Please describe your proposed solution.

DigiFarm has successfully completed a Fund 8 funded project titled: "Open ledger for agricultural land" where the purpose and objective was to create the POC solution for digitising 914 agricultural field boundaries in Tanzania, where we are working on collaborating on implementing this in the last mile delivery with UNCDF, Gates Foundation and UN, and the benefits from this project and how it helped smallholder farmers included:

1. Benefits included improved access to micro-financing for crop-input, i.e. ~10-12% lower cost
2. Digitisation of field boundaries and measuring of this opens up a new market for carbon credit accessibility, new revenue streams for farmers, i.e. ability to increase revenue per ha of ~40%

This project builds on the original idea successfully completed in Fund 8 and also presented during the most recent Town Hall (12.07.2023) as the cornerstone of enabling wide-scale adaption (Oracle) of digital identity, open ledger of agricultural field boundaries and blockchain components to create a decentralised, independent source of truth for smallholder farmers in the Cardano ecosystem, hence, this project will be focused on expanding the reach and impact of this pilot project completed in Fund 8 across all the cropland area in Kenya and Tanzania, reaching over 75+ million hectares of farmers.

The current problem in smallholder markets is firstly that 84% of the world’s 570 million farms are smallholdings; that is, farms less than two hectares in size. Many smallholder farmers are some of the poorest people in the world. Tragically, and somewhat paradoxically, they are also those who often go hungry. Lastly, currently 29% of the world's agricultural food production is produced in smallholder market but this is forecasted to change drastically as smallholder farmers gains access to better agronomic advise, crop-input prices and micro-financing.

Additionally, in order to provide additional context on the agricultural market in general:

1. 40% of all agricultural fields are over fertilised
2. Farmers are losing 10-15% on adequate input application (crop protection, seeds and fertiliser)
3. In the most advanced agricultural nation, US, still only 25% use precision ag-services
4. Agricultural fields in Tanzania and Kenya are on avg. smaller than 0.5 ha’s rendering publicly and freely available SatEO insufficient, i.e. this is a largely untapped market

The solution we're building in this project will enable smallholder farmers to easier access financing, agronomic advisory and build their credit profile. Furthermore, with nearly 80% of households in Tanzania engaging in agriculture and at least one third gaining more than half of their income from agricultural activities, while the agriculture sector in Kenya employs more than 40 percent of the total population and 70 percent of the rural population, access to finance for small-scale producers is a major catalyst to broad based economic growth. For a long time and especially in traditional forms of financing, one of the key limiting factors for access to loans for smallholder farmers has been lack of collateral. Looking at land ownership registration for instance, data collected by Tanzania’s bureau of statistics in 2018 shows that out of 8.7 million farms surveyed only 18% were registered.

In addition to this - Small-scale farming systems already grow 50% of our food calories on 30% of the agricultural land. When access to inputs and conditions are equal, smaller farms tend to be more productive per hectare than much larger farms.

The current problem is the lack of historical and in-season data to assess credit risk on smallholder farmers, this due to a lack of infrastructure consisting of agricultural land classification, crop classification, long-term productivity assessment (20-30+ years) on the individual farm-land and field boundaries. Currently, field boundaries are manually created by field agents walking the corners of a physical agricultural field and geo-tagging those boundaries, this is time-consuming, expensive and often inaccurate. In order to provide a reliable and affordable solution the only way is to automate this through the use of deep learning object detection and high-resolution Satellite data.

How does your proposed solution address the challenge and what benefits will this bring to the Cardano ecosystem?

The solution will address the following sections of the challenge:

• National governance systems - New governance systems for nation states
• Climate Change - Solutions that help to solve environmental issues
• Business solutions - Software products, data management, process management, data management solutions (CRM, ERP etc), privacy products**.**
• Artificial intelligence

Furthermore, the solution will enable smallholder farmers to easier access financing, agronomic advisory and build their credit profile. Furthermore, with nearly 80% of households in the region engaging in agriculture and at least one third gaining more than half of their income from agricultural activities, access to finance for small-scale producers is a major catalyst to broad based economic growth.

