An Automated Crop Growth Detection Method Using Satellite Imagery Data

Dong-Chong Hsiou, Fay Huang, Fu Jie Tey, Tin-Yu Wu and Yi-Chuan Lee
Additional contact information
Dong-Chong Hsiou: Information Management Center, Council of Agriculture Executive Yuan, Taipei City 10014, Taiwan
Fay Huang: Department of Computer Science and Information Engineering, National Ilan University, Yilan 260007, Taiwan
Fu Jie Tey: Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan
Tin-Yu Wu: Department of Management Information Systems, National Pingtung University of Science and Technology, Pingdong 912301, Taiwan
Yi-Chuan Lee: Department of Biotechnology and Animal Science, National Ilan University, Yilan 260007, Taiwan

Agriculture, 2022, vol. 12, issue 4, 1-25

Abstract: This study develops an automated crop growth detection APP, with the functionality to access the cadastral data for the target field, that was to be used for a satellite-imagery-based field survey. A total of 735 ground-truth records of the cabbage cultivation areas in Yunlin were collected via the implemented APP in order to train a deep learning model to make accurate predictions of the growth stages of the cabbage from 0 to 70 days. A regression analysis was performed by the gradient boosting decision tree (GBDT) technique. The model was trained on multitemporal multispectral satellite images, which were retrieved from the ground-truth data. The experimental results show that the mean average error of the predictions is 8.17 days, and that 75% of the predictions have errors less than 11 days. Moreover, the GBDT algorithm was also adopted for the classification analysis. After planting, the cabbage growth stages can be divided into the cupping, early heading, and mature stages. For each stage, the prediction capture rate is 0.73, 0.51, and 0.74, respectively. If the days of growth of the cabbages are partitioned into two groups, the prediction capture rate for 0–40 days is 0.83, and that for 40–70 days is 0.76. Therefore, by applying appropriate data mining techniques, together with multitemporal multispectral satellite images, the proposed method can predict the growth stages of the cabbage automatically, which can assist the governmental agriculture department to make cabbage yield predictions when creating precautionary measures to deal with the imbalance between production and sales when needed.

Keywords: multispectral satellite imagery; multitemporal satellite imagery; artificial intelligence; gradient boosting decision tree (GBDT); heading cabbage (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/12/4/504/pdf (application/pdf)
https://www.mdpi.com/2077-0472/12/4/504/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:12:y:2022:i:4:p:504-:d:785969

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().