Crop disease detection

Amref Health Africa, a global health nonprofit, implemented an Azure AI-powered malnutrition forecasting tool in partnership with the Kenyan Ministry of Health, the University of Southern California, and Microsoft AI for Good Lab.The tool ingests large volumes of anonymized health data from Kenya's District Health Information System (DHIS2), satellite imagery, and other public datasets.AI models trained on historical health and environmental data forecast malnutrition severity and hotspots at sub-county level for 1, 3, and 6 months ahead.This enables Amref and partners to plan interventions, mobilize resources, and pre-position supplies ahead of droughts, floods, or food insecurity events.Healthcare workers gain critical, location-specific insights for early action, aiming to reduce malnutrition and its long-term effects in children and vulnerable groups.The collaborative effort demonstrates Microsoft Azure AI's power in transforming public health with predictive and preventive care.Amref plans to expand and replicate the AI solution to other countries and humanitarian crises where health data is available.

Amref Health AfricaHealthcare

Taranis, an agricultural technology company, uses AI, machine learning, and drone imaging to enable early detection of pests in crops, helping farmers minimize damage, increase yields, and reduce pesticide overuse.Their solution deploys drones and aircraft with high-resolution cameras to capture crop imagery, which is then analyzed by AI-powered machine learning algorithms to spot signs of pest activity.Taranis's system helps identify pest species, delivers real-time analysis, and gives actionable recommendations for targeted pesticide use.The platform reduces the need for manual scouting, saves labor, and improves the precision of pest management.Farmers leveraging Taranis have seen a reduction in pesticide use by 30% and are better able to protect high-value crops and optimize harvests.The system also supports sustainability goals through more efficient and responsible chemical use.

AgResearch in New Zealand leverages AI and remote sensing to improve biosecurity surveillance in the agriculture sector.The solution integrates high-resolution satellite imagery, drones, and Google Street View with computer vision and machine learning to map maize crops and identify biosecurity threats, such as Fall Armyworm and the invasive Tree of Heaven.Researchers combined over a million samples of satellite images with automated labeling techniques to create digital maps and inform pest management.The methodology employs LiDAR and drone photography, automatically labeling host plants using AI-driven plant identifiers like Pl@ntNet.Insights are provided to national authorities, including the Ministry for Primary Industries, enhancing surveillance strategy and resource focus.The scalable approach makes use of accessible, freely available imagery and advanced data integration to inform policy decisions.Collaboration with stakeholders, such as the Christchurch City Council Urban Forest Team, is highlighted.Impact includes informing long-term pest management, supporting sustainable agriculture and ecosystem protection, and providing a blueprint for others in biosecurity surveillance.

AgResearchAgriculture

AdaViv, an agricultural technology company, developed an adaptive indoor growing system integrating AI capabilities to address the efficiency and data fragmentation challenges faced by indoor farms. The system collects and centralizes sensor and production data on Microsoft Azure and leverages Azure AI and Azure ML to monitor plant growth, predict yields, detect plant diseases, and optimize environmental variables and resource allocation (light, water, nutrients, labor).The initiative was part of Microsoft’s AI for Earth program, focusing on sustainable, tech-powered food production. The AI-driven insights help automate repetitive farming tasks, centralize disparate production data, and enable real-time crop and environmental monitoring. This targeted approach allows even smaller growers to access advanced agricultural technology.Growers have achieved higher yields, improved quality control, and more profitable production cycles. The data-driven analysis supports sustainability by optimizing resource use and minimizing waste. The project demonstrates how AI and cloud technology can drive a significant, measurable impact on modern horticulture.

AdaVivAgriculture

Traditional manual methods for crop disease identification are slow, labor-intensive, and often inconsistent, impacting productivity and sustainability in agriculture.Agentic AI workflows on Databricks allow autonomous agents to analyze imagery and sensor data, detecting diseases, triggering alerts, recommending treatments, and enabling model auto-retraining.The solution uses Databricks AI platform components, including Azure, MLflow, Unity Catalog, Delta Lake, and Apache Spark.The full-agentic architecture ingests streams from drones, satellite imagery, IoT sensors, and mobile apps to process and classify plant diseases in near real-time.Agents operate on multi-agent architectures, collaborating for holistic diagnosis that incorporates environmental, soil, and disease factors.Real-time alerts integrate with dashboards, automating recommended crop protection interventions and scheduling drone inspections.Outcomes include a 40-60% acceleration in disease detection, up to 30% reduction in pesticide usage, improved yields, and higher classification consistency across large-scale operations.The architecture supports continuous feedback and automatic retraining, further improving accuracy and outcomes over time.