For a long time and especially in traditional forms of financing, one of the key limiting factors for access to loans for smallholder farmers has been lack of collateral. Looking at land ownership registration for instance, data collected by Tanzania’s bureau of statistics in 2018 shows that out of 8.7 million farms surveyed only 18% were registered. In addition to this it is a known fact that still 30% of the world's agricultural fields are not mapped nor digitised which also creates risk in terms of property ownership and rights.

The current problem is the lack of historical and in-season data to assess credit risk on smallholder farmers, this due to a lack of infrastructure consisting of agricultural land classification, crop classification, long-term productivity assessment (20-30+ years) on the individual farm-land and field boundaries.

Currently, field boundaries are manually created by field agents walking the corners of a physical agricultural field and geo-tagging those boundaries, this is time-consuming, expensive and often inaccurate. In order to provide a reliable and affordable solution the only way is to automate this through the use of deep learning object detection and high-resolution Satellite data.

How do you intend to measure the success of your project?

And in terms of metrics of success:

• Nation governance systems - Amount of population onboarded, amount of costs saved due to new solution, security difficulty improvements over previous approach - i.e. we plan to reach all 75 million hectares of farm land in Kenya and Tanzania, i.e. complete digitisation and mapping of historical and in-season productivity using SatEO.
• Climate change - Total number of users, total CO2 sequestered, amount of awareness being produced, number of people changing a environmentally damaging habit - i.e. we target to be able to provide sustainable and regenerative farming practice advise, through variable rate technologies (VRT), specifically for fertilisation, crop protection and seed which would decrease costs by 10-15% (which are currently over 40% of total crop production cost) and increase potential yield by over 10%.

Additional technical KPIs include:

• Ability to reach target IoU accuracy of delineation of permanent crops across all arable land in Tanzania and Kenya at above IoU of 0.94
• Ability to accurately assess and calibrate with local farmers and ground truth data, e.g. yield-data and productivity zones, e.g. low/medium and high zones will be assigned predictive yield-values, in-season and historically above 90% accuracy which can be used as leverage from securing financing for the smallholder farmers.
• Ability to secure 10,000+ farmers within first 9 months and secure 5 lending institutions as pilot users
• Successful sustainable commercial model after the grant-period has ended

Please describe your plans to share the outputs and results of your project?

DigiFarm will make the output of the project fully open source and available to the wider public, starting primarily with the people and demographics that will benefit the most from the results, the smallholder farmers in Kenya and Tanzania, but then also to the agricultural value chain including land management companies, NGOs, universities and research institutions, agribusinesses, governments and also most importantly larger and wider stakeholders in the Cardano ecosystem, as this will create an "Oracle" for the most critical fundamental datalayer in precision agriculture and in-field analytics, enabling other developers and stakeholders to develop services on top of the datalayers we will provide and hence create an entire community around the data, which is currently non-existent.

What is your capability to deliver your project with high levels of trust and accountability?

DigiFarm has successfully demonstrated it's internal capacity to successfully achieve KPIs in the project funded in Fund 8 "Open ledger for agricultural land" and was highlighted recently selected as one of the projects spotlighted amongst the recently completed 500 Catalyst projects and presented during the Town Hall (12.07.2023).

Additionally, DigiFarm’s team is the ideal fit for the project as our core team has extensive experience in (a) developing agricultural technology for crop-monitoring using AI and remote sensing (Satellite data) to the agribusinesses market (B2B/B2G) using SaaS-models. Successfully built commercial agricultural technological solutions using remote sensing (Satellite-data) and AI across 100 million hectares: >90% accuracy in crop Detection and >85% accuracy in yield-prediction in soybean and corn (US/Brazil) (b) core team has over 15+ years of on-the-ground crop-producing (farming) experience and close partnership withs Felleskjøpet (largest ag-coop in Norway, NLR (Norwegian Agricultural Advisory Organisation) and University of Life Sciences (NMBU) (c) commercial and corporate Ag-market: over 20+ years combined corporate agriculture leadership experience (d) over 40+ experience in agronomy academic research internationally.

