Abstract
Synecoculture ™ is a method of farming that produces useful plants while making multifaceted use of the
self organizing ability of the ecosystem by growing a wide variety of plants densely mixed in the same
farmland. As a technology to support Synecoculture , robotics are being developed to automate major
management tasks Still, the complexity of recognition and operation is imposing a heavy burden against
automation compared with conventional farming that is based on a uniform operation of a single plant.
On Synecoculture it is essential to grow plants with high diversity , but the dominance of some plants over
other s may change the species composition and occupancy in the ecosystem which might result in reduce d
diversity Pruning these excessively dominant plants is needed to maintain the balance of species
composition in the vegetation of Synecoculture . In this study, we aim to detect such overly propagating plants
that m ight reduce the diversity of the vegetation community (dominant plants).
The proposed method detects the dominant plants using the Chopped Picture Method (CPM), a
Convolutional Neural Network CNN learning method for segmenting RGB images. In this study, we treat
Mentha suaveolens ( as one of the dominant plants to be detected and trained the CNN with three
labels: “mint,” “plants other than mint” and “others.” As a result, we obtained high accuracy segmentation
in detecting the dominant plants, especially in distinguishing the plant group from the non plant group.
