Smart Connected Farms and Networked Farmers to Tackle Climate Challenges Impacting Agricultural Production (2312.12338v1)
Abstract: To meet the grand challenges of agricultural production including climate change impacts on crop production, a tight integration of social science, technology and agriculture experts including farmers are needed. There are rapid advances in information and communication technology, precision agriculture and data analytics, which are creating a fertile field for the creation of smart connected farms (SCF) and networked farmers. A network and coordinated farmer network provides unique advantages to farmers to enhance farm production and profitability, while tackling adverse climate events. The aim of this article is to provide a comprehensive overview of the state of the art in SCF including the advances in engineering, computer sciences, data sciences, social sciences and economics including data privacy, sharing and technology adoption.
- Climate change risks pushing one-third of global food production outside the safe climatic space. One Earth, 4(5):720–729, 2021.
- Anupama Mahato. Climate change and its impact on agriculture. International Journal of Scientific and Research Publications, 4(4):1–6, 2014.
- Internet of things. Academic Press, 2014.
- Jeffrey M Perkel. The internet of things comes to the lab. Nature, 542(7639):125–126, 2017.
- Smart agriculture wireless sensor routing protocol and node location algorithm based on internet of things technology. IEEE Sensors Journal, 21(22):24967–24973, 2020.
- KA Patil and NR Kale. A model for smart agriculture using iot. In 2016 international conference on global trends in signal processing, information computing and communication (ICGTSPICC), pages 543–545. IEEE, 2016.
- N Sakthipriya. An effective method for crop monitoring using wireless sensor network. Middle-East Journal of Scientific Research, 20(9):1127–1132, 2014.
- GV Satyanarayana and SD Mazaruddin. Wireless sensor based remote monitoring system for agriculture using zigbee and gps. In Conference on advances in communication and control systems, volume 3, pages 237–241, 2013.
- Big data and ai revolution in precision agriculture: Survey and challenges. IEEE Access, 9:110209–110222, 2021.
- Agrisens: Iot-based dynamic irrigation scheduling system for water management of irrigated crops. IEEE Internet Things Journal, 8(6):5023–5030, 2021.
- The utility maximization for data collection in uav-enabled sensor networks via judiciously determining hovering locations. 19th IEEE International Conference on Pervasive Computing and Communications (PerCom), pages 1–10, 2021.
- COALESCE. https://coalesce.me.iastate.edu/, 2021a. Accessed: 2022-11-4.
- A novel multirobot system for plant phenotyping. Robotics, 7(4):61, 2018.
- Cyber-agricultural systems for crop breeding and sustainable production. Trends in Plant Science, 2023.
- Economics of robots and automation in field crop production. Precision Agriculture, 21(2):278–299, 2020.
- Moving from uniform to variable fertilizer rates on iowa corn: Effects on rates and returns. Journal of Agricultural and Resource Economics, pages 385–400, 1998.
- Precision agriculture and sustainability. Precision agriculture, 5(4):359–387, 2004.
- Smart poultry management: Smart sensors, big data, and the internet of things. Computers and Electronics in Agriculture, 170:105291, 2020.
- Design and implementation of the span greenhouse agriculture internet of things system. In 2015 International Conference on Fluid Power and Mechatronics (FPM), pages 398–401. IEEE, 2015.
- Impact of wild blueberry harvesters on weed seed dispersal within and between fields. Weed Science, 57(5):541–546, 2009.
- Primary and secondary pesticide drift profiles from a peach orchard. Chemosphere, 177:303–310, 2017.
- On-farm performance and farmers’ perceptions of droughttego-climate-smart maize hybrids in kenya. 2019.
- Keith Cressman. Role of remote sensing in desert locust early warning. Journal of Applied Remote Sensing, 7(1):075098, 2013.
- Smart integrated farm network for rural agricultural communities (SIRAC). https://sirac.agron.iastate.edu/, 2021b. Accessed: 2022-12-5.
- Peter M Kyveryga. On-farm research: Experimental approaches, analytical frameworks, case studies, and impact. Agronomy Journal, 111(6):2633–2635, 2019.
- Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment, 236:111402, 2020.
- Katherine LoPiccalo. Impact of broadband penetration on u.s. farm productivity. OEA Working Paper 50 Office of Economics and Analysis FCC, 2021.
- E-Rate - schools & libraries USF program. https://www.fcc.gov/general/e-rate-schools-libraries-usf-program, April 2012a. Accessed: 2022-11-4.
- Lifeline program for Low-Income consumers. https://www.fcc.gov/general/lifeline-program-low-income-consumers, January 2012b. Accessed: 2022-11-4.
- Connect america fund (CAF). https://www.fcc.gov/general/connect-america-fund-caf, April 2012c. Accessed: 2022-11-4.
- Rural health care program. https://www.fcc.gov/general/rural-health-care-program, November 2010. Accessed: 2022-11-4.
- Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding. Crop Science, 63(4):1722–1749, 2023.
- David Shepardson. FCC awards $9.2 billion to deploy broadband to 5.2 million U.S. homes, businesses. Reuters, December 2020.
- High-throughput field phenotyping of leaves, leaf sheaths, culms and ears of spring barley cultivars at anthesis and dough ripeness. Frontiers in plant science, 8:1920, 2017.
- Uas-based plant phenotyping for research and breeding applications. Plant Phenomics, 2021, 2021.
- Identification of soybean foliar diseases using unmanned aerial vehicle images. IEEE Geoscience and Remote Sensing Letters, 14(12):2190–2194, 2017.
- Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean. Frontiers in plant science, 13, 2022a.
- Computer vision and machine learning for robust phenotyping in genome-wide studies. Scientific Reports, 7(1):1–11, 2017.
- Soybean iron deficiency chlorosis high-throughput phenotyping using an unmanned aircraft system. Plant methods, 15(1):1–9, 2019.
- A real-time phenotyping framework using machine learning for plant stress severity rating in soybean. Plant methods, 13(1):23, 2017.
- Uav-based crop and weed classification for smart farming. In 2017 IEEE International Conference on Robotics and Automation (ICRA), pages 3024–3031. IEEE, 2017.
- High-throughput phenotyping of soybean maturity using time series uav imagery and convolutional neural networks. Remote Sensing, 12(21):3617, 2020.
- Automatic uav-based counting of seedlings in sugar-beet field and extension to maize and strawberry. Computers and Electronics in Agriculture, 191:106493, 2021.
- Cotton yield estimation model based on machine learning using time series uav remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 104:102511, 2021.
- Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems. Plant methods, 14(1):1–13, 2018.
- Generating 3d hyperspectral information with lightweight uav snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. ISPRS Journal of Photogrammetry and Remote Sensing, 108:245–259, 2015.
- Remote thermal infrared imaging for rapid screening of sudden death syndrome in soybean. In 2018 ASABE Annual International Meeting, page 1. American Society of Agricultural and Biological Engineers, 2018.
- Unmanned aerial system and satellite-based high resolution imagery for high-throughput phenotyping in dry bean. Computers and Electronics in Agriculture, 165:104965, 2019.
- Thermal imaging to monitor the crop-water status in almonds by using the non-water stress baselines. Scientia Horticulturae, 238:91–97, 2018.
- The daily erosion project–daily estimates of water runoff, soil detachment, and erosion. Earth Surface Processes and Landforms, 43(5):1105–1117, 2018.
- Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using uav lidar and multispectral imaging. International Journal of Applied Earth Observation and Geoinformation, 92:102177, 2020.
- K. Li. Computation offloading strategy optimization with multiple heterogeneous servers in mobile edge computing. IEEE Transactions on Sustainable Computing, pages 1–1, 2019.
- Sustainable deep learning at grid edge for real-time high impedance fault detection. IEEE Transactions on Sustainable Computing, pages 1–1, 2018.
- Distributed deep learning for persistent monitoring of agricultural fields. In NeurIPS 2021 AI for Science Workshop, 2021a.
