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

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.

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.

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.

Problem: Farmers are unable to find appropriate markets for their produce due to a mismatch in demand and supply

Solution: Jivabhumi Agri Tech utilizes technologies such as blockchain to get the produce data at different stages in the distribution chain.

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.

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.

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.

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