Additional qualifications in DigiFarm’s core team and founders (10) include technical and agronomical experience: (a) over 40 years combined international work experience in precision-Ag projects in Canada, USA, Germany, Switzerland, Brazil, Australia, Russia and Ukraine (b) successfully filed 5 patents (AI-based technologies) in agriculture/biology (e) developed technology for Zoner.ag (one of first geospatial web-platforms for analyzing agricultural fields) successfully acquired by Bayer to become the geospatial engine of Xarvio digital-farming platform (owned by BASF). Management capacity: led and managed the Bayer CropScience division as Global Technology Lead with the Digital Farming Division, overseeing expansion Xarvio to over 100 employees, serving over 3.4 million farmers and agronomists worldwide (b) founded and grew AI-based Gamaya (Swiss-based) agtech startup, managed team growth to 45 employees in under 24 months and secured $20 million in VC funding from Mahindra.

What are the main goals for the project and how will you validate if your approach is feasible?

The main goals of the projects including objectives targeted includes as following:

1. Detecting in-season field boundaries and seeded acres - will help farmers assess and plan their growing season accurately through accessing the crop-inputs required (seeds, fertiliser, crop protection).
2. Long-term productivity assessment - DigiFarm will provide 35 years of long-term productivity data (NDVI) for each individual field, including slope (growth of plant biomass over that period) which can be used to access financing and build a credit rating.
3. In-season detection of management zones - DigiFarm will provide in-field assessment of the low, medium and high productivity zones (EVI/biomass) in order for the farmer to assess the problem areas and ability to apply variable fertiliser/fungicide application.

The three components above will make up the digital solution, decentralised and open source, enabling wide adoption of latest technical advancements in AI and SatEO for farmers to leverage a cost-affordable solution in the markets which desperately needs this, i.e. smallholder markets, starting with Tanzania and Kenya.

Please provide a detailed breakdown of your project’s milestones and each of the main tasks or activities to reach the milestone plus the expected timeline for the delivery.

The main goals of the projects including technical KPIs are as follows:

M1-M2

• Identify initial extended AOIs in Tanzania and Kenya in conjunction with UNCDF local team where ground truth data is available (from small section of small holder farmers)
• Process Sentinel-2 imagery for 2017-2023 data (3-4 cloud free images, L2A) across the AOI
• Deeply resolve the Sentinel-2 10m per pixel imagery to 1m per pixel (10-bands, RGB + NIR)
• Start collecting training data (manual delineation of field boundaries across the AOI)
• Start model training (deep neural network) based on the training data

M2-M4

• Proof-of-concept: field boundaries and seeded acres first model results
• Re-train model if necessary in order to achieve accuracy of IoU of 0.95+
• Technical architecture documentation posted at https://digifarming.readme.io/
• Automatically detect long-term productivity Zones (35 years) on the AOI
• Coordinate with local financial institutions and credit bureau's to assess credit portfolio of smallholder farmers - in order to build a model where farmers can access affordable and reliable financing pre-season and during-season
• Proof-of-concept: mobile DigiFarm
• Formal partnership with legal professional(s) to liaise with land management offices in Tanzania and Kenya
• Blog post: write-up
• Blog post: pilot scenario

M4-M6

• Extend AOI to all agricultural land in Tanzania and Kenya
• Open the database to smallholder farmers to access this data and to communicate data to the financial and credit lending facilities
• Blog post: "DigiFarm in Kenya & Tanzania - Results and findings"
• DigiFarm Whitepaper (overview of solution concept and technical architecture)

M6-M12

• DigiFarm Whitepaper v2: Decentralized registry of field boundary productivity in Kenya and Tanzania
• DigiFarm Initial Stake Pool Offering (ISPO)
• DigiFarm beta Dapp on Testnet
• 10,000 Tanzania property owners field testing DigiFarm
• Secure partnerships with local financial institutions and credit lending facilities

End of Fund 10 grant

M12+

• DigiFarm Level 3 Certified Dapp
• DigiFarm launch on Cardano mainnet
• 50,000 registered smallholder farmers in Tanzania
• DigiFarm's platform fully operational
• Expand to additional regions incl. Uganda and Zambia

Please describe the deliverables, outputs and intended outcomes of each milestone.

