The AgriDataValue project presented by Synelixis at the EC-ESA JOINT EARTH SYSTEM SCIENCE INITIATIVE workshop, organized by ESA and the DG-RTD of EC. The event was held on the 22nd -24th of NOVEMBER 2023 in ESA-ESRIN, FRASCATI, ITALY. This initiative is expected to provide a significant scientific contribution to realize the vision of the EU Green Deal. AgriDataValue targets high-level EU impact goals, including call’s expected outcomes, de stination impacts, and the Key Strategic Orientation of “Innovative governance, environmental observations and digital solutions in support of the Green Deal”. The project is designed and structured to significantly contribute to the overall EU agricultural development and the objectives set by the Green Deal, the EU Climate Change policy and new CAP.
Drones and multispectral cameras are a powerful combination for enabling timely and informed decisions on crop management. Scalability is a key advantage in this context, since drones can be used to fly over large fields quickly and efficiently, collecting multispectral data from a variety of angles, corresponding to images of crops in multiple spectral bands. This data can then be processed to create detailed maps of crop health and variability.
The AgriDataValue project will work on the so-called “Drones Data Toolbox” that will enable the ingestion of aerial imaging captured by multispectral cameras on drones to the emerging agri-data spaces. The project will utilize drones equipped with both visual and multi-spectral cameras to collect in-situ observations of the crop that will complement the remote sensing, satellite-based earth observations. The images that will be captured by the drones will be appropriately geo-tagged and semantically enriched to be integrated in the AgriDataValue data ecosystem and be available to feed data processing pipelines using advanced AI techniques.
A key enabling application stemming from the processing of the in-situ aerial imaging and the satellite-based data is the calculation of vegetation indices (VIs) such as the Normalized Difference Red Edge (NDRE) and the Normalized Difference Vegetation Index (NDVI). VIs are mathematical transformations of multispectral imagery that are used to quantify the presence and condition of vegetation. The indicators can be easily interpreted by the farmers to detect subtle changes in crop health, such as nutrient deficiencies, water stress, and pest infestations. In the context of AgriDataValue project, we will test the efficacy of AI-enabled VIs prediction models, adhering to data space principles for trustworthy data sharing, to estimate various crop health parameters. In addition, we will test the applicability of these Vis prediction models in real-world scenarios and the acceptance from farmers, paving the way for the uptake of drones technologies and multispectral cameras in the agricultural sector.
After almost 11 months since the project’s kick off, the partners have gathered to address project related issues in a two-day hybrid meeting. The third plenary meeting hosted by SIXENSE, in Paris, France on November 23rd and 24th of 2023. A fruitful discussion took place among the partners of the consortium. During the two-day meeting, the consortium reviewed the progress on all work packages and had the chance to address development issues. The partners presented the results achieved so far and the next steps towards the successful delivery of the AgriDataValue project.
In modern farming, sustainability is a big concern. Traditional farming methods have sometimes caused problems like soil damage and harm to the environment. To address these issues, farmers have started using new ways of farming that are kinder to the land. Two of these methods are minimum tillage and strip-till, and they’re becoming more popular in conventional farming. They both focus on being gentle to the soil, saving resources, and making farming more sustainable. In this article, we’ll look at the differences and similarities between minimum tillage and strip-till methods. We’ll talk about how they help the environment, affect crop growth, what kind of equipment they need, and how they fit into the future of farming. Let’s dive into these new farming techniques and see how they might change the way we farm.
Minimum Tillage vs. Strip-Till: A Comparison
Both minimum tillage and strip-till are practices aimed at reducing soil disturbance and promoting sustainable agriculture (Table 1):
In the realm of modern agriculture, the pursuit of sustainability has led to the emergence of innovative farming practices, namely minimum tillage and strip-till. These methods, driven by the desire to reduce soil disruption, conserve resources, and promote environmental well-being, have demonstrated their worth in the world of conventional farming.