IAPrecision, a startup established in Nigeria in 2021, leverages advanced drone technology and Microsoft Azure AI to transform agricultural practices.Its offerings include precision mapping, crop monitoring, early pest/disease detection, automated seed sowing, and highly targeted drone spraying of crops.IAPrecision aims to reduce the environmental impact of farming by enabling more sustainable practices, decreasing reliance on manual labor and harmful chemicals.Using in-house assembled drones equipped with robust sensors, the company provides affordable, scalable tech for both commercial and smallholder farmers, and has flexible pricing to broaden access.The company partners with entities such as the International Institute of Tropical Agriculture and Agrobyte to further its impact.To date, IAPrecision has positively impacted over 300 farmers across more than 5,000 acres, with reported revenue exceeding $30,000 in its first year.

IAPrecisionAgriculture

Kenyan farmers have long faced challenges related to poor crop yields and low-quality produce, compounded by climate change, supply chain disruptions, and lack of access to agricultural best practices. A team led by Pius Mutuma developed FarmersSavvy, a user-friendly app leveraging Microsoft's AI Builder, SharePoint, and PowerApps. Through AI-driven crop image analysis, the app detects diseases and provides tailored cultivation advice, helping users identify issues early. SharePoint integration allows for efficient real-time data management, and PowerApps ensures accessibility for all technological proficiency levels. The initiative is designed as a scalable foundation, with future plans to enhance the platform with live chat and automation. The FarmersSavvy project has received positive feedback and demonstrated improved crop yields, higher produce quality, and farmer empowerment through technology adoption.FarmersSavvy uses AI Builder to analyze images of crops for disease, pest, or deficiency detection. The data is stored and managed in SharePoint, facilitating seamless record-keeping and analysis. The app's personalized recommendations are tailored to each farmer's crops and circumstances, supporting best practices like soil prep, fertilization, irrigation, and harvesting.Real-time insights from the platform have enabled farmers to adjust agricultural operations, leading to increased productivity and improved market acceptance of their produce, including potatoes previously rejected by KFC.Future enhancements to the platform include an AI-powered chatbot and further workflow automation.

FarmersAgriculture

Cropin Technology Solutions, an Indian agritech company, developed Cropin Cloud—an AI-powered agriculture industry cloud. The platform unifies farm and ecosystem data from numerous sources, including IoT devices, drones, financial institutions, and more, harnessing AI and machine learning to predict crop health, disease risk, and yields. Cropin Cloud supports localized and crop-agnostic deployment worldwide, making farming operations efficient, predictable, and sustainable. The journey began with the company's drive to systematically digitize agriculture and address challenges like climate variability and decentralized data, drawing on the founder’s manufacturing philosophy: treat every farm like a factory. Cropin now operates in over 50 countries and supports stakeholders ranging from farmers to insurers, providing actionable insights at scale. Integration with external data sets further augments the intelligence delivered to farmers. Their technology delivers traceability, optimized risk, improved crop quality, and supports sustainable agriculture. Financial backing from the Bill & Melinda Gates Foundation signals huge impact potential.Cropin’s early milestones included building a standard operating procedure to manage farm assets and risks, followed by developing a location- and crop-agnostic platform for global scalability. The addition of the Data Hub allows rapid ingestion and unification of various data types; the Intelligence Platform leverages AI/ML for proactive decision-making. Now, Cropin Cloud is the first full agriculture ecosystem cloud solution, allowing banks, manufacturers, and other stakeholders to participate. Stakeholders gain predictive insights and the ability to make decisions based on unified real-time data, scaling digital transformation across agriculture worldwide.