Outcomes:

Improved understanding of the technological requirements & specifications to guide adoption of DigiFarm’ s existing satellite imagery technology in a Kenya and Tanzania context.

An adapted version of DigiFarm’ s solution based on findings from the field test and validation exercise.

Better decision making based on data and leading to improved product/service delivery packages including insurance, finance, access to markets and in offering agricultural extension and advisory services.

Participating FinTechs, digital economy platforms, are more deliberate and better track gender inclusion.

Outputs:

• Output 1: Field study to understand on the ground realities and to obtain additional layers of data.
• Output 2: Deployment of adapted version and scale up.
• Output 3: Broker relationships that would allow facilitation of API integration between DigiFarm’ s technology and the FinTechs/digital economy platforms.
• Output 4: Offer technical assistance and grant support to selected FinTechs/digital economy platforms.
• Output 5: Liaison with regulators and relevant government departments and agencies to advocate for a more enabling environment to explore and utilize such innovative technologies (satellite imagery, AI and machine learning).
• Output 6: Obtain commitment from the benefiting FinTechs and digital economy platforms to mainstream gender disaggregation tools in capturing data.

Please provide a detailed budget breakdown of the proposed work and resources.

Budget breakdown:

Hosting for the project website and code repositories are provided free of charge via Github. Community outreach will be done via (free) Linkedin, Facebook and YouTube accounts along with Project Catalyst communication channels.

Detailed roadmap above for descriptions of the tasks and work products that will be delivered in three, four-week sprints

• Month 1-2 (Tasks #1-5): 300 hours x $65 = $26,000 = 90,444 ADA
• Month 2-4 (Tasks #1-9): 550 hours x $65 = $35,750 = 124,360 ADA
• Month 4-6 (Tasks #1-4): 400 hours x $65 = $35,750 = 124,360 ADA
• Professional fees: including legal, accounting, and communications: $12,000 = 41,473 ADA
• SUBTOTAL: $109,500 = 380,909 ADA
• plus 10% project management and office expenses: $10,950 = 38,090 ADA

Total $120,450 equivalent to ADA 419,000

Who is in the project team and what are their roles?

• Nils Helset, 15th generation farmer in Norway. Extensive experience with precision agriculture services and agronomy - over 8 years experience in crop-producing, managing over 40k decares of pilot farmers with Felleskjøpet. Nils will be the agronomic advisor and project manager lead.

• Konstantin Varik - built advanced AI models in agriculture using remote sensing for over 8 years including AI model for crop-yield prediction for maize and soybeans for USA, Brazil and Argentina with 98-99% yield-prediction accuracy 1-1.5 months prior to harvesting (data provided to the USDA). Konstantin will be the technical lead, developing the algorithm, field delineation and vegetation time series.

• Alex Melnichouck founded B2B ag-tech startups Zoner.ag in 2012 (acquired by BASF in 2015), led and managed the global BASF digital farming team for over 4 years. Alex will be the strategic lead in the project, securing collaboration partnerships.

• Yosef Akhtman founded and grew Gamaya, Swiss-based ag-tech startup focused on remote sensing and AI funded by Mahindra. Yosef is a scientist and inventor with over 5 registered patents in agriculture and biology. Yosef will be the lead on developing the local super-resolution of Sentinel-2 from 10m to 1m per pixel resolution, trained locally in the region.

How does the cost of the project represent value for money for the Cardano ecosystem?

The project will represent significant value to the Cardano ecosystem as it addresses the key challenge objective including enabling and creating a completely decentralised system for agricultural field boundaries, measuring it's sustainability and creating transparency to the agricultural value chain in an independent ledger, which can be used to leverage across multiple facets of the value chain with food companies, multi-national consumer brands, and providing unbiased and the "truth" of sustainable practices.

Additionally, the project represents the largest undertaking of mapping and digitising agricultural fields in two of the most critical agricultural nations in the world, Kenya and Tanzania, both of these areas have never been digitally mapped before, and providing this data layer and fundamental blockchain with a digital ledger that creates, manages and monitors continuously the practices and changes in agricultural land, will be a first of its kind.