Minimum tillage, with its gentler approach to soil management, stands as a valuable ally in the fight against soil erosion, soil degradation, and greenhouse gas emissions. By leaving crop residues on the field and tilling the soil less deeply, minimum tillage helps preserve soil structure and health while contributing to a more sustainable agricultural future.
Strip-till, on the other hand, offers precision in soil preparation. By creating well-defined tilled strips, it not only minimizes soil disturbance but also provides a favorable environment for crops to thrive. Improved weed management and the potential for increased crop yields underscore its role in sustainable farming practices.
Within the scope of the AgriDataValue project, the incorporation of advanced technologies such as weather stations and IoT sensors marks a significant leap forward. These devices will be installed in the field to collect comprehensive data on weather patterns and soil conditions, enabling precise adjustments to farming practices. The insights gained from this real-time data can lead to more informed decision-making, optimized resource use, and further advancements in sustainable agriculture.
As we look toward the future, it becomes evident that these practices have a vital role to play in the quest for sustainable agriculture. Here are some considerations for the road ahead (Figure 1):
1. Technology and Innovation: Continuous advancements in farming technology and equipment will likely enhance the efficiency and accessibility of minimum tillage and strip-till methods. Integrating data-driven solutions and automation can further optimize these practices.
2. Education and Training: Providing farmers with the knowledge and training required to implement minimum tillage and strip-till effectively is crucial. Outreach programs and support from agricultural institutions can facilitate the adoption of these practices.
3. Tailoring to Local Conditions: Different regions and crop types may benefit differently from minimum tillage and strip-till. Future strategies should focus on tailoring these practices to local conditions and crop needs.
4. Monitoring and Research: Ongoing research and monitoring efforts should assess the long-term impacts of these methods on soil health, crop productivity, and the environment. This knowledge will help refine best practices.
5. Policy Support: Government policies and incentives that encourage sustainable farming practices, including minimum tillage and strip-till, can play a pivotal role in their widespread adoption.
In conclusion, minimum tillage and strip-till farming methods offer promising avenues for more sustainable and environmentally friendly agriculture. The integration of IoT sensors, such as weather stations, into these practices enhances their efficacy by providing precise, real-time data. This data-driven approach allows for the optimization of resources and informed decision-making, which can lead to improved crop yields, reduced environmental impact, and cost savings. Embracing these practices, alongside continued innovation and support, holds the potential to reshape the future of farming for the better, ensuring that we can meet the needs of today without compromising the needs of tomorrow. The AgriDataValue Project exemplifies this synergy between traditional farming techniques and modern technology, setting a benchmark for the future of sustainable agriculture.
- Reicosky, D.C., and R.R. Allmaras. “Minimum Tillage Effects on Soil Carbon and Aggregate Stability in the Northern Great Plains.” Soil Science Society of America Journal, 2002.
- Blanco-Canqui, Humberto, and Richard B. Ferguson. “Strip tillage and nitrogen rate effects on soil carbon and nitrogen in irrigated continuous corn.” Soil Science Society of America Journal, 2013.
- Kaur, G., et al. “Environmental and economic benefits of no-tillage and strip-tillage practices: a review.” Renewable Agriculture and Food Systems, 2018.
- Wilhelm, W.W., et al. “Corn and soybean yield response to crop rotation and tillage.” Agronomy Journal, 2004.
- USDA Natural Resources Conservation Service. “Conservation Tillage.” (chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.ers.usda.gov/webdocs/publications/90201/eib-197.pdf)
- Sustainable Agriculture Research & Education (SARE). “Strip Tillage and No-Till for Vegetables.”
The AgriDataValue technological tools, mechanisms, and Lean Multi-Actor Approach (LMAA) will be fully tested and validated during the AgriDataValue project lifetime through several Use Cases (UC) in 23 pilots in 9 countries, representing more than 181,000ha with 25 types of crops that span from southwest to northeast Europe, outdoor and greenhouse crops, organic and non-organic production, and more than 2,000 animals of 5 types. In addition, more than 4,200 farmers will provide insights and more than 89,000 will be directly informed.