- Convergence of edge computing and deep learning: A comprehensive survey. IEEE Communications Surveys Tutorials, pages 1–1, 2020.
- Mobile cloud computing with voltage scaling and data compression. In Proc. SPAWC, pages 1–5, 2017.
- Collaborative deep learning in fixed topology networks. Advances in Neural Information Processing Systems, 30, 2017.
- Cross-gradient aggregation for decentralized learning from non-iid data. In International Conference on Machine Learning, pages 3036–3046. PMLR, 2021b.
- An efficient on-line computation offloading approach for large-scale mobile edge computing via deep reinforcement learning. IEEE Transactions on Services Computing (Special Issue on Edge AI As-a-Service), page to appear, 2022.
- Dlsense: Distributed learning-based smart virtual sensing for precision agriculture. IEEE Sensors Journal, 21(16):17556–17563, 2020.
- A survey on fog computing for the internet of things. Pervasive and Mobile Computing, 52:71–99, 2019.
- Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
- Crop yield prediction integrating genotype and weather variables using deep learning. Plos one, 16(6):e0252402, 2021a.
- Leveraging genomic prediction to scan germplasm collection for crop improvement. PloS one, 12(6):e0179191, 2017.
- Patriot: A pipeline for tracing identity-by-descent for chromosome segments to improve genomic prediction in self-pollinating crop species. Frontiers in plant science, page 2095, 2021b.
- Energy-efficient edge-fog-cloud architecture for iot-based smart agriculture environment. IEEE Access, 9:110480–110492, 2021.
- bioMCS 2.0: A distributed, energy-aware fog-based framework for data forwarding in mobile crowdsensing. Pervasive and Mobile Computing, 73: 101381, 2021.
- Improving iot data quality in mobile crowdsensing: A cross validation approach. IEEE Internet of Things Journal, 6(3):5651–5664, 2019.
- Energy efficient data forwarding scheme in fog based ubiquitous system with deadline constraints. IEEE Transactions on Network and Service Management, 17(1):213–226, 2020.
- Quantitative analysis of deep leaf: a plant disease detector on smart edge. In IEEE Conference on Smart Computing (SMARTCOMP), 2020.
- Porting deep neural networks on the edge via dynamic k-means compression: A case study of plant disease detection. Pervasive and Mobile Computing, 75:101437, 2021.
- A drone-based application for scouting halyomorpha halys bugs in orchards with multifunctional nets. In 20th IEEE International Conference on Pervasive Computing and Communications (PerCom) Demo, 2022a.
- Drone-based optimal and heuristic orienteering algorithms towards bug detection in orchards. In 18th International Conference on Distributed Computing in Sensor Systems (DCOSS), 2022b.
- Daknet: Rethinking connectivity in developing nations. Computer, 37(1):78–83, 2004.
- Jaldimac: taking the distance further. In Proceedings of the 4th ACM workshop on networked systems for developing regions, page 2. ACM, 2010.
- The village base station. In Proceedings of the 4th ACM Workshop on Networked Systems for Developing Regions, page 14. ACM, 2010.
- Lifenet: a flexible ad hoc networking solution for transient environments, volume 41. ACM, 2011.
- Designing delay constrained hybrid ad hoc network infrastructure for post-disaster communication. Ad Hoc Networks, 25:406–429, 2015.
- Gsm whitespaces: An opportunity for rural cellular service. In Dynamic Spectrum Access Networks (DYSPAN), 2014 IEEE International Symposium on, pages 271–282. IEEE, 2014.
- A Survey on LoRa Networking: Research Problems, Current Solutions, and Open Issues. IEEE Communications Surveys Tutorials, 22(1):371–388, 2020.
- An energy efficient smart metering system using edge computing in lora network. IEEE Transactions on Sustainable Computing (Special Issue on Energy-Efficient Edge Computing), page to appear, 2022.
- Designing sustainable smart connected communities using dynamic spectrum access via band selection. In Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments, page 12. ACM, 2017.
- Designing green communication systems for smart and connected communities via dynamic spectrum access. ACM Transactions on Sensor Networks (TOSN), 14(3-4):31, 2018.
- Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer networks, 50(13):2127–2159, 2006.
- Dynamic spectrum access: from cognitive radio to network radio. IEEE Wireless Communications, 19(1), 2012.
- 2010.
- Feasibility, architecture and cost considerations of using tvws for rural internet access in 5g. In Innovations in Clouds, Internet and Networks (ICIN), 2017 20th Conference on, pages 23–30. IEEE, 2017.
- White space networking with wi-fi like connectivity. ACM SIGCOMM Computer Communication Review, 39(4):27–38, 2009.
- Licensed spectrum sharing schemes for mobile operators: A survey and outlook. IEEE Communications Surveys & Tutorials, 18(4):2591–2623, 2016.
- 2015. Last accessed on Jan, 2019.
- I. Canada. Canada whitespace. http://www.ic.gc.ca/eic/site/smt-gst.nsf/eng/sf10928.html. a. Last accessed on Jan, 2019.
- Imda. Singapore whitespace. https://www.imda.gov.sg/regulations-licensing-and-consultations/frameworks-and-policies/spectrum-management-and-coordination/spectrum-planning/tv-white-space. b.
- Tenet. South Africa Whitespace. https://www.tenet.ac.za/tvws. a.
- To white space or not to white space: That is the trial within the ofcom tv white spaces pilot. In Dynamic Spectrum Access Networks (DySPAN), 2015 IEEE International Symposium on, pages 11–22. IEEE, 2015.
- mtsfb. Malaysia whitespaceo. www.mtsfb.org.my/working-group/tv-white-space-ws-wg.
- Techweez. Kenya Whitespace. www.techweez.com/2016/10/31/ca-white-spaces-rules/. b.
- Cran. Namibia Whitespace. https://www.cran.na/images/docs/GGs/5480-Gen_N150.pdf. c.
- Techweez. Namibia Whitespace. http://techweez.com/2014/01/15/microsoft-whitespaces-broadband-namibia/. d.
- ENACOM. Argentina Whitespace. https://www.enacom.gob.ar/. e.
- Toward enabling broadband for a billion plus population with tv white spaces. IEEE Communications Magazine, 54(7):28–34, 2016.
- Cognitive radio on tv bands: a new approach to provide wireless connectivity for rural areas. IEEE Wireless Communications, 15(3), 2008.
- Lte-advanced in white space: A complementary technology. Radisys White Paper, 2011.
- Plant disease identification using explainable 3d deep learning on hyperspectral images. Plant methods, 15(1):98, 2019.
- A deep learning framework to discern and count microscopic nematode eggs. Scientific reports, 8(1):1–11, 2018.
- Self-supervised learning improves agricultural pest classification. In AI for Agriculture and Food Systems, 2021.
- Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean. Frontiers in Plant Science, 13, 2022b.
- Challenges and opportunities in machine-augmented plant stress phenotyping. Trends in Plant Science, 26(1):53–69, 2021.
- Exploring the use of 3d point cloud data for improved plant stress rating. In AI for Agriculture and Food Systems, 2021.
- An explainable deep machine vision framework for plant stress phenotyping. Proceedings of the National Academy of Sciences, 115(18):4613–4618, 2018.
- Comparative prediction accuracy of hyperspectral bands for different soybean crop variables: From leaf area to seed composition. Field Crops Research, 271:108260, 2021.
- Using large soybean historical data to study genotype by environment variation and identify mega-environments with the integration of genetic and non-genetic factors. bioRxiv, 2022.
- Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute, 2011.
- M Kunisch. Big data in agriculture–perspectives for a service organization. Landtechnik, 71(1):1–3, 2016.
- A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143:23–37, 2017.
- Big data in smart farming–a review. Agricultural systems, 153:69–80, 2017.
- Development of optimized phenomic predictors for efficient plant breeding decisions using phenomic-assisted selection in soybean. Plant Phenomics, 2019:5809404, 2019a.
- Machine learning approach for prescriptive plant breeding. Scientific reports, 9(1):1–12, 2019b.
