Sugarcane density mapping and estimation yield using UAV image and OBIA analysis | |
| Author | Jaturong Som-ard |
| Call Number | AIT Thesis no.RS-16-08 |
| Subject(s) | Sugarcane Aeronautics in agriculture Agriculture--Geographic information system |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Scince in Remote Sensing and Geographic Information Systems |
| Publisher | Asian Institute of Technology |
| Abstract | Estimating sugarcane yield is one of the direct ways to develop farmer's income in Thailand. This study was to develop a suitable classification model for sugarcane density mapping using UAV image and Object-Based Image Analysis (OBIA) technique and estimate yield. The dense maps were generated using Multi Resolution Segmentation. OBIA, GE, and farner's techniques were used to estimate yields, and these results were investigated using statistical methods. OBIA was segmented using new rule-setting. The overall accuracy of OBIA that KK3 and UT12 were succeeded 91 and 85% which were most satisfactory. Estimating yield with OBIA was 197.63 (KK3) and 152.78 (UTI2), and GE was 282.53 and 204.24. Farmer's was 283.09 and 250.02, while actual yields were 183 and 148 tons. The results were investigated using the mean deviation of KK3 as 14.63, 99.53, and 100.09, whereas UT12 as 4.78,56.24, and 102.29, respectably. Thus, OBIA had higher accuracy to classify sugarcane density maps. OBIA gave better results of estimating yield methods than another because the mean deviation values of OBIA were more closed to mean value of actual yield. In addition, this technique is most certainty to count a number of sugarcane stalks using dense maps. OBIA is high potential to segment structure of sugarcane canopy and leaf because it has to consider spectral, spatial, and the textural image objects. Also, it is high efficiency to segment shadow, soil, and grass land. Therefore, this study helps to develop varieties, and the farmers can use estimating methods for their further improvement and decision making. |
| Year | 2016 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Remote Sensing (RS) |
| Chairperson(s) | Sarawut Ninsawat; |
| Examination Committee(s) | Tripathi, Nitin Kumar;Soni, Peeyush;Vorraveerukorn Veerachitt; |
| Scholarship Donor(s) | Royal Thai Government;Asian Institute of Technology Fellowship |
| Degree | Thesis (M.Sc.) - Asian Institute of Technology, 2016 |