The pilots will also be used to collect sensor data and feedback from the involved end-users, monitor and adapt the pilots over the project lifetime. The activity will continue though out the project’s lifetime (and at least 3 years after). The captured datasets will be used for the ML training and evaluation the evolving impact in each use case. In this phase mature AgriDataValue tools and solutions will be delivered evaluating the impact and collect feedback.
The Use Cases (UC) of the project have been finalized and grouped in clusters, addressing similar domains in different regions, crops, cultural, societal and farming contexts. Since the beginning of the AgriDataValue project, two new clusters have been added. The first one is ‘’Cluster number 6 – CAP Realization’’, and the second one is ‘’Cluster number 7 – Climate Monitoring’’. Below, a short description of each cluster is provided.
UC CLUSTER #1 – Arable Crops
Optimize the quality and quantity of crop production and increase environmental sustainability. Reduce the wasted irrigation water, fertilizers, pesticides, and energy. Involve different technologies and data platforms such as IoT sensors, edge cloud, drones/satellite visual/multispectral images and AI models.
UC CLUSTER #2 – Vegetables
Precision irrigation/ fertilization, harvest /diseases prediction for vegetables/arable crop increased production. Involve IoT sensors, edge cloud, radiation/clorofile/pH metering, multiple data platforms with geotagged photos alone with drones/satellite multispectral imagery.
UC CLUSTER #3 – Trees & Vineyard
Protect the health and quality of fruit trees and vineyards crop. Increase quality and quantity, avoid diseases with less pesticides, foresee and mitigate frost. Involve IoT weather/soil sensors, edge cloud, diverse geotagged photos’ datasets, drones/satellite multispectral imagery.
UC CLUSTER #4 – Livestock
Use edge cloud and real-time IoT sensor data (e.g. neck collar, feeders, emission sensors) together with GPS location data to monitor the cattle/pig health, activity, feeding and calving, proactively control milk and meat quality, reduce the greenhouse gas emissions and nitrogen deposition.
UC CLUSTER #5 – Cross Sector
Validate cross domain use cases (fruit, vineyards, livestock, milk, oil, biogas, manure, energy) and address both supply and demand sides of the supply chain, including interoperability and traceability of platforms, electricity production and waste management.
UC CLUSTER #6 – CAP Realization
Focuses on CAP realization tools/applications and aims to assess and manage risk through modern ML with the aim of reducing the use of pesticides, fertilisers and antibiotics. Promote modern crop monitoring technologies. Benchmarking eco-scheme monitoring tools to support the new CAP towards fair income, land use and environmental protection.
UC CLUSTER #7 – Climate Monitoring
Climate monitoring plays a vital role in agriculture by providing valuable information about weather patterns, climate variability, and climate change impacts on crop growth, pests, diseases, and water availability. It involves collecting data from weather stations, satellites, remote sensing technologies, and climate models.
AgriDataValue presents a comprehensive and innovative approach to revolutionize the agricultural sector by leveraging advanced technologies and data-driven solutions. Throughout the project, significant advancements will be made in several key areas, including data integration and interoperability, DSSs, precision agriculture, remote sensing, IoT, and blockchain technology.
Overall, the AgriDataValue project signifies a transformative shift in the agricultural sector, where data-driven approaches, advanced technologies, and collaborative frameworks converge to address the challenges and unlock the opportunities in modern agriculture. By integrating diverse datasets, developing advanced decision support systems, promoting precision agriculture and remote sensing, and exploring blockchain technology, the project paves the way for a more sustainable, efficient, and resilient agricultural ecosystem. The outcomes of the project have the potential to drive innovation, optimize resource utilization, mitigate risks, and ultimately contribute to global food security and environmental stewardship.
