The problem CropVest | Cultivating Success, Crop by Crop. solves

Sustainable Agriculture Investment Platform

The Sustainable Agriculture Investment Platform revolutionizes agriculture investment by empowering sustainable investments through providing a secure platform for investors to support farmers & ensure a stable food supply.

Key Benefits:

• Tokenize real-world crops using blockchain technology, in order to maximize transparaency and traceabaility, as well as bridge agriculture commodities markets into the decentralized finance space.

• Invest on time and with Purpose: Allocate capital to sustainable agriculture projects that align with your values and financial goals, enabling fast decisions for farmers. Timing is everything in agriculture!

• Strengthen Farmer-Financier Relationships: Connect farmers directly with investors for funding, fostering collaboration and growth.

• Secure Transactions: Blockchain technology ensures transparent and secure financial transactions.

• Mitigate Risks: Diversify investments across crop types, locations and farmers to reduce risks associated with market fluctuations and unforeseen events.

• Data-driven Decisions: Leverage data analytics for informed decision-making and optimized farming practices.

• Empower Local Communities: Support rural development and fair trade practices, empowering local economies.

Challenges we ran into

Overcoming Challenges in Satellite-Based Crop Yield Prediction

The concept of using satellites to predict crop yield seems straightforward: capture field images, assess crops, measure field area, and predict yield. However, numerous challenges hinder this process, including:

Clouds

Frequent cloud cover due to regular rain complicates satellite image capture, making it difficult to obtain clear crop pictures.

Overcoming Clouds: Advanced image processing techniques are employed to address cloud interference, including cloud masking and compositing of multiple cloud-free images.

Colour

Identifying crops based on satellite imagery relies on detecting "green" fields, but this method struggles to distinguish between various green vegetation types.

Spectral Analysis: Leveraging hyperspectral and multispectral imaging, advanced spectral analysis techniques enable better differentiation of crop types.

Phenology

Differentiating between similar crops and understanding specific crop types (e.g., sugar vs. cassava) is challenging due to multiple crops.

Phenological Indices: Utilizing remote sensing data and temporal vegetation indices, crops at different growth stages are distinguished.

Area v Yield

Identifying crop areas is one step, but predicting yield involves accounting for factors like weather conditions and plant health.
Yield Models: Complex predictive models integrate weather, soil, and historical yield data to estimate crop yield based on identified areas. Therefore data from different sources was combined: , including the European Soil Data Centre (ESDAC), Sentinel satellite data, and weather information. This amalgamation serves to enhance the precision and reliability of crop yield forecasts but increases comlexity.

Blockchain

Connecting and updating oracle data via Gelato web3 functions and configuring the molecule protocol contracts. Also, it was challenging embedding satellite map images on the front-end.