Scientists from Bioversity International and the International Center for Tropical Agriculture (CIAT) are using artificial intelligence to analyse drone images shot above the soil to better understand how root crops respond to drought or heat beneath the soil. The work utilises CIAT’s Pheno-i platform, which relays real-time data to scientists, to help develop more climate-resilient crops.
Pheno-i is a web-based application originally developed by CIAT to rapidly analyse drone and satellite images, providing decision support to plant breeders and farmers. The platform maps physical features to evaluate water flow, germination and productivity zones of individual farms. Growers can assess traits quickly such as plant height, canopy cover, biomass, nitrogen use efficiency, resistance to pest and diseases, canopy temperature, etc.
Root crops like cassava, carrots and potatoes are notoriously good at hiding disease or deficiencies which might affect their growth. While leaves may look green and healthy, farmers can face nasty surprises when they go to harvest their crops.
This also poses problems for plant breeders, who have to wait months or years before knowing how crops respond to drought or temperature changes. Not knowing what nutrients or growing conditions the crop needs early on also hinder crop productivity.
New research using machine learning to help predict root growth and health with above-ground imagery was published 14 June, 2020, in Plant Methods.
“One of the great mysteries for plant breeders is whether what is happening above the ground is the same as what’s happening below,” explained Michael Selvaraj, a crop physiologist and co-author of the paper published by the Bioversity International and CIAT alliance.
“That poses a big problem for all scientists,” he continued. “You need a lot of data – plant canopy, height, and other physical features that take a lot of time and energy, and multiple trials – to capture what is really going on beneath the ground and how healthy the crop really is.”
Pheno-i – machine learning for real time crop monitoring
While drones and hardware for capturing physical images through crop trials are becoming cheaper and more accessible, analysing vast quantities of visual information to interpret it into useful data for breeders remains a challenge. However, using drone images, the Pheno-i platform can now merge data from thousands of high-resolution images, analysing them through machine learning to produce a spreadsheet. This shows scientists exactly how plants are responding to stimuli in the field in real time.
Using the technology, breeders can now respond immediately, applying fertiliser if a particular nutrient is lacking, or water. The data also allows scientists to quickly discover which crops are more resistant to climate shocks, so they can advise farmers to grow more drought or heat-resilient varieties.
“We’re helping breeders to select the best root crop varieties more quickly, so they can breed higher yielding, more climate-smart varieties for farmers,” noted Selvaraj. “The drone is just the hardware device, but when linked with this precise and rapid analytics platform, we can provide useful and actionable data to accelerate crop productivity.”
The technology also holds promise for other crops: “Automated image analytical software and machine learning models developed from this study is promising and could be applied to other crops than cassava to accelerate digital phenol-typing work in the alliance research framework,” said Joe Tohme, an alliance research director for crops for nutrition and health.
About the alliance
The Cali, Colombia-based alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) delivers research-based solutions that harness agricultural biodiversity and sustainably transform food systems to improve people’s lives. Alliance solutions address the global crises of malnutrition, climate change, biodiversity loss, and environmental degradation. The alliance is part of CGIAR, a global research partnership for a food-secure future.