- A weakly supervised deep learning framework for sorghum head detection and counting. Plant Phenomics, 2019:1525874, 2019.
- Deep learning for plant stress phenotyping: trends and future perspectives. Trends in plant science, 23(10):883–898, 2018.
- A systematic literature review on deep learning applications for precision cattle farming. Computers and Electronics in Agriculture, 187:106313, 2021.
- Machine learning for high-throughput stress phenotyping in plants. Trends in plant science, 21(2):110–124, 2016.
- Deep multiview image fusion for soybean yield estimation in breeding applications. Plant Phenomics, 2021, 2021.
- Self-supervised learning improves classification of agriculturally important insect pests in plants. The Plant Phenome Journal, 6(1):e20079, 2023.
- Agri-gnn: A novel genotypic-topological graph neural network framework built on graphsage for optimized yield prediction. arXiv preprint arXiv:2310.13037, 2023.
- When machine learning meets privacy: A survey and outlook. ACM Computing Surveys (CSUR), 54(2):1–36, 2021.
- Privacy preservation in distributed deep learning: A survey on distributed deep learning, privacy preservation techniques used and interesting research directions. Journal of Information Security and Applications, 61:102949, 2021.
- Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492, 2016.
- Towards federated learning at scale: System design. Proceedings of Machine Learning and Systems, 1:374–388, 2019.
- Federated learning for internet of things: A comprehensive survey. IEEE Communications Surveys & Tutorials, 23(3):1622–1658, 2021.
- A survey on federated learning systems: vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge and Data Engineering, 2021.
- Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1–2):1–210, 2021.
- A federated learning-based crop yield prediction for agricultural production risk management. In 2022 IEEE Delhi Section Conference (DELCON), pages 1–7. IEEE, 2022.
- Felids: Federated learning-based intrusion detection system for agricultural internet of things. Journal of Parallel and Distributed Computing, 165:17–31, 2022.
- Asynchronous decentralized sgd with quantized and local updates. Advances in Neural Information Processing Systems, 34:6829–6842, 2021.
- Privacy-aware adaptive data encryption strategy of big data in cloud computing. In 2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud), pages 273–278. IEEE, 2016.
- {{\{{GAZELLE}}\}}: A low latency framework for secure neural network inference. In 27th USENIX Security Symposium (USENIX Security 18), pages 1651–1669, 2018.
- Delphi: A cryptographic inference service for neural networks. In 29th USENIX Security Symposium (USENIX Security 20), pages 2505–2522, 2020.
- Privacy-preserving deep learning via additively homomorphic encryption. IEEE Transactions on Information Forensics and Security, 13(5):1333–1345, 2017.
- Privacy-preserving and verifiable deep learning inference based on secret sharing. Neurocomputing, 483:221–234, 2022.
- Cynthia Dwork. Differential privacy: A survey of results. In International conference on theory and applications of models of computation, pages 1–19. Springer, 2008.
- Tinygarble2: Smart, efficient, and scalable yao’s garble circuit. In Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice, pages 65–67, 2020.
- Privacy-preserving deep models for plant stress phenotyping. In AI for Agriculture and Food Systems, 2021a.
- Sphynx: Relu-efficient network design for private inference. arXiv preprint arXiv:2106.11755, 2021b.
- Distributed additive encryption and quantization for privacy preserving federated deep learning. Neurocomputing, 463:309–327, 2021.
- Privfl: Practical privacy-preserving federated regressions on high-dimensional data over mobile networks. In Proceedings of the 2019 ACM SIGSAC Conference on Cloud Computing Security Workshop, pages 57–68, 2019.
- Towards efficient and privacy-preserving federated deep learning. In ICC 2019-2019 IEEE international conference on communications (ICC), pages 1–6. IEEE, 2019.
- Privacy-preserving collaborative deep learning with unreliable participants. IEEE Transactions on Information Forensics and Security, 15:1486–1500, 2019.
- Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273–1282. PMLR, 2017.