Cropin Technology Solutions Pvt. LtdAgriculture

Cloud Agronomics, a Colorado-based agtech company, implemented an AI-powered hyperspectral imaging solution that revolutionizes soil carbon monitoring and crop disease detection for large-scale agribusiness. Their unique system uses aircraft-mounted hyperspectral sensors to collect massive amounts of data per flight, capturing insights into soil carbon, nutrients, and disease presence with significantly improved accuracy compared to traditional soil sampling. Partnering with Microsoft’s AI for Earth, Cloud Agronomics is expanding its offering into Brazil, leveraging Microsoft’s AI capabilities for advanced analytics on aerial imagery and agronomic data. The system is targeted at agribusinesses, insurers, and government agencies, helping them measure and monitor soil health, crop diseases, and optimize fertilizer use at scale. The AI solution demonstrated less than 10% error rate in soil carbon estimation versus up to 50–60% error in traditional methods. The expansion supports global scalability, environmental stewardship, and offers year-round analytics for varied crop types, delivering high-value, actionable insights to agricultural stakeholders.

Cloud AgronomicsAgriculture

Land O'Lakes, a leading American farmer-owned cooperative, partnered with Microsoft for a multi-year strategic alliance to transform agriculture through advanced technologies.Farmers in the cooperative face unpredictable weather, trade disruptions, climate change, and market pressures, all contributing to financial instability and reduced productivity.Land O'Lakes leveraged Microsoft Azure, AI, and machine learning to turn decades of manually gathered data into actionable intelligence for farmers across more than 300,000 member producers.Through digital agritech solutions powered by Azure, the cooperative delivers hyper-local, field-specific recommendations on crop planting, nutrient optimization, and disease detection.The alliance prioritizes sustainability by enabling more targeted and environmentally sound fertilization, while also supporting crop diversity and reducing waste.AI and machine learning algorithms move decision-making from intuition to optimized, data-driven field management, helping even small farms transition from operating at a loss to financial viability.The joint effort expands rural broadband access, supporting digital transformation for remote and smaller farms and innovating the food supply chain.This project exemplifies the role of cloud-based AI in modernizing agriculture, improving productivity, and fostering sustainable practices for U.S. farmers.

Land O'LakesAgriculture

CSIRO, Australia's national science agency, partnered with Microsoft to apply AI, cloud, and data analytics in tackling major agricultural and environmental challenges, such as plastic pollution, illegal fishing, and boosting farm productivity.Scientists use machine learning and Microsoft’s Custom Vision to rapidly analyze images and videos from beach and ocean surveys, allowing for automatic identification and tracking of plastic garbage and marine debris.The partnership deploys Azure FarmBeats and low-cost sensors at the Boorowa agricultural station to monitor soil health, crop yields, and farm operations through connected digital ecosystems.Rangers and indigenous land managers use mobile dashboards and AI-driven analysis to support on-ground conservation decisions, resulting in tangible ecological benefits, e.g., recovery of native species.The collaboration also helps combat illegal fishing with AI-powered audio and vision analytics, providing authorities actionable insights from underwater microphones, satellite data, and sensors.Microsoft’s tools facilitate integration of various environmental and agricultural data sources, enabling informed intervention, research, and ecological management decisions.The CSIRO–Microsoft partnership exemplifies how AI-driven farming techniques and environmental monitoring can deliver resource efficiency, sustainability, and improved farm and fisheries management.

CSIROAgriculture

Airdoc, a fast-growing Chinese health technology startup, developed an AI-based system for rapid, accurate retinal imaging and chronic illness screening. The solution leverages deep learning models trained and deployed on Microsoft Azure ML and Azure cloud for scalable compute and data security. The system analyzes high-resolution retina images for early signs of diseases like diabetes, hypertension, arteriosclerosis, and macular degeneration, returning results within seconds to doctors and patients via smartphones or in-store devices.The company addresses a significant shortage of qualified retinal diagnosticians and aims to make early, preventive care affordable and accessible globally. Collaborations with major optical retail chains have seen over 1.12 million scans worldwide, with deployments across China, the US, UK, India, and Africa. The system is helping prioritize severe cases, relieve medical staff workloads, and reduce blindness and complications from chronic diseases. Ongoing R&D promises even broader disease detection via continued model training on Azure. Patient privacy is ensured through Azure's secure infrastructure.

AirdocHealthcare