Companies are required to implement a robust due diligence system that gathers and assesses relevant information, identifies potential risks, and ensures these risks are reduced to negligible levels. This means that each coffee lot must be precisely geolocated, either with GPS coordinates or mapped polygons linked to the specific farm of origin. Additionally, compliance with all applicable local laws is essential, encompassing land tenure, labor rights, and environmental regulations. To ensure traceability around farm-to-export tracking, companies must submit due diligence statements to the EU prior to importing or exporting coffee products. All this must done in an environment where 60% of the coffee farmers in Africa are smallholders, who are farming land that are managed under largely unwritten customary land tenure systems. Given these challenges, ensuring program integrity to reduce fraud, inefficiencies and undo burdens on businesses is key for the EU to meet its program objectives. These challenges will require a sophisticated information system that can intelligently manage information as articulated by EU\u2019s Environment Commissioner Jessika Roswell\u2019s concern over disrupting businesses as she states \u201cWe need the time to combat the risk with the load of information in the IT System\u201d [4<\/a>].<\/p>\n\n One way to reduce the information system load is to choose a technology that best fits the challenges of ensuring compliance \u2013 one that considers the geography of the business as a natural starting place. For example, the EUDR introduces a critical need for robust geospatial verification of land use history, making Earth Observation (EO) and Geographic Information Systems (GIS) essential tools for ensuring regulatory compliance. Given the need for GIS, Esri announced in 2024 that it has renewed support for the Forest Stewardship Council through a software donation to help accelerate the land verification task. [5<\/a>]. This blog article outlines potential applications of Esri\u2019s GIS technology and platform.<\/p>\n\n Esri\u2019s technology could aid the geospatial approach that focuses on an open solution to provide transparency around the benchmarking of countries. This involves the implementation of a country benchmarking methodology framework that categorizes countries into low, standard or high-risk categories for deforestation-free commodities such as coffee. In this way, transparency enables the opportunity for self-policing because every participant can see where they stand.<\/p>\n\n Transparency relies heavily on open data, where the geographical approach renders that transparency through maps encapsulating key data sets from Esri\u2019s Living Atlas, Digital Earth Africa, Global Forest Watch, UNWCMC Protected Areas, etc. Esri offers a few of the key data sets, such as:<\/p>\n\n Using these data sets, several techniques from the Esri suite can be leveraged to detect deforested areas using Esri\u2019s ability to manage satellite data along with various band ratioing techniques (e.g., vegetation indices like NDVI, NDRE) as outlined in [6<\/a>] and shown in fig. 1.<\/p>\n\n The challenge, of course, is around combining these data sets together to make key determinations around coffee production, which can range from shaded, polycropping to unshaded, monocropping systems. For shaded systems, tree canopies can make EO difficult when trying to create coffee production maps, which could appear like non-deforestation areas despite being defined by FAO as deforested land (i.e., agroforestry) [7<\/a>]. For these reasons and other geolocation accuracy issues, capturing good ground truth potentially from the farmers themselves would ensure that the monitoring systems constantly improve, especially given the advances in machine learning techniques.<\/p>\n\n In [8<\/a>], to automate detection of these difficult scenarios, numerous methods of classification for mapping coffee production systems can be used such as spectral pixel-based, spectral sub-pixel based, texture-based, data-fusion based, object-detection based, and hybrid. Each of the techniques offers a wide range of accuracy where advanced image processing techniques can be developed over time to produce more accurate maps. Serving as an environment for potentially detecting coffee fields and plants whether shaded or not, Esri\u2019s image processing capabilities offer a comprehensive suite of tools designed to support advanced image processing and analytics described in [8<\/a>] across a variety of EO data sources. Leveraging both traditional and cutting-edge techniques, Esri enables users to apply spectral indices (such as NDVI and NDRE), texture analysis, and data fusion to derive meaningful insights from satellite imagery. The platform also integrates deep learning methods, such as field boundary detection<\/a>, allowing for the automated extraction of complex features like farm field perimeters from high-resolution imagery. These deep learning models can be trained to recognize subtle differences in crop cover, potentially supporting the distinction between shaded and unshaded cropping systems, and improving the accuracy of agricultural mapping even in challenging environments. Esri\u2019s geoprocessing framework allows organizations to flexibly mix and match these methods in order to streamline workflows. As a result, this will minimize redevelopment time as analytical requirements evolve due to increasing accuracy requirements as compliance standards move from larger holders to smaller operations over time.<\/p>\n\n To reduce the information load in the information system, we suggest starting with a simple workflow that uses a combination of mobile data capture, imagery analysis, and spatial analytics on a single geo-platform.<\/p>\n\n As shown in fig. 2, a practical workflow leverages open-access EO datasets and modern GIS technologies through a 3-step process: data collection, risk assessment, & risk mitigation. <\/em><\/p>\n\n The collection of data is necessary for compliance and involves providing geolocation data and legal documentation, where data collection will likely be form based. These forms could be entered by the farmer or, more likely, with help from local authorities or NGOs.<\/p>\n\n The geolocation requirements depend on the size of the coffee farm, which must provide a valid farm ID:<\/p>\n\n Such a requirement lends itself to solutions from Esri\u2019s mobile suite of tools. ArcGIS Survey123<\/a>, for example, can take geolocation data from either GPS devices or mobile phones and combine it with document attachments, form fields, and other data. Survey123 also has the capability that would allow a coffee farmer to walk around a field to automatically produce the full polygon requirement for farms larger than 4 hectares.