.001Rose, D. C., Sutherland, W. J., Parker, C., Lobley, M., Winter, M., Morris, C., … Dicks, L. V. (2016). Decision support tools for agriculture: Towards effective design and delivery. Agricultural Systems, 149, 165-174. doi:10.1016/j.agsy.2016.09.009Colaço, A. F., & Molin, J. P. (2016). Variable rate fertilization in citrus: a long term study. Precision Agriculture, 18(2), 169-191. doi:10.1007/s11119-016-9454-9Nawar, S., Corstanje, R., Halcro, G., Mulla, D., & Mouazen, A. M. (2017). Delineation of Soil Management Zones for Variable-Rate Fertilization. Advances in Agronomy, 175-245. doi:10.1016/bs.agron.2017.01.003Fountas, S., Carli, G., Sørensen, C. G., Tsiropoulos, Z., Cavalaris, C., Vatsanidou, A., … Tisserye, B. (2015). Farm management information systems: Current situation and future perspectives. Computers and Electronics in Agriculture, 115, 40-50. doi:10.1016/j.compag.2015.05.011Precision Agriculture in Europe: Legal, Social and Ethical Considerations—Think Tankhttp://www.europarl.europa.eu/thinktank/en/document.html?reference=EPRS_STU(2017)60320

Edge IoT Driven Framework for Experimental Investigation and Computational Modeling of Integrated Food, Energy, and Water System

As the global population soars from today’s 7.3 billion to an estimated 10 billion by 2050, the demand for Food, Energy, and Water (FEW) resources is expected to more than double. Such a sharp increase in demand for FEW resources will undoubtedly be one of the biggest global challenges. The management of food, energy, water for smart, sustainable cities involves a multi-scale problem. The interactions of these three dynamic infrastructures require a robust mathematical framework for analysis. Two critical solutions for this challenge are focused on technology innovation on systems that integrate food-energy-water and computational models that can quantify the FEW nexus. Information Communication Technology (ICT) and the Internet of Things (IoT) technologies are innovations that will play critical roles in addressing the FEW nexus stress in an integrated way. The use of sensors and IoT devices will be essential in moving us to a path of more productivity and sustainability. Recent advancements in IoT, Wireless Sensor Networks (WSN), and ICT are one lever that can address some of the environmental, economic, and technical challenges and opportunities in this sector. This dissertation focuses on quantifying and modeling the nexus by proposing a Leontief input-output model unique to food-energy-water interacting systems. It investigates linkage and interdependency as demand for resource changes based on quantifiable data. The interdependence of FEW components was measured by their direct and indirect linkage magnitude for each interaction. This work contributes to the critical domain required to develop a unique integrated interdependency model of a FEW system shying away from the piece-meal approach. The physical prototype for the integrated FEW system is a smart urban farm that is optimized and built for the experimental portion of this dissertation. The prototype is equipped with an automated smart irrigation system that uses real-time data from wireless sensor networks to schedule irrigation. These wireless sensor nodes are allocated for monitoring soil moisture, temperature, solar radiation, humidity utilizing sensors embedded in the root area of the crops and around the testbed. The system consistently collected data from the three critical sources; energy, water, and food. From this physical model, the data collected was structured into three categories. Food data consists of: physical plant growth, yield productivity, and leaf measurement. Soil and environment parameters include; soil moisture and temperature, ambient temperature, solar radiation. Weather data consists of rainfall, wind direction, and speed. Energy data include voltage, current, watts from both generation and consumption end. Water data include flow rate. The system provides off-grid clean PV energy for all energy demands of farming purposes, such as irrigation and devices in the wireless sensor networks. Future reliability of the off-grid power system is addressed by investigating the state of charge, state of health, and aging mechanism of the backup battery units. The reliability assessment of the lead-acid battery is evaluated using Weibull parametric distribution analysis model to estimate the service life of the battery under different operating parameters and temperatures. Machine learning algorithms are implemented on sensor data acquired from the experimental and physical models to predict crop yield. Further correlation analysis and variable interaction effects on crop yield are investigated

Towards In-situ Based Printed Sensor Systems for Real-Time Soil-Root Nutrient Monitoring and Prediction with Polynomial Regression

This dissertation explores how to increase sensor density in the agricultural framework using low-cost sensors, while also managing major bottlenecks preventing their full commercial adoption for agriculture, accuracy and drift. It also investigated whether low-cost biodegradable printed sensor sheets can result in improved stability, accuracy or drift for use in precision agriculture. In this dissertation, multiple electrode systems were investigated with much of the work focused on printed carbon graphene electrodes (with and without nanoparticles). The sensors were used in two configurations: 1) in varying soil to understand sensor degradation and the effect of environment on sensors, and 2) in plant pod systems to understand growth. It was established that 3) the sensor drift can be controlled and predicted 2) the fabricated low-cost sensors work as well as commercial sensors, and 3) these sensors were then successfully validated in the pod platform. A standardized testing system was developed to investigate soil physicochemical effects on the modified nutrient sensors through a series of controlled experiments. The construct was theoretically modeled and the sensor data was matched to the models. Supervised machine learning algorithms were used to predict sensor responses. Further models produced actionable insight which allowed us to identify a) the minimal amounts of irrigation required and b) optimal time after applying irrigation or rainfall event before achieving accurate sensor readings, both with respect to sensor depth placement within the soil matrix. The pore-scale behavior of solute transport through different depths within the sandy soil matrix was further simulated using COMSOL Multi-physics. This work leads to promising disposable printed systems for precision agriculture