Using Satellite Imagery to Detect and Assess the Damage of Armyworms in Farming

Within two months, the team developed tools to collect satellite image data and created machine learning models for the automatic damage assessment in farming caused by armyworms. This has gone a long way in solving the problem of damage assessment of army worms attack on plants. The partner company OKO is a Google for Startups accelerated insurtech startup using satellite and mobile technology to bring affordable and simple crop insurance to smallholder farmers.

The problem

Armyworms are caterpillar pests of grass pastures and cereal crops, where they attack grains and feed on leaves. A very hungry caterpillar is rampaging through crops across the world, leaving a trail of destruction in its wake. The fall armyworm, also known as Spodoptera frugiperda (fruit destroyer), loves to eat corn but also plagues many other crops vital to human food security, such as rice and sorghum.

This invasive eating machine originated in the Americas and has gone global in recent years. It was reported in Africa in 2016 with a noticeable outbreak in Mali, Ivory Coast, and Ouganda. 

Another type of invasive insect is the Desert Locust, one of the species of short-horned grasshoppers (Acridoidea). During plagues, Desert Locusts may spread over an enormous area of some 29 million square kilometers. The spread of these pests into the world will cause massive pressure on the global food production systems.

The project outcomes

The team developed an AI pipeline for generating, preprocessing, and training classification algorithms; with a developed web application connected to the deployed model solving the problem of damage assessment of army worms attack on plants.

Using satellite images, the team was able to detect and identify the damage assessment of either or all together (depending on data availability):

• Fall Armyworm
• Africa Armyworm
• Locust Desert, with surge/outbreak in Mali (or Ivory Coast or Ouganda)