Wheat yield estimation based on analysis of UAV images at low altitude

Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
Kurchatov Genomics Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
Mathematical Center in Akademgorodok, 630090 Novosibirsk, Russia
Department of Mathematics and Mechanics, Novosibirsk State University, 630090 Novosibirsk, Russia

^* Corresponding author: ada@bionet.nsc.ru

Abstract

Information about the yield of wheat crops makes it possible to correctly assess their productivity and choose apropriate agronomic procedures to maximize yield. However, determining yields based on manual ear counts is labor intensive. Recently UAVs demonstrated high efficiency for rapid yield estimation. This paper presents a software package WDS (Wheat Detection System) for ears counting in wheat crops based on RGB images obtained from UAVs. WDS creates the flight plan, for the acquired images carries out automatic georeferencing to the appropriate fragment of the field, counts ears using the neural network models, reconstructs the density of ears in the crop and visualizes it as a heat map in the interactive web application. Based on the field experiment the accuracy of ears counting in plots was assessed: Spearman and Pearson correlation coefficients between the ears density counted manually and using WDS were 0.618 and 0.541, respectively (p-value < 0.05). WDS avaliable at https://github.com/Sl07h/wheat_detection.