Case Studies
By Sector
Advanced Search
By Sector
Advanced Search
How precise is precision farming?
Problem: To get timely & accurate alerts for risk of diseases and pests; and manage the usage of water to help farmers manage and monitor their financial resources.
Solution: The application uses farm level data to predict ideal growth conditions and resource requirements using machine learning. The output
Weather forecast bulletin for farming?
Problem: Agromet advisories are issued to combat extreme weather conditions and help farmers take immediate decisions on harvesting, draining excess water and other rejuvenation measures. There was no application which could track more than 40 weather and plant measurements to give deeper visibility into climate variability, crop health, and the decisions they need to factor, such as event timing and irrigation.
The Arable App fills this gap.
Solution: To have devices that collect and synthesize in-field climate and plant data to produce actionable insights in all growing conditions. The solution uses a robust sensor suite with rugged durability and cutting-edge global cellular connectivity.
Machine learning is used to provide deeper insight to climate variability, crop health, etc. by tracking more than 40 parameters.
Digital agronomy solutions
Problem: To improve the efficiency and sustainability of crop production by using digital agronomy solutions for large farmers or farming businesses
Solution: The AI based solutions are used in early detection of diseases and weeds to reduce potential crop losses.
AI driven 'farm to market' supply chain
Problem: Farmers experience price risk, information asymmetry about demand, distribution inefficiency, and late payment receival. Retailers face problems like higher costs, low quality and unhygienic produce, high price volatility, and the everyday hassle of going to the market.
The traditional Supply Chain is highly inefficient, disorganized, and has a high rate of food waste.
Solution: The application attempts to remove inefficiencies by putting in place AI driven farm to market supply chain.
Reduction of food wastage from the farm to the shelf.
Problem: Large amount of food wastage takes place in the journey from farm to the shelf. There is a need for an application that will work across fresh produce supply chains and reduce food waste by detecting variance from specifications and matching output to needs.
Solution: Machine learning and computer vision are used to digitize the quality assessment of fresh fruits and vegetables. The technology makes quality processes objective, efficient, and less wasteful.
Farm to fork traceability
Problem: To bring farm to fork traceability and ensure quality control.
Solution: The solution digitizes the farm management, while managing the data for the entire ecosystem.
Digitization of the farm management
Problem: To develop an application:
For accurate decision making
To reduce cost of operations
To provide effective credit risk assessment
To develop plot level monitoring system
For crop risk assessment at regional level
For risk adjusted variable pricing
Solution: The solution digitizes the farm management and provides capabilities of live reporting, analysis, interpretation and insight.
Seed to Shelf traceabilty with the help of Geo Tagging
Problem: To develop an application for:
End-to-end supply chain traceability
non-replicable QR code stickers
Customizable, tamper-proof, and weather-resistant labels
QR codes that can be scanned only using CropIn’s app to prevent counterfeiting
Solution: Geo tagging for accountability & accurate predictability and Incorporating end-to-end solutions
Computer Vision to be the eyes for Visually Challenged
Problem: Visually impaired people face multiple challenges in navigating public spaces which impacts their jobs and opportunities
Solution: The system uses AI camera that provided computer vision functions to the user. A Bluetooth-enabled earphone lets the user interact with the system via voice queries and commands, and the system responds with verbal information.