Abstract
Rice lodging identification relies on manual in situ assessment and often leads to a compensation dispute in agricultural disaster assessment. Therefore, this study proposes a comprehensive and efficient classification technique for agricultural lands that entails using unmanned aerial vehicle (UAV) imagery. In addition to spectral information, digital surface model (DSM) and texture information of the images was obtained through image-based modeling and texture analysis. Moreover, single feature probability (SFP) values were computed to evaluate the contribution of spectral and spatial hybrid image information to classification accuracy. The SFP results revealed that texture information was beneficial for the classification of rice and water, DSM information was valuable for lodging and tree classification, and the combination of texture and DSM information was helpful in distinguishing between artificial surface and bare land. Furthermore, a decision tree classification model incorporating SFP values yielded optimal results, with an accuracy of 96.17% and a Kappa value of 0.941, compared with that of a maximum likelihood classification model (90.76%). The rice lodging ratio in paddies at the study site was successfully identified, with three paddies being eligible for disaster relief. The study demonstrated that the proposed spatial and spectral hybrid image classification technology is a promising tool for rice lodging assessment.
Highlights
Grains are the foundation of social development, and efficient and accurate classification of agricultural lands can facilitate the control of crop production for social stability
After training samples were selected from the site, the single feature probability (SFP) value was texture analysis was conducted followed by image combination to produce spatial and computed, which was later used as the threshold value in the decision tree classification (DTC) process
After training samples were selected from the site, the SFP value was computed, classification accuracy was evaluated using maximum likelihood classification (MLC) and DTC, and the rice lodging ratio at the study which was later used as the threshold value in the DTC process
Summary
Grains are the foundation of social development, and efficient and accurate classification of agricultural lands can facilitate the control of crop production for social stability. According to statistics published by the Food and Agriculture Organization of the United Nations, among various grains, rice (Oryza sativa L.) accounts for 20% of the world’s dietary energy and is the staple food of. Frequent natural disasters such as typhoons, heavy rains, and droughts hinder rice production and can cause substantial financial losses for smallholder farmers [2,3,4,5], in intensive agricultural practice areas such as Taiwan. Many countries have implemented compensatory measures for agricultural losses caused by natural disasters [6,7]. According to the Implementation Rules of Agricultural Natural Disaster Relief in Taiwan, township offices must perform a preliminary disaster assessment within 3 days of a disaster and complete a comprehensive disaster investigation within 7 days. A sampled agricultural paddy with ≥20% lodging is considered a disaster area; to able to receive cash and project
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
