北海道大学 大学院農学院 修士論文発表会，2020 年 2 月 7 日

Detection and Localization of Pumpkin by UAV System
環境資源学専攻 生物生産工学講座 ビークルロボティクス 范 顔岩

1. Introduction
It is reported that Japan is now facing a problem of super-aging, accompanying with which is a labor
shortage on agriculture. This puts a burden on farmers especially when harvesting heavyweight crops.
Under this situation, the automation of agricultural machinery becomes an urgent need. With the wide
spread of smart agriculture in Japan, people can now use robotic tractors to implement precision
agriculture. This research is the first step for realizing an automatic pumpkin harvesting system, which isto get a position map of pumpkins before harvesting. In this study, a pumpkin detection and localization
system was implemented based on a UAV system and a post-processing of machine learning.

2. Methods
The study was conducted in the pumpkin field of Hokkaido Agricultural Research Center, Sapporo. Flight
experiments were conducted during August and September in 2019. The UAV system consists of an
agricultural drone mounted with a multispectral camera, which can record 5 bands of light spectrum, Blue,Green, Red, Near Infra-red and Red-Edge. After the acquisition of data, photos were mosaicked into
complete maps cover the whole field. A machine learning process of logistic regression was run withsamples taken from these maps. The 5 bands’ spectral information were taken as 5 features in the model.
With the machine learning process finished, parameters were exported and then taken into calculation ofhypothesis with each map pixel. Based on the hypothesis, we were able to classify each pixel as pumpkin
or background. After a denoising, the detection section was finished with a binary map outputted. For thelocalization section, the “regionprops” function was used to get the centroids of pumpkin regions in thebinary map. Centroids of pumpkins were saved in a CSV format file. After that, a converting from image
coordinate to geographic coordinate was operated. Then the GPS information of pumpkins was finallycreated.

3. Results and Discussion
In the georeferencing section, a comparison was made to evaluate the effect of georeferencing. It provedthat doing georeferencing improves the accuracy of mosaicked map significantly. In the post processing
section, precision-recall was chosen as evaluation indexes to the detection performance. Comparing theresult of different varieties revealed the importance of data balance between different kinds of samples.
The result under different scales showed that low resolution maps are less likely to classify small areanoises as pumpkins but more likely to lose pumpkins which are covered a lot. It also showed the system
has a good robustness to scale change. Comparison among different growth conditions showed an
influence caused by leaves. The further researches were supposed to solve these problems at first.

4. Conclusions
A detection and localization system for pumpkin was accomplished by acquiring data with UAV systemand processing data with machine learning. Result analyses showed the system has a high precision andrecall to be feasible. The robustness to scale change was also proved in this research.