Farm-gate tech-enabled collection centre
Our solution is Farm-gate Collection Centres with integrated automation, mobile quality control and source traceability. Custom-built computer vision and machine learning algorithms enable fast and accurate sorting, grading, and quality control across parameters such as size, colour, and defects closer to the source.
For buyers, through “quality-at-source” we have a scannable, traceably grocery packaged at farm-gate to maximise hygiene (i.e. less manual handling) that improves quality and efficiency by reducing the number of quality-control steps and thus wastage, labor, and time. For farmers, our solution decreases labor and logistics costs and enables greater market access to high value buyers and direct consumer linkages.
For farmers, we address 3 major pain points – post-harvest supply chain inefficiency, lack of market linkages, and perishability. Prior to Covid-19, farmers and buyers suffered from high supply chain costs and inconsistent quality controls. In the tomato value chain, post-harvest wastage and inefficiency can be >20%; quality is inconsistent due to seasonality, local supply shocks, and fragmented supply base. Farmers have difficulty connecting to premium buyers (oftentimes 10-15% higher prices) due to rejections and non-standardised sorting and grading; short shelf-life lessens farmers’ market timing flexibility.
Covid-19 has amplified these problems even further. Beyond premium market access, farmers are struggling to find any market access at all. Similarly, rather than just supply chain inefficiency, farmers and buyers are struggling to maintain operations due to lack of labor and logistics. Additionally, perishable produce with low shelf-life is highly susceptible to market instability.
Buyers and customers also benefit from cleaner produce, reduced costs, higher quality and source traceability.
By locating the mobile CCs near farm-gate, we will leverage the advantage of establishing “quality-at-source” and lay the foundation for an end-to-end hygienic supply chain. For example, by immediately packaging agricultural produce at the farm-gate, there would be minimal environmental exposure all the way to the end customer. In addition, sorted and graded produce quality controlled “at-source” will be directly ticketed for the relevant buyer with minimal risk of rejection, thus maximising logistics cost-savings by reducing redundant logistics (e.g. transport to CC, and redundant reverse transport onward to a buyer that is located in a different geography) and by avoiding transporting tomatoes that have potential to be rejected by the buyer (i.e. wasted logistics). Quality at the source greatly improves the efficiency of the supply chain by match different buyers for different grade. For example, lower grade produce can be sent for processing while higher grade produce can be sent to premium buyers. In addition to the overall cost of logistics coming down, these efficiencies drive farmer access to higher value buyers.
Vision: "To improve lives for smallholder farmers and deliver quality food to customers by transforming agricultural value chains"
Our model:
1) Engaging in value chain development interventions with a focus on post-harvest supply chain efficiency and premium market linkages
2) Scaling these market linkages by marketing and selling products under our brand.
Upaya Social Ventures will be our impact M&E partner and have signed an MOU with them to conduct case studies, baseline studies and impact measurement. GIZ has linked us to farmer groups in Narayangaon area and we have run several pilot procurements.
Automated sorting and grading (tomato) FCCs, we calculated potential scale of in terms of market size as:
· Short-term 5,750 MT per annum (950MT e-Grocery, 2,500 MT Modern Retail, 2,300 MT processing) for total available impact of INR 2Cr per annum farmer benefit.
· Long-term (5-years) Including QSR and 5% of informal market - 274,000 MT and total available impact of 80 Cr / yr per annum.
Impact of Covid-19 on these estimates:
Informal market will be accessed earlier and at greater scale due to anticipated restrictions on wholesale markets. In addition, we have partnered with several direct “farm-to-consumer” initiatives to help them scale their markets.
- Improve supply chain practices to reduce food loss, scale new business models for producer-market connections, and create low-carbon cold chains
Supply chains in India are unorganised and fragmented leading to increased wastage and inefficiencies. Sorting and grading is currently done manually resulting in inaccuracies and over-handling. Farmers are unable to access higher value markets due to a lack of market linkages and awareness of product specifications yet, the retail markets are desperate to differentiate their assortments but are unable to secure regular supplies and therefore compete with local street sellers. Customers want assurances over their produce i.e. clean, pesticide free but have no way of accessing these reliably. Our solution avoids these pain points thorough improved processes and technical innovation.
- Pilot: An organization deploying a tested product, service, or business model in at least one community
- A new business model or process
By locating the mobile CCs at or near farm-gate, we will leverage the advantage of establishing “quality-at-source” and lay the foundation for an end-to-end hygienic supply chain. For example, by immediately packaging agricultural produce at the farm-gate, there would be minimal environmental exposure all the way to the end customer. In addition, sorted and graded produce quality controlled “at-source” will be directly ticketed for the relevant buyer with minimal risk of rejection, thus maximising logistics cost-savings by reducing redundant logistics (e.g. transport to CC, and redundant reverse transport onward to a buyer that is located in a different geography) and by avoiding transporting tomatoes that have potential to be rejected by the buyer (i.e. wasted logistics). Quality at the source greatly improves the efficiency of the supply chain by match different buyers for different grade. For example, lower grade produce can be sent for processing while higher grade produce can be sent to premium buyers. In addition to the overall cost of logistics coming down, these efficiencies drive farmer access to higher value buyers. Over time, these efficiencies work to strengthen network relationships and to help buffer regional supply volatility.