At ILVO the enteric emissions from dairy cows are being monitored to gather data that will be used to develop AI models within the AgriDataValue project. Currently, enteric emissions are already being monitored at the ILVO dairy farm using GreenFeeds (C-Lock Inc., USA). In these devices cows are being fed small amounts of concentrate while at the same time the concentration of methane and carbon dioxide in their breath and eructation are being measured. Cow measurements are combined into daily emission values expressed as grams CH4 per day which provide a good idea of enteric emissions from cows. As these emissions are influenced by many other factors, general cow data such as milk production, lactation stage etc. will also be made available for further analysis.
Apart from these individual measurements, SynField sensors will be used to gather more emission data from the barn environment in the future. Using the AgriDataValue platform, these data will be made available for the development of AI models to create more added value.
As one of the pilots in the AgriDataValue project, ILVO intends to further increase the range and quality of emission data and make them available to third parties in the project in order to work towards a reduction of greenhouse gasses in the dairy sector.
In large scale agricultural research projects such as AgriDataValue, where vast amounts of sensitive data are generated and analyzed, having a reliable and secure storage solution is required. MinIO, an open-source object storage server, emerges as a significant tool in this scenario, offering a scalable, high-performance, and secure storage platform tailored to the unique needs of agricultural innovations.
The use case partners involved in AgridataValue generate large datasets, ranging from satellite imagery to real-time IoT sensor data. MinIO’s scalable architecture allows effortless expansion, ensuring that the storage infrastructure can grow seamlessly with the increasing volume of agricultural data. Whether it is analytics for crops management or climate monitoring, MinIO’s scalability makes it a suitable choice for handling diverse data types and massive datasets.
Data security is always necessary when dealing with sensitive agricultural information. MinIO offers robust encryption mechanisms, both in transit and at rest. This means data is encrypted during transmission, safeguarding it against interception, and remains encrypted when stored, ensuring unauthorized access is impossible. This level of security instills confidence among farmers and project stakeholders, enhancing the overall trust in the technology.
Data integrity must be ensured in our project, where decisions are often data driven. MinIO’s advanced features, such as checksumming and bitrot protection, guarantee that the stored data remains intact and uncorrupted. With MinIO, AgriDataValue can rely on the accuracy and reliability of the data, enabling precise analysis and informed decision-making.
Collaboration among stakeholders is a cornerstone of successful European projects. MinIO’s support for various APIs and compatibility with standard protocols ensures seamless integration with other technologies and tools. Whether it is sharing data insights with researchers or policymakers, MinIO enables secure and controlled data collaboration, enhancing the project’s collaborative potential.
Agricultural innovation heavily relies on advanced technologies like AI and machine learning. MinIO’s compatibility with AI frameworks and analytical tools simplifies the integration process. The stored data can be seamlessly accessed and processed by AI models, enabling the project to derive meaningful insights and optimize agricultural practices effectively.
MinIO, with its scalability, security features, data integrity assurance, and seamless integration capabilities, stands as a robust solution for securing the vast and diverse datasets generated by modern agricultural projects. By choosing MinIO as the storage backbone, the AgriDataValue project not only ensures the confidentiality and integrity of agricultural data but also lays a solid foundation for future innovations. With MinIO tools in place, our project can focus on what truly matters: leveraging data-driven insights to revolutionize farming practices and secure a sustainable future for agriculture.
The SynField system has been installed in a cultivation area in the outskirts of Zalenieki, Latvia. This is the first smart agriculture system installed within the AgriDataValue project. SynField provides the capability to monitor and record the environmental and soil conditions prevailing in the area. The installation includes a meteorological station, a soil temperature sensor and a humidity sensor. The meteorological station records the ambient temperature, relative humidity, wind intensity, wind direction and rainfall. SynField smart agriculture systems will be installed in many European countries to collect valuable data and measure key environmental parameters.
The AgriDataValue technologies will be fully tested and validated during the AgriDataValue project lifetime through the projects’ Use Cases (UC) in 9 countries, representing more than 181,000ha with 25 types of crops that span from southwest to northeast Europe, outdoor and greenhouse crops, organic and non-organic production, and more than 2,000 animals of 5 types. In addition, more than 4,200 farmers will provide insights and more than 89,000 will be directly informed.
