Development of a computer vision system for recognizing grain seed samples

Denis Serdechnyy; Otari Didmanidze1; Maria Karelina; Vladimir Filatov; Dmitriy Rybakov; Nikita Andriyanov; Sergey Korchagin; Yuliya Kafiyatullina

Denis Serdechnyy The State University of Management https://orcid.org/0000-0003-3060-9469
Otari Didmanidze1 The State University of Management https://orcid.org/0000-0003-2558-0585
Maria Karelina The State University of Management https://orcid.org/0000-0003-0335-7550
Vladimir Filatov The State University of Management https://orcid.org/0000-0002-9477-9013
Dmitriy Rybakov The State University of Management https://orcid.org/0000-0003-3455-6026
Nikita Andriyanov The State University of Management https://orcid.org/0000-0003-0735-7697
Sergey Korchagin The State University of Management https://orcid.org/0000-0001-8042-4089
Yuliya Kafiyatullina The State University of Management https://orcid.org/0000-0001-9569-7530

Keywords: computer vision, object recognition, machine learning, wheat seed bank, convolutional neural networks

Abstract

This article presents the development of a computer vision system for recognizing suitable samples of grain crops for further formation of the seed fund. The goal of the work is to create a reliable and efficient algorithm that can accurately determine the presence and characteristics of damage in images of wheat, oats, and peas. Modern machine learning methods, such as convolutional neural networks, are introduced to train the recognition model. The development process included the collection and preparation of training data, selection and configuration of the neural network architecture, as well as testing and optimization of the algorithm. A comparison was made of the computer vision libraries YOLO, FASTER R-CNN, VISSL, OpenCV. The resulting system demonstrated high accuracy in recognizing defects and morphological characteristics of seeds in test images, with an accuracy of up to 87%. The developed system can be used in various applications related to the automation of agricultural processes, product quality analysis, photo sorters, phenotyping devices, as well as in seed quality control systems and in intelligent control systems for agricultural production processes in crop production.