<\/p>\n\n To ensure compliance with the EUDR, several key factors must be carefully assessed. These include the prevalence of deforestation within the country or region, the proximity of farms to areas experiencing forest loss, protected areas, and biodiversity hotspots. Additionally, it is vital to consider the presence of indigenous or customary land rights and to evaluate the level of corruption or overall governance in the source country. Assessing these elements helps identify potential risks and supports the implementation of sustainable sourcing practices that align with the regulatory requirements, but this must be done efficiently in a cost-effective manner to avoid incentivizing producers to move to alternative markets where regulatory requirements are non-existent.<\/p>\n\n In order to reduce \u201cred tape\u201d, workflows for assessment must be done efficiently by governing organizations who look at the data captured in the data collection phase. These organizations are highly dependent on map accuracies, which can vary by geography, and the ability to apply spatial analytic tools from mature GIS systems like Esri.<\/p>\n\n Spatial Analytics tools from Esri range from finding patterns to predicting values and forecasting conditions, except with a focus on space and time. Techniques like geospatial cluster analysis (e.g., Density-based Clustering<\/a>, Multivariate Clustering<\/a>, Spatially Constrained Multivariate Clustering<\/a>) could be used in models that inform stakeholders of countries\u2019 overall EUDR compliance risk at the country and potentially local level, as shown in fig. 3.<\/p>\n\n To mitigate the risk of incorrectly assessing EUDR compliance, stakeholders should seek additional evidence or clarification directly from suppliers, conduct on-site audits or field inspections to validate reported practices, and leverage satellite imagery or other remote-sensing technologies to independently verify land-use claims. These combined approaches enhance the accuracy of compliance assessments by cross-checking information from multiple sources and ensuring that data provided aligns with actual conditions on the ground. This multi-faceted verification process helps reduce the likelihood of errors or misrepresentation, supporting more robust and credible compliance with EUDR requirements.<\/p>\n\n But perhaps the best way of mitigating risks for all models is to apply predictive analytics to anticipate where problems may occur, whether they are about predicting suppliers who may be at risk of non-compliance or potential future country or local level risk. For this, Esri provides a large suite of spatially-oriented models such as Forest-based Classification and Regression, Geographically Weighted Regression, EBK Regression Prediction, and Support Vector Machines. In addition to prediction, tools that do time series analysis and forecasting including Curve Fit Forecasting<\/a>, Exponential Smoothing Forecast<\/a>, Forest-based Forecast<\/a>, Evaluate Forecasts By Location<\/a>, Train Time Series Forecasting<\/a> & Forecast Using Time Series<\/a>, and Emerging Hot Spot Analysis<\/a>.<\/p>\n\n As a practical example, consider a coffee plantation in Uganda, which is located in a country currently classified as standard risk. Since the plantation is larger than 4 hectares, it is required to submit a full boundary polygon as part of compliance documentation. Referring to the ESA land use map from 2020, this region is categorized as forest, as shown in Figure 4.<\/p>\n\n The Global Forest Watch dataset reveals evidence of forest disturbance in both 2023 and 2024. However, when overlaying the EU forest cover map from 2020, it becomes clear that none of the recent disturbances occurred within areas that were classified as forest at that time, which means the plantation is compliant.<\/p>\n\n To strengthen compliance verification, stakeholders including the farmers can use tools like Esri\u2019s Sentinel-2 Explorer<\/a> to compare satellite images from the past five years, analyze NDVI data, and perform on-the-fly change detection. This could help the plantation when reporting this data to the exporter to enable their coffee to be placed on the EU market. <\/p>\n\n At the 2025 Esri User Conference [11<\/a>], Esri rolled out the components of its geospatial Artificial Intelligence (AI) platform and the GIS Ag community suggested that AI should be reframed as Agriculture Intelligence by focusing on the critical attribute of \u201ctrust\u201d. That trust must be built into systems where there is trusted<\/em> data<\/strong>, trusted<\/strong><\/em> analytics<\/strong> and trusted<\/em> knowledge<\/strong>. While this article has emphasized trusted data built on sound geoprocessing algorithms for spatial analytics, trusted knowledge is the key to providing agriculture intelligence to the coffee endeavor.<\/p>\n\n First, it\u2019s key to understanding supply chain traceability to ensure that processors (e.g., roasters) do not taint the supply chain by mixing beans where one producer could be non-compliant. Through Esri\u2019s ArcGIS Knowledge product<\/a>, Knowledge Graph (KG) technology can easily show complex supplier relationships, that when coupled with geography and the aforementioned imagery solutions can facilitate the discovery of those tainted supply chains by visualizing the supply chain (see [12<\/a>] for a tutorial on ArcGIS Knowledge for agriculture supply chains). <\/p>\n\nA Geospatial Solution<\/h2>\n\n
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![\"Difference](\"https:\/\/www.esri.com\/en-us\/industries\/blog\/wp-content\/uploads\/2025\/10\/Coffee-NDVI.png\")
Efficient and Effective Workflows<\/h2>\n\n
![\"Figure](\"https:\/\/www.esri.com\/en-us\/industries\/blog\/wp-content\/uploads\/2025\/10\/EUDR-platform-1024x432.jpg\")
Collect Information<\/h3>\n\n
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Assess Risk<\/h3>\n\n
![\"Figure](\"https:\/\/www.esri.com\/en-us\/industries\/blog\/wp-content\/uploads\/2025\/10\/Risk-Africa.png\")
Mitigate Risk<\/h3>\n\n
Ugandan Plantation Example<\/h2>\n\n
![\"Figure](\"https:\/\/www.esri.com\/en-us\/industries\/blog\/wp-content\/uploads\/2025\/10\/Coffee-plantation-Composite.png\")
![\"Figure](\"https:\/\/www.esri.com\/en-us\/industries\/blog\/wp-content\/uploads\/2025\/10\/Coffee-Sentinel-Viewer-1024x563.jpg\")
Agriculture Intelligence applied to the Coffee Supply-Chain<\/h2>\n\n