Within the projects’ Use Cases, SynField smart agriculture systems will be utilized to collect valuable data in order to optimize the quality and quantity of the crop production and increase environmental sustainability. Innovative technologies such as SynField will contribute to reducing the wasted irrigation water, fertilizers, pesticides, and consumed energy.
AgriDataValue participated at the ‘’Open company day’’ event hosted by the project’s partner Inagro in Beitem, Belgium. The event was attended by more than 3000 visitors, including farmers. During the event, a demonstration of the precision agriculture technologies, that will be incorporated within the project’s pilot activities was performed. More specificaly, unwanted herbs in a celeriac field detection using drones scenario and demo has been presented.
The domain of agriculture faces many challenges such as disease and pest infestation, suboptimal soil management, inadequate drainage, and irrigation, and many more. These challenges result in significant crop losses and environmental hazards, primarily due to the excessive use of chemicals. Several research have been conducted to address these issues. The field of Artificial Intelligence with its rigorous learning capabilities has become a key technique for solving different agriculture related problems. (2) Systems are being developed to assist – agricultural experts for better solutions throughout the world. (3) Among these systems, it is widely recognised that Artificial Intelligence (AI) techniques will play a key role in the immediate foreseeable future. In parallel, also the introduction of algorithms able to explain AI algorithms (also known as eXplainable AI or XAI) is deemed necessary. An interesting analysis on this subject was done by Masahiro Ryo (4) from whose paper the following diagram is taken, illustrating the AI/XAI usage trend.
The importance of adopting an explainable AI approach is also critical when the end users are farmers or agricultural companies. By providing an explanation of the AI’s decision-making process, farmers can gain better understanding of its functioning, increase their level of trust in the technology, and make informed decisions about how to optimize their operations. This can result in significant cost savings, as farmers can target their resources on the areas that will yield the greatest benefits.
Man and Machine Gap
In the past decade, we have witnessed a massive development in Machine Learning and related fields. Due to the increased availability of big datasets and generally more accessible powerful computing resources Deep Learning techniques experienced a boost that revitalized Machine Learning. The resulting super-human performance of AI technologies in solving complex problems has made AI extremely popular. As a matter of fact, however, this increase in performance has been achieved using increasingly complex models that are not interpretable by humans (Black Box AI). Furthermore, in specific contexts closely tied to human experiences, the utilization of AI algorithms is still viewed as risky and lacking in trustworthiness. For example, in healthcare AI applications that support clinicians’ decisions by suggesting a specific treatment for a patient based on predictions of complications. Or, in banking field, AI mechanisms that suggest whether a customer who has applied for a loan is creditworthy or not. In these contexts, it is essential that decision-makers have access to information that can explain why a patient risks a specific complication or why an applicant should not get a loan. But not only that: a mechanical maintenance operator will for instance be able to make little use of information about a future system failure if he or she does not also have indications of which component is beginning to degrade, just as a farmer should know in detail which s suboptimal management will cause a predicted crop failure so that he or she can take informed and targeted action.
The XAI bridge
Contributing to these results, XAI technology has grown exponentially in the AI domain, dealing with the development of algorithms able to explain AI algorithms, thus closing the gaps. Its relevance has become evident not only in academia, but also in industry and institutions. The European Union in the AI Act (1) has explicitly declared the ‘interpretability’ of AI to be a fundamental characteristic for all AI systems, and indispensable for those AI solutions deemed to be high-risk, to be used in European soil.
Explainable AI, also known as Interpretable AI, or Explainable Machine Learning (XML), either refers to an AI system over which it is possible for humans to maintain oversight, or to the methods to achieve this.
Main XAI techniques
In AI, Machine learning (ML) algorithms can be categorized as white-box or black-box. White-box models, sometimes called glass box models, provide results that are understandable to experts in the domain. Black-box models, on the other hand, are any AI systems whose inputs and operations aren’t visible to the user, or another interested party. The goal of the XAI is to ‘open up’ black boxes and make them as like a white box as possible. XAI algorithms follow the two principles of:
- Transparency: when the processes of extracting features from the data and generating labels can be described and motivated by the designer.