- The role of cross-silo federated learning in facilitating data sharing in the agri-food sector. Computers and Electronics in Agriculture, 193:106648, 2022.
- Pefl: Deep privacy-encoding-based federated learning framework for smart agriculture. IEEE Micro, 42(1):33–40, 2021.
- Farmer Netwoek Design Manual, 2015.
- On-Farm Replicated Strip Trials, chapter 13, pages 189–207. John Wiley & Sons, Ltd, 2018. ISBN 9780891183679. doi:https://doi.org/10.2134/precisionagbasics.2016.0096. URL https://acsess.onlinelibrary.wiley.com/doi/abs/10.2134/precisionagbasics.2016.0096.
- A framework for visualization and analysis of agronomic field trials from on-farm research networks. Agronomy Journal, 111(6):2712–2723, 2019.
- Farmers as researchers: In-depth interviews to discern participant motivation and impact. Agronomy Journal, 111(6):2670–2680, 2019.
- Data sharing platforms: How value is created from agricultural data. Agricultural Systems, 193:103241, 2021.
- Farmers and their data: An examination of farmers’ reluctance to share their data through the lens of the laws impacting smart farming. NJAS-Wageningen Journal of Life Sciences, 90:100301, 2019.
- “if they don’t tell us what they do with it, why would we trust them?” trust, transparency and benefit-sharing in smart farming. NJAS-Wageningen Journal of Life Sciences, 90:100285, 2019.
- New technology, ethical tensions and the mediating role of translational research. Ethical Tensions from New Technology: The Case of Agricultural Biotechnology, 6:162, 2018.
- Between forestry and farming: Policy and environmental implications of the barriers to agroforestry adoption. Canadian Journal of Agricultural Economics/Revue canadienne d’agroeconomie, 60(2):155–175, 2012.
- Mark Shucksmith. Farm household behaviour and the transition to post-productivism. Journal of agricultural economics, 44(3):466–478, 1993.
- Future changes in british agriculture: projecting divergent farm household behaviour. Journal of Agricultural Economics, 53(1):37–50, 2002.
- Farmers, the practice of farming and the future of agroforestry: an application of bourdieu’s concepts of field and habitus. Rural Sociology, 68(1):64–86, 2003.
- The role of networks of practice and webs of influencers on farmers’ engagement with and learning about agricultural innovations. Journal of Rural Studies, 26(4):404–417, 2010.
- Using translational research to enhance farmers’ voice: a case study of the potential introduction of gm cassava in kenya’s coast. Agriculture and Human Values, 31(4):673–681, 2014.
- Jane L Glover. Capital usage in adverse situations: Applying bourdieu’s theory of capital to family farm businesses. Journal of Family and Economic Issues, 31(4):485–497, 2010.
- Human and institutional dimensions of agroforestry. North American Agroforestry, pages 489–519, 2021.
- Elinor Ostrom. A general framework for analyzing sustainability of social-ecological systems. Science, 325(5939):419–422, 2009.
- Family businesses and adaptation: A dynamic capabilities approach. Journal of family and economic issues, 39(4):683–698, 2018.
- Steven H Woolf. The meaning of translational research and why it matters. Jama, 299(2):211–213, 2008.
- Complexity in climate-change impacts: an analytical framework for effects mediated by plant disease. Plant Pathology, 60(1):15–30, 2011.
- The effects of climate variability and the color of weather time series on agricultural diseases and pests, and on decisions for their management. Agricultural and Forest Meteorology, 170:216–227, 2013.
- Local forecast communication in the altiplano. Bulletin of the American Meteorological Society, 90(1):85–92, 2009.
- Effects of seasonal climate forecasts and participatory workshops among subsistence farmers in zimbabwe. Proceedings of the National Academy of Sciences, 102(35):12623–12628, 2005.
- Human and institutional dimensions of agroforestry. North American agroforestry: an integrated science and practice, pages 339–367, 2009.
- Socio-ecological dimensions of andean pastoral landscape change: bridging traditional ecological knowledge and satellite image analysis in sajama national park, bolivia. Regional Environmental Change, 19(5):1353–1369, 2019.
