African Observatory on Responsible AI’s Post

🌱 AI for a Sustainable Future in Agriculture Economics & Natural Resources Artificial Intelligence is transforming how we understand, analyze, and manage the interconnected systems of agriculture, natural resources, and the environment. By integrating diverse data sources and advanced analytical tools, AI helps us move from observation to prediction, and from prediction to more informed and sustainable decision-making. From forecasting market dynamics and agricultural yields to valuing ecosystem services, monitoring land and water resources, and assessing policy impacts, AI enables deeper insights, greater accuracy, and more effective strategies for sustainability. For young researchers, this is a remarkable opportunity to: ✅ Work with diverse and real-world datasets ✅ Apply advanced methods to practical and meaningful questions ✅ Collaborate across disciplines and research fields ✅ Generate evidence that supports policy and practice The real challenge is not only to use AI, but to use it responsibly, transparently, and ethically, with a clear understanding of context, uncertainty, and consequences. Let us use the power of AI to build resilient food systems, protect natural resources, and support inclusive and sustainable rural futures. #AgEcon #AIinAgriculture #NaturalResources #Sustainability #YoungResearchers #DataForGood #EvidenceBasedPolicy #FoodSecurity #ClimateAction #AcademicResearch majid.Zadmirzai@gmail.com For academic use and knowledge sharing.

AI Salon — June 3 | 4:15–7:15 PM PT (In Person) Santa Cruz Mountains Stewardship Network x San Jose Water Convened by the Santa Cruz Mountains Stewardship Network, this small, curated salon brings practitioners together to explore how AI can be applied (carefully and responsibly) to real stewardship and environmental management challenges. The session is designed to be hands-on and candid: Define a real stewardship problem together Use AI as a structured analytical tool, not a black box Critically evaluate what it helps with—and where it falls short This salon is about building shared literacy in the AI space, openly surfacing skepticism, and strengthening our collective ability to make values-aligned decisions about emerging tools. 📍 San Jose Water, Bascom Campus (San Jose) 🪑 Limited capacity by design If you’re interested in joining the conversation, message me directly for details.

Someone recently challenged me to think about the water impact of cattle in the Klamath River watershed relative to the growing impact of AI data centers. It was a sort of stumbly argument on my part, but I felt like I left with new direction in my thinking. Water use from domestic cattle production is high. However, it is a relatively stable volume and there have been practitioners focused on this problem for decades. I have worked with ranchers throughout Santa Barbara and Ventura County for years and know that in our region there is deep genuine concern for resource conservation and maintaining agricultural livelihoods. Data centers on the other hand are expanding their footprints rapidly and the pace is accelerating with every AI query, every model training run, every new campus breaking ground. I went digging around and I found the following paper: "Making AI Less 'Thirsty': Uncovering and Addressing the Secret Water Footprint of AI Models" Li, Yang, Islam & Ren (UC Riverside / UT Arlington, 2023) 📄 arxiv.org/abs/2304.03271 — also published in Communications of the ACM (2025) It projects global AI water demand hitting 4.2–6.6 billion cubic meters of withdrawal by 2027. That is 3,405,000 acre feet. The optimistic counterargument is that data center cooling innovation could actually spur breakthroughs in energy and water efficiency that society has long dreamed about, such as fusion. I believe that's possible. But the same research shows that right now, most major operators are still defaulting to the least efficient, highest-impact form of direct water cooling which is evaporative systems pulling from potable municipal supplies. The real takeaway for me wasn't who uses more water. It was this: the definition of water conservation is going to have to expand. Investors who care about natural capital will need to be active in a blended solution space that includes land conservation, natural capital markets, massive advancements in energy and climate tech, and vibrant regional agricultural economies. More than anything it is going to take new kinds of thinking and new kinds of coalitions that bring farmers, conservationists, technology companies, and capital together to help create a landscape where people can flourish in connection with the earth even as we continue to advance.   ~If you have a better understanding of the available data streams for tracking data center water consumption, please let me know — I'd love to start dashboarding this for folks.~

🔎 Project – Enhancing transparency of AI and machine learning in chemical risk assessment How can we make AI and machine learning tools in chemical risk assessment more transparent, reliable and suitable for regulatory use? AI and machine learning are increasingly used to predict chemical behaviour and support risk assessment. However, limited transparency and unclear documentation make it difficult to understand models and their uncertainties. This project aims to improve the transparency, reproducibility and regulatory acceptance of AI and ML approaches in chemical risk assessment, while supporting a shift away from animal testing. 📌 What the project is doing: ✔ Reviewing AI, ML and QSAR models used in risk assessment ✔ Identifying best practices for transparency and regulatory use ✔ Assessing reproducibility and uncertainty handling ✔ Exploring key barriers such as applicability domains ✔ Making selected models available as FAIR data via QsarDB.org 💡 Key results: ➡️ Better understanding of AI/ML model transparency ➡️ Improved handling of uncertainty and reproducibility ➡️ FAIR access to selected models ➡️ Support for regulatory acceptance of AI-based methods 🔗 Learn more: https://lnkd.in/e3eGbWhf Project partners: INERIS (FR), Istituto Mario Negri (IT), Istituto Superiore di Sanità (IT), Swedish Chemicals Agency (SE), Norwegian Institute of Public Health (NO), The Norwegian Institute for Water Research (NIVA) (NO), Uniwersytet Gdański (PL), UK Centre for Ecology & Hydrology (UKCEH) (GB), Environment Agency (GB), University of Basel (CH), University of Tartu (EE) #EU_PARC #ArtificialIntelligence #MachineLearning #ChemicalRiskAssessment #NAMs #NonAnimalTesting #HumanHealth #ScienceToPolicy