- Interpretability: possibility of comprehending the ML model behaviour and presenting the underlying basis for decision-making in a human-understandable way.
If such algorithms fulfil these principles, they provide a basis for justifying decisions, tracking them and thereby verifying them, improving the algorithms, and exploring new facts.
Over the years, numerous methods have been introduced to describe the operation of more ‘Black-Box’ AI algorithms, for example Deep Neural Networks. These methods are characterized along two axes:
- Local and global methods: as the name suggests, these are methods used to explain different aspects of the AI model behaviour. Local explanations explain single model decisions, while global explanations characterize the general behaviour of a model (e.g., a neuron, a layer, an entire network). In some cases, global explanation is derived from local explanations, but this is not necessarily true for all artificial intelligence models.
- Post-hoc and Ante-hoc Methods: Post-hoc “after this event” methods are those methods that provide the explanation after the model has been trained with a standard training procedure; examples of such methods are LIME, BETA, LRP. Ante-hoc methods are those that are interpreted immanently in the system, i.e., they are transparent by nature in the sense that these methods introduce a new network architecture that produces an explanation as part of its decision.
There have been several attempts to explain the prediction of the AI models. The most active among them has been on the problem of feature attribution. The feature attribution aims to explain which part of the input is most responsible for the output of the model. For example, in Computer Vision tasks detecting plant diseases, heatmaps can show the region of the most damaged leaf area of the image that affects mostly the output. Other techniques include feature visualization, interpretability by design.
These studies have produced many tools and frameworks for developing XAI systems. A short view of them could be:
- LIME: Uses interpretable feature space and local approximation with sparse K-LASSO.
- SHAP: Additive method; uses Shapley values (game theory) unifies Deep LIFT, LRP, LIME.
- Anchors: Model agnostic and rule based, sparse, with interactions.
- Graph LIME: Interpretable model for graph networks from N-hop neighbourhood.
- XGNN: Post-hoc global-level explanations for graph neural networks.
- Shap Flow: Use graph-like dependency structure between variables
XAI in AgriDataValue Project
As part of the AgriDataValue project, which aims to drive digital transformation in agriculture at the European level, a specialized module is being designed and created to increase trust and confidence of end-users in this technological ecosystem that will make massive use of AI.
The Human Explainable AI Conceptual Framework Component will fully integrate AgriDataValue data platform and Federated Machine Learning component offering two main business services to the users/citizen users:
- The ability to investigate the identity of an already trained AI model (what algorithm it is, what data it was trained on, what processing steps the data underwent, etc.).
- The possibility of being able to interpret a model’s decision on one or more data.
AI eXplainability is still object of a lively and active research that has produced, so far, many development tools and frameworks to address the difficult task of explaining the predictions of AI models, in particular those produced by Black-Box models. It is quickly becoming one of the main areas of development of the future of the AI systems. The research on XAI subject is still progressing and, in the meantime the existing methods and frameworks start being applied in application domain fields, including Agriculture, to comply with stronger and stronger interest of government bodies and Legislators. One point on which all seem to agree is that the AI of the future should conform to human values, ethical principles, and legal requirements so to ensure the privacy, security, and safety of the human users.
(1) “Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on Artificial Intelligence (Artificial Intelligence Act) and amending certain Union legislative acts, COM/2021/206 final, in https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=celex:52021PC0206.
(2) Benos et al., 2021 “Machine Learning in Agriculture: A Comprehensive Updated Review”, MDPI.
(3) Gouravmoy Bannerjee, Uditendu Sarkar et al., 2018 “Artificial Intelligence in Agriculture: A Literature Survey”, ISSN 2319 – 1953.
(4) Masahiro Ryo, 2022, “Explainable artificial intelligence and interpretable machine learning for agricultural data analysis”, KeAi Communications Co.