- Learning by doing and learning from others: Human capital and technical change in agriculture. Journal of political Economy, 103(6):1176–1209, 1995.
- Farmers’ willingness to participate in a big data platform. Agribusiness, 36(1):20–36, 2020.
- Factors determining adoption of new agricultural technology by smallholder farmers in developing countries. Journal of Economics and sustainable development, 6(5), 2015.
- Uncertainty, learning, and technology adoption in agriculture. Applied Economic Perspectives and Policy, 42(1):42–53, 2020.
- The economics of risk, uncertainty and learning in the adoption of new agricultural technologies: where are we on the learning curve? Agricultural systems, 75(2-3):215–234, 2003.
- E Katung and Kenneth Akankwasa. Community-based organizations and their effect on the adoption of agricultural technologies in uganda: a study of banana (musa spp.) pest management technology. In IV International Symposium on Banana: International Conference on Banana and Plantain in Africa: Harnessing International 879, pages 719–726, 2008.
- Social networks and technology adoption in northern mozambique. The economic journal, 116(514):869–902, 2006.
- Inside the black box: technology and economics. cambridge university press, 1982.
- Empathy-conditioned conservation:“walking in the shoes of others” as a conservation farmer. Land Economics, 87(3):433–452, 2011.
- Will farmers trade profits for stewardship? heterogeneous motivations for farm practice selection. Land Economics, 84(1):66–82, 2008.
- Agricultural adoption and behavioral economics: Bridging the gap. Applied Economic Perspectives and Policy, 42(1):54–66, 2020.
- Social capital and public goods. The Journal of Socio-Economics, 39(4):474–481, 2010.
- Social capital and contributions in a public-goods experiment. American Economic Review, 94(2):373–376, 2004.
- Trust, voluntary cooperation, and socio-economic background: survey and experimental evidence. Journal of Economic Behavior & Organization, 55(4):505–531, 2004.
- Fragility of the provision of local public goods to private and collective risks. Proceedings of the National Academy of Sciences, 114(5):921–925, 2017.
- Cooperation and competition in intergenerational experiments in the field and the laboratory. American Economic Review, 99(3):956–78, 2009.
- Sabrina Teyssier. Inequity and risk aversion in sequential public good games. Public Choice, 151(1):91–119, 2012.
- On the provision of public goods with probabilistic and ambiguous thresholds. Environmental and Resource economics, 61(3):365–383, 2015.
- Elaine M Liu. Time to change what to sow: Risk preferences and technology adoption decisions of cotton farmers in china. Review of Economics and Statistics, 95(4):1386–1403, 2013.
- Risk pooling, risk preferences, and social networks. American Economic Journal: Applied Economics, 4(2):134–67, 2012.
- Robert E Lucas Jr. On the mechanics of economic development. Journal of monetary economics, 22(1):3–42, 1988.
- Daron Acemoglu. Introduction to modern economic growth. Privredna kretanja i ekonomska politika, 123:89, 2010.
- Learning about a new technology: Pineapple in ghana. American economic review, 100(1):35–69, 2010.
- K. M. Moore. Network framing of pest management knowledge and practice. Rural Sociology, 73(3):414–439, 2008.
- Neighbors and extension agents in ethiopia: Who matters more for technology adoption? American Journal of Agricultural Economics, 96(1):308–327, 2014.
- Input diffusion and the evolution of production networks. Technical report, National Bureau of Economic Research, 2014.
- Social learning and incentives for experimentation and communication. The Review of Economic Studies, 86(3):976–1009, 2019.
- Anthony Martin. Yield benefits from combined applications of foliar fungicides and insecticides on soybean. working paper, 2022.
- iNaturalist. inaturalist, 2023. URL https://www.inaturalist.org/. Accessed: 2023-12-18.
- Deep learning powered real-time identification of insects using citizen science data. arXiv preprint arXiv:2306.02507, 2023.
- Out-of-distribution detection algorithms for robust insect classification. arXiv preprint arXiv:2305.01823, 2023.