Authors:
Haruka Ide
1
;
Hiroyuki Ogata
2
;
Takuya Otani
3
;
Atsuo Takanishi
1
and
Jun Ohya
1
Affiliations:
1
Department of Modern Mechanical Engineering, Waseda University, 3-4-1, Ookubo, Shinjuku, Tokyo, Japan
;
2
Faculty of Science and Technology, Seikei University, 3-3-1, Kichijoji-kitamachi, Musashino-shi, Tokyo, Japan
;
3
Waseda Research Institute for Science and Engineering, Waseda University, 3-4-1, Ookubo, Shinjuku, Tokyo, Japan
Keyword(s):
Synecoculture, Agriculture, Deep Learning, Robot Vision.
Abstract:
Synecoculture cultivates useful plants while expanding biodiversity in farmland, but the complexity of its management requires the establishment of new automated systems for management. In particular, pruning overgrown dominant species that lead to reduced diversity is an important task. This paper proposes a method for detecting overgrown plant species occluding other species from the camera fixed in a Synecoculture farm. The camera acquires time series images once a week soon after seeding. Then, a deep learning based semantic segmentation is applied to each of the weekly images. The plant species map, which consist of multiple layers corresponding to the segmented species, is created by storing the number of the existence of that plant species over weeks at each pixel in that layer. Finally, we combine the semantic segmentation results with the earlier plant species map so that occluding overgrown species and occluded species are detected. As a result of conducting experiments usi
ng six sets of time series images acquired over six weeks, (1) UNet-Resnet101 is most accurate for semantic segmentation, (2) Using both segmentation and plant species map achieves significantly higher segmentation accuracies than without plant species map, (3) Overgrown, occluding species and occluded species are successfully detected.
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