Use of machine learning for monitoring the growth stages of an agricultural crop

Abstract

As one of the world's major crops, oats (Avena sativa L.) require management strategies to increase their yield and quality. This study utilised an unmanned aerial vehicle (UAV) with multispectral image sensors to predict winter oats height (1.18 m at ripening stage) and yield (maximum >7.62 t per ha) using the normalised difference vegetation index (NDVI) and chlorophyll green vegetation index (CI green VI) across three different growth stages (flowering, grain filling and ripening). To corroborate the vegetation indices ground truth data on the measured crop yield, a variety of chemical soil health indicators (i.e. nitrogen, phosphorus, potassium, pH, and soil organic matter), and a crop quality indicator (β-glucan) were determined. A hierarchical multinomial logistic regression machine learning model was developed to predict the oats yield incorporating the chemical soil health indicators and crop quality indicator. The determined ‘combination model’ using the CI green VI, with 16 soil feature parameters, showed good specificity (0.87), sensitivity (0.95), and accuracy (0.93) at estimating the very high oat yield. Finally, the study provides the range of soil nutrient levels and the crop quality indicator that farmers must maintain to gain the highest oat yield at harvest. The findings of this research study will be particularly valuable as a Precision Agriculture management strategy for maximising winter oat yield and quality.