Monitoring of pea crops with neural network processing of images obtained using UAVs

Abstract

The article is devoted to the development and testing of technology for recognizing pea sprouts and estimating its biomass based on images from UAVs using neural networks. Rocket peas were sown by the “Kuzbass” sowing complex in the Topkinsky district of the Kemerovo Region on an area of 21.55 hectares. The soil type is slightly leached chernozem. The predecessor is spring wheat. The seed depth is 6 cm, the seeding rate is 1.1 million seeds per 1 hectare. Aerial photography was performed three weeks later with a quadcopter with a 20MP camera resolution from a flight altitude of 3 m. The shooting was carried out in two stages — in the early morning in cloudy conditions to obtain images of pea shoots without shadows and in the daytime with shadows from sprouts and weeds. As a result, two sets of 120 source photos were generated to train the neural network. Based on the obtained datasets, the Ultralytics YOLOv8 neural network model was trained. Testing of the obtained models was performed in a Python program for batch image processing and counting the number of plants in each image. The accuracy of recognizing sprouts on the first dataset was 97.3%, on the second — 67.3%. This is due to the different shooting conditions. Combining the two datasets allowed for a recognition accuracy of 94.7%. This is slightly lower than the first option, but much closer to the actual conditions of aerial photography. The result of the work is a program that allows batch image processing for automatic counting of pea sprouts and calculating their area in the images.

Plant phenotyping relevance

UAV画像とニューラルネットワークを用いて、エンドウ苗の認識、個体数計数、面積およびバイオマス推定技術を開発・検証しており、表現型取得手法が研究の中心である。

Code and data availability

The paper describes UAV pea imagery datasets, YOLOv8 models, and a Python counting program, but provides no public deposit or availability statement for any of them. Roboflow, Yandex Datasphere, and ExactFarming are third-party services used, not paper-specific public assets; all other URLs are references.

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