Excited to have participated in the IEEE Conference on Artificial Intelligence at Universidad de Granada, discussing how AI is transforming agriculture from the field to the market. It was a pleasure to share this conversation with Jesús Rodrigo Comino from Universidad de Granada and Gonzalo Martín Díaz from Hispatec - Agrointeligencia, moderated by J. Carlos Calvo Tudela from nazaríes intelligenia From computer vision and data analysis to traceability, forecasting and decision-making, AI is opening new possibilities for a more efficient, resilient and sustainable agri-food sector. Technology alone is not enough — the real value comes when data, operational knowledge and people work together. 🌱🤖 #AI #Agriculture #AgriTech #Innovation #DigitalTransformation #IEEE

》》》This visual represents the powerful convergence of Artificial Intelligence (AI) and Modern Biology, where data-driven intelligence meets the complexity of life. The image illustrates how machine learning, genomics, and cellular biology work together to decode biological systems, accelerate discovery, and design smarter solutions for healthcare, agriculture, and sustainability. By integrating big data, predictive modeling, and biotechnology, AI transforms raw biological information into actionable knowledge—enabling precision medicine, supporting biodiversity, and shaping a healthier, smarter, and more sustainable future. _______________________________ ■Key highlights ▪︎AI & Machine Learning → Decoding complex biological data ▪︎Genomics & Omics → Understanding the code of life ▪︎Cell Biology → Insights at the smallest biological scale ▪︎Predictive Modeling → Simulating life processes for discovery ▪︎Biotechnology → Innovation in health, agriculture, and environment ▪︎Precision Medicine → Personalized and data-driven healthcare ▪︎Biodiversity & Sustainability → Protecting life and the future

It is my pleasure to inform that my Scholar KIRAN MYSA has publised a conference paper in "2025 IEEE International Conference on Sustainable Communication Networks and Application (ICSCN)" Date of Conference: 15-17 October 2025 DOI: 10.1109/ICSCN67106.2025.11308356 Date Added to IEEE Xplore: 30 December 2025 Publisher: IEEE Now it is indexed in Scopus database. https://lnkd.in/gkBFnrC2 The summary of the article is: "Indian agriculture suffers from rigid pricing, lack of transparency, and unequal market access, reducing farmer incomes. The proposed Digital Market, a cloud-based AI platform, enhances transparency, efficiency, and fairness. It enables real-time transactions, price forecasting, and demand prediction, empowering farmers to make informed decisions, improve market access, eliminate intermediaries, and increase earnings nationwide." SR University CS&AI -SRU #SRUniversity #CSCS&AI -SRU #DigitalMarket #AI #Prediction #forcasting #realtime #Agriculture

🌍 Can AI Help Feed the World? 🌾 Researchers at the University of Virginia are taking on one of the most urgent challenges of our time: climate-driven food insecurity. A new UVA Environmental Institute Climate Collaborative led by Molly Lipscomb in the University of Virginia Frank Batten School of Leadership and Public Policy and Terence Johnson in the UVA School of Data Science is partnering with farmers, researchers, and extension workers in Lesotho, Southern Africa to explore how responsibly designed AI can help smallholder farmers adapt to increasingly volatile growing conditions. Their goal: build a customized AI tool that translates existing, science-backed agricultural guidance—often buried in complex documents—into timely, accessible, and locally relevant insights for farmers. By working alongside the National University of Lesotho and 4D Climate Solutions, the team is ensuring the technology is grounded in community needs, expert knowledge, and real-world constraints like rural connectivity. As climate change accelerates, smallholder farmers—already farming at the margins—face disproportionate risks. This project asks a critical question: Can AI, when developed carefully and collaboratively, help close the gap between knowledge and impact?

🚀 Excited to share my latest research publication! I’m thrilled to announce that my paper titled 📄 “AI-Driven Seasonal Crop Disease Prediction with Economic Impact” has been successfully published in the International Journal of Information Science and Computing (IJISC). 🔗 Read the full paper here: https://lnkd.in/dQSYyTUW This work explores how AI and deep learning (LSTM, TCN, and advanced time-series models) can be leveraged to predict seasonal crop diseases in advance, enabling preventive action instead of reactive treatment. 🌱 Key highlights of our research: Achieved >85% prediction accuracy across multiple crop-disease systems Integrated meteorological, phenological, and pathological data for robust forecasting Demonstrated significant economic benefits (300–500% ROI) through early intervention Showcased real-world case studies like rice blast, tomato late blight, and wheat rust 💡 The goal is simple yet impactful: Use AI to enhance food security, reduce unnecessary pesticide usage, and improve farmers’ economic outcomes. I would thank my co-author Vyomika Anand who helped me in this work 🙌 📌 This is just the beginning—looking forward to pushing further into AI for sustainable agriculture and real-world impact. #AI #MachineLearning #DeepLearning #AgricultureTech #PrecisionAgriculture #FoodSecurity #Research #Sustainability #TimeSeries #LSTM #TCN

It's truly rewarding to share that my book Artificial Intelligence in Food Science has been reviewed by the Institute of Food Technologists in FST Magazine. I am extremely delighted by the overwhelmingly positive response and thoughtful appreciation of the work. The review highlights the book’s comprehensive coverage of AI and ML applications across the food system — from ingredient discovery and product development to nutrition, quality control, sustainability, ethics, and future trends. My sincere thanks to Nicole Potenza Denis for being a constant support in this journey. I am grateful for sharing this recognition, and to the reviewers for their valuable insights. The growing scholarly and industry interest in the transformative role of artificial intelligence within the global food sector drives as significant motivation for my continued contribution to the literature in this domain. Thanks to my research fraternity for this constant motivation. #foodtechnology #AI #foodscience