We will form a collaborative adaptation of Occipital Tech's proprietary software for use in the agricultural sector, "Agrograde" is a smartphone-based AI enabled grading and QC solution which processes images and identifies the grade of a sample of commodity.
Agrograde uses a combination of custom built computer vision and machine learning algorithms deployed over cloud for fast and accurate identification and classification of various parameters. The machine learning models are trained over a vast dataset which helps us provide accurate and reliable results for agricultural produce like tomatoes, pomegranate or apples. Our technology processes images of multiple samples within a few minutes, thus helping users make data driven decisions more quickly. Existing parameter capabilities include size and colour distribution, detection of major defects, approximate weight of samples, location of QC performed, and time of inspection.
Automated sorting/grading - we worked with Occipital Tech to adapt their tech and piloted our model with BigBasket (BB). Currently, BB maintains quality control points at both collection and distribution centres on top of the sorting/grading already done by farmers. This is labor-intensive and time-consuming - at least 3x manual sorting/grading, 2-3 days, and ~5% wastage from transit, handling, and over-ripening. This results in losses to both small farmers and buyers, in our pilot, up to 25% cumulatively. We ran 5 transactions of 432kg of tomatoes with an incremental farmer benefit of ~12%. We project long-term impact for BB of 2/3 reduced labor, 1 less transit day, and 2/3 the manual handling. For farmers, ~15% impact through reduced costs and price premium.
- Artificial Intelligence / Machine Learning
- Big Data
- Imaging and Sensor Technology
- Manufacturing Technology
- Software and Mobile Applications
Overall, this initiative will be crucial in developing a network of quality-focused buyers across grades and geographies. In turn, this will allow a large network of small and marginal farmers to command higher premiums and maximise farmer outcomes from a "full harvest". We have piloted this initiative in Nashik region and seen the potential there, next we need to establish a fully working FCC and then go on to develop a network using our existing farmer groups in different geographies. Each farmer group works with on average 200 farmers so every new centre that opens will create new jobs and improve the livelihoods of the participating farmers in each location.
- Rural
- Urban
- Poor
- Low-Income
- 1. No Poverty
- 2. Zero Hunger
- 9. Industry, Innovation, and Infrastructure
- 11. Sustainable Cities and Communities
- India
- India
Funds will go establishing two Farm-gate Collection Centres, automated sorting and grading. Currently in the pilot phase we are working with one collection centre who works with approximately 200 marginal farmers.
For the first year we plan to open 2 collection centres having an approximate outreach of 400 farmers. 5,750 MT per annum (950MT e-Grocery, 2,500 MT Modern Retail, 2,300 MT processing) for total available impact of INR 2Cr per annum farmer benefit.
In 5 years we anticipate a network of CCs impacting approximately 20,000 farmers
Long-term (5-years) Including QSR and 5% of informal market - 274,000 MT and total available impact of 80 Cr / yr per annum.
Overall, we plan to grow and sustain by scaling the system across geographies and target market segments. In the first stage, we will engage with e-Grocery and Modern Retail and eventually into QSR and food processing. Scaling across geographies will be through association with our partners such as GIZ. In the second stage, we will enter informal markets in major metros alongside distribution partners.
Within our target market segment, Krishi Star’s brand positioning is as a supplier of global-best quality, natural foods at affordable, local prices. We position ourselves in the premium category vis-à-vis domestic competitors. Our direct competitors for majority of our products are domestic low-cost producers and imports.
In sorting and grading, our competitors still rely on manual methods and have not adapted technology or automated processes. Our technical partner will need to work closely with us to develop our solution with the end users in mind.
FCC’s will be initially run and managed by Krishi Star; we envision the model to eventually create farmer-ownership as we transfer operations of the FCC to farmers through Build-Operate-Transfer (BOT) model post Year 1.
In long-term, the coordinated network of FCCs becomes a trusted platform through which future innovations can be launched. For example, future interventions in areas such as Global GAP would benefit from the accountability and transparency of the FCC. This platform would be the perfect medium on which to launch such initiatives.
We have signed an MOU with Occipital, our tech partner and have agreed a way forward whereby we have access to the data that is collected at our centres while they continue to develop the front end of the application and focus on UX.
The two initial pilot projects forming the foundation of these initiatives were run with the support of GIZ. Going forward, FCCs established through the fund support would be sustainable through internal accruals after launch. FCC gross margin is projected at approximately 10% with net margins around 3-5%. The FCC would be sustainable on 100% earned revenues after the initial pilot period. Krishi Star would raise additional capital if necessary or partner with other organisations for launch of subsequent FCCs.
Krishi Star will initially operate the FCC and later, once the FCC has stabilised operations (~2 years), we will transfer ownership to the associated farmer group (BOT) and serve as a quality control and market linkage partner. Krishi Star will coordinate across multiple FCCs.
