Article

CropQuant grows with crop to provide robot eye on performance

Published: 8 November, 2017

Earlham Institute's CropQuant at REAP 2017A small, portable robot that can ‘grow’ with the crop was demonstrated for the first time at REAP.

CropQuant, developed by the Zhou Laboratory at the Earlham Institute, provides continuous monitoring in-field of the micro-climate, providing unique insights into crop performance. CropQuant monitors the crop growth and its growing environment using a suite of sensors and an imaging ‘eye’ that can extend up to 3 metres. This allows visualisation of the crop canopy as it grows.

The data is processed using machine-learning based algorithms to create a very detailed picture of the way that a particular variety of wheat grows in the field, allowing comparison between different genotypes and a better understanding of how performance can be enhanced. It is well known that there is often a big gap between the potential yield of wheat and that actually attained.

The data obtained from CropQuant will allow analysis of the multiple factors involved to provide better models for forecasting and also decision making about application of fertiliser or the best timing for harvest. Dr Ji Zhou explains that the environmental factors that determine crop growth – soil moisture, humidity, temperature (ambient and soil) and light level – can now be measured easily with cheap sensors built into the robot, which then feed into the growth predictive model.

He says: “The data is analysed using machine-learning based algorithms to automate continuous measurement of crop performance such as crop height, growth rate, vegetation and lodging risk, which are key to yields. “Conditions vary widely across a field, so by moving the robot through the crop and imaging the canopy a much more detailed picture can be obtained than from a drone, for example.”

He continues: “Dynamic measurements from the crop also allow a much more accurate method of predicting the yield. For example, early establishment and stem elongation have a major impact on the performance of the crop and this can be visualised and quantified by CropQuant. It provides the farmer or breeder with a 24/7 alternative to walking the fields.”

At REAP Zhou showed how data from the robot can be visualised and displayed on a mobile device, allowing remote monitoring of the crop.