AI Case Study

United Phosphorous (UPL) and Microsoft machine learning predictions were used to alert farmers to the likelihood of pest infestation resulting in the doubling of farm income

United Phosphorous (UPL) and Microsoft machine learning predictions were used to alert farmers to the likelihood of pest infestation. About 3,000 small farmers with less than five acres in 50 small Indian villages receive automated calls indicating the likelihood of infestation. With advance notice and preventative actions it resulted in the doubling of farm income. The predictions were based on machine learning models that looked at weather conditions and crop stage in addition to sowing advisories.

Industry

Basic Materials

Agriculture

Project Overview

"In the first phase, about 3,000 marginal farmers with less than five acres of land holding in 50 villages across in Telangana, Maharashtra and Madhya Pradesh are receiving automated voice calls for their cotton crops. The calls indicate the risk of pest attacks based on weather conditions and crop stage in addition to the sowing advisories. The risk classification is High, Medium and Low, specific for each district in each state."

Reported Results

"Our collaboration with Microsoft to create a Pest Risk Prediction API enables farmers to get predictive insights on the possibility of pest infestation. This empowers them to plan in advance, reducing crop loss due to pests and thereby helping them to double the farm income,” says Vikram Shroff, Executive Director, UPL Limited."

Technology

Machine learning

Function

Strategy

Strategic Planning

Background

Microsoft in "collaboration with United Phosphorous (UPL), India’s largest producer of agrochemicals, led to the creation of the Pest Risk Prediction API that leverages AI and machine learning to indicate in advance the risk of pest attack.

Common pest attacks, such as Jassids, Thrips, Whitefly, and Aphids can pose serious damage to crops and impact crop yield. To help farmers take preventive action, the Pest Risk Prediction App, providing guidance on the probability of pest attacks was initiated."

Benefits

Data

"...the risk of pest attacks is based on weather conditions and crop stage in addition to the sowing advisories. The risk classification is High, Medium and Low, specific for each district in each state."