- For-profit, including B-Corp or similar models
Full Time:
Bryan Lee - CEO
Agastya Chopra - Director
David Nott - Director
Abdul Rahiman - Head of Finance
Part Time:
Nissim Chandorkhar - Special Projects
John Matarazzo - Special Projects
Evangeline Joseph - Special Projects
Our team has a diverse set of experiences in both on-ground food and agriculture businesses and various development projects across agricultural value chains. Most recently, core members of the team are driving a project in partnership with Maharashtra State Agricultural Marketing Board and Asian Development Bank to build a platform connecting farmers directly to consumers.
Prior to Krishi Star, Bryan had eight years of entrepreneurial experience as founder of Social Point Solutions, co-founder of Kingmin Steel and Associates and Head of Marketing/Partner at Nemesis Records. In addition, he spent three years as a consultant with Accenture. Bryan has an MBA from Kellogg School of Management and a BS (Summa Cum Laude) in Engineering from Cornell University.
Agastya has expertise developed overseeing manufacturing and business development at Anca Leathers. He is also the co-founder of Gaia Conservation, an NGO devoted to actively engaging community in environmental conservation. Agastya has a degree in Business Management from Rochester Institute of Technology.
David has leadership expertise of more than 25 years in managing a variety of Stores with Marks & Spencer and Waitrose. His portfolio includes managing the Covent Garden Flagship for Marks & Spencer and Waitrose Flagship in Kensington. He is also a certified executive coach and a graduate from Queen Mary’s University
Abdul has experience as manager at Excel Crop Care where he was the regional head for North Africa region. He has a PGP-ABM from IIM Ahmedabad and BSci (Agri) from Kerela Agricultural University.
ADB - Krishi Star has recently embarked on projects to assess impact of technology aided interventions in post harvest management
GiZ - Krishi Star has been working with the German Development agency for 2 years on tomato and apple value addition projects in Narayangaon and Himachal Pradesh
Go4Fresh - Krishi Star has recently partnered with this company to deliver vegetable boxes from farm to consumer during the COVID-19 crisis
Krishi Star generates revenue through the sale of our products. Currently, our customer-base is primarily hotels and restaurants in urban centres and online retail outlets.
Our model is two-fold (1) value chain development interventions with a focus on post-harvest supply chain efficiency and premium market linkages and (2) scaling these market linkages by marketing and selling products through our food brand – Krishi Star.
On-the-ground – Market-building Currently, we are supplying a range of tomato products (whole peeled tomatoes, tomato puree, sun-dried tomatoes, dried mushroom, etc) to 100+ institutional buyers, mainly Italian and Mediterranean cuisine focused hotels and restaurants and online retail suppliers in Mumbai, Pune, Delhi, Bangalore and Goa. Through our supply chain partners, we have manufacturing capabilities including canning and pulping, dehydration, IQF, and spray drying. Our supply chain network supplies customers including PepsiCo, Coca-Cola, Nestle, and Weikfield Foods.
In addition, we are supplying fresh apples from Himachal Pradesh to retail and high-end processing such as Nature’s Basket and Cure.Fit (Eat.Fit).
- Organizations (B2B)
Overall, this initiative will be crucial in developing a network of quality-focused buyers across grades and geographies. In turn, this will allow a large network of small and marginal farmers to command higher premiums and maximise farmer outcomes from a "full harvest", directly fulfilling the 10th stated United Nations Millennium Development Goal of "Supporting the Marginalised and Disadvantaged."
If we are successful in The Solve 2020 global challenge initiative this would allow us to have a guided expansion of our piloted model utilising the extensive support network that MIT can offer. Scaling our project will not only impact more tomato farmers, it will provide a template for replication of our model in different commodities and counties where the potential partnerships the MIT can offer can be fully explored.
- Solution technology
- Funding and revenue model
- Board members or advisors
Solution Technology - Krishi star is an Ag-market business and so we are looking for support to develop this automatic sorting and grading solution using an existing application. We would need support in ensuring that our end case solution is compatible with the existing platform and can be easily modified to allow for new commodities and geographies.
Funding/Revenue - We are looking for funding to help scale our initiative and bring on the necessary resource to manage the collection centres and carry out the necessary training of personnel in these locations.
Board Members - Krishi Star is interested in bringing on either Board Members or Advisors who can share their experiences in this field.
We currently partner with the German Development Agency - GiZ and we would like to continue to work closely with them on this initiative. Their expertise is in the pre-harvest management and coordination of farmer groups which will prove valuable to us.
We work with a variety of farmer groups typically separated geographically and so would like to continue to grow this network in the areas where we will be opening new collection centres.
One of the tech solutions that we are proposing uses AI and Machine Learning to determine the size and the ripeness of tomatoes. If we were successful in winning this prize we would like to roll out this solution with a platform that can work with a variety of fruits and vegetables to determine the grading. Individual specifications, temperatures, photos, probable pest infestations, etc will all need to be captured and tested to create a robust grading tool. In order to achieve great accuracy, the system will need to have vast data sets captured and input across the produce spectrum. This will require additional resource and infrastructure as well as critical timeline coordination.