著者

Baofeng SU Noboru NOGUCHI

出版者

日本生物環境工学会

雑誌

Environmental Control in Biology (ISSN:1880554X)

巻号頁・発行日

vol.50, no.3, pp.277-287, 2012 (Released:2012-10-30)

参考文献数

19

被引用文献数

1 3

---

The availability of agricultural land use information allows decision makers and managers to establish short-term and to long-term plans for land conservation and sustainable use. The objective of this study was to develop a method for extraction of agricultural land use information based on remote sensing imagery. By combining particle swarm optimization (PSO), k-means clustering algorithm and minimum distance classifier, a PSO-k-means-based minimum distance classifier for agricultural land use classification was developed. Crop planting information was collected and divided into five classes: water bodies, paddy fields, bean fields, wheat fields and others (windbreak, roads, rare areas, and buildings, etc.). K-means, a widely used algorithm in pattern recognition for unsupervised classification, became a part of supervised classification by using PSO to find the optimal initial position vectors in a training sample pretreatment process. The optimal cluster of each subclass was finally used for minimum distance classification. The results obtained from Miyajimanuma wetland land use information extraction showed that merely using a small feature space composed of the first three principal components of a SPOT 5 image enabled classification accuracy of 93%.

著者

Ryota ISHIBASHI Takuma TSUBAKI Shingo OKADA Hiroshi YAMAMOTO Takeshi KUWAHARA Kenichi KAWAMURA Keisuke WAKAO Takatsune MORIYAMA Ricardo OSPINA Hiroshi OKAMOTO Noboru NOGUCHI

出版者

The Institute of Electronics, Information and Communication Engineers

雑誌

IEICE TRANSACTIONS on Communications (ISSN:09168516)

巻号頁・発行日

vol.E105-B, no.4, pp.364-378, 2022-04-01

被引用文献数

2

---

To sustain and expand the agricultural economy even as its workforce shrinks, the efficiency of farm operations must be improved. One key to efficiency improvement is completely unmanned driving of farm machines, which requires stable monitoring and control of machines from remote sites, a safety system to ensure safe autonomous driving even without manual operations, and precise positioning in not only small farm fields but also wider areas. As possible solutions for those issues, we have developed technologies of wireless network quality prediction, an end-to-end overlay network, machine vision for safety and positioning, network cooperated vehicle control and autonomous tractor control and conducted experiments in actual field environments. Experimental results show that: 1) remote monitoring and control can be seamlessly continued even when connection between the tractor and the remote site needs to be switched across different wireless networks during autonomous driving; 2) the safety of the autonomous driving can automatically be ensured by detecting both the existence of people in front of the unmanned tractor and disturbance of network quality affecting remote monitoring operation; and 3) the unmanned tractor can continue precise autonomous driving even when precise positioning by satellite systems cannot be performed.