Chenghai Yang, Bradley K. Fritz, Charles P.-C. Suh
{"title":"在有人驾驶飞机和无人驾驶飞机上使用消费级相机进行航空图像采集和反射率转换的实用方法","authors":"Chenghai Yang, Bradley K. Fritz, Charles P.-C. Suh","doi":"10.1007/s11119-024-10145-w","DOIUrl":null,"url":null,"abstract":"<p>Consumer-grade cameras have emerged as a cost-effective alternative to conventional scientific cameras in precision agriculture applications. However, there is a lack of information on their appropriate use and calibration. This study focused on developing practical methodologies for determining optimal camera settings and converting image digital numbers (DNs) to reflectance. Two Nikon D7100 and two Nikon D850 cameras with visible and near-infrared (NIR) sensitivity were deployed on both manned and unmanned aircraft for image acquisition. To optimize camera settings, including exposure time and aperture, an approach that considered flight parameters and image histograms was employed. Linear and nonlinear regression analyses based on multiple nonlinear models were performed to accurately characterize the reflectance-DN relationship across all four bands (blue, green, red and NIR) based on seven calibration tarps. The results revealed that the exponential model with vertical translation was the optimal model for reflectance conversion for both camera types. Based on the optimized camera parameters and the optimal model type, this study provided an extensive analysis of the models and their root mean square errors (RMSE) derived from all 952 possible 2- to 6-tarp combinations for all bands in both camera types. This analysis led to the selection of optimal tarp combinations based on the desired level of accuracy for each of the five multi-tarp configurations. As the number of tarps increased to 4, 5, or 6, the RMSE values stabilized for all bands, indicating 4-tarp combinations were the optimal choice. These findings hold significant practical implications for practitioners in precision agriculture seeking guidance for configuring consumer-grade cameras effectively while ensuring accurate reflectance conversion.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"9 1","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Practical methods for aerial image acquisition and reflectance conversion using consumer-grade cameras on manned and unmanned aircraft\",\"authors\":\"Chenghai Yang, Bradley K. Fritz, Charles P.-C. Suh\",\"doi\":\"10.1007/s11119-024-10145-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Consumer-grade cameras have emerged as a cost-effective alternative to conventional scientific cameras in precision agriculture applications. However, there is a lack of information on their appropriate use and calibration. This study focused on developing practical methodologies for determining optimal camera settings and converting image digital numbers (DNs) to reflectance. Two Nikon D7100 and two Nikon D850 cameras with visible and near-infrared (NIR) sensitivity were deployed on both manned and unmanned aircraft for image acquisition. To optimize camera settings, including exposure time and aperture, an approach that considered flight parameters and image histograms was employed. Linear and nonlinear regression analyses based on multiple nonlinear models were performed to accurately characterize the reflectance-DN relationship across all four bands (blue, green, red and NIR) based on seven calibration tarps. The results revealed that the exponential model with vertical translation was the optimal model for reflectance conversion for both camera types. Based on the optimized camera parameters and the optimal model type, this study provided an extensive analysis of the models and their root mean square errors (RMSE) derived from all 952 possible 2- to 6-tarp combinations for all bands in both camera types. This analysis led to the selection of optimal tarp combinations based on the desired level of accuracy for each of the five multi-tarp configurations. As the number of tarps increased to 4, 5, or 6, the RMSE values stabilized for all bands, indicating 4-tarp combinations were the optimal choice. These findings hold significant practical implications for practitioners in precision agriculture seeking guidance for configuring consumer-grade cameras effectively while ensuring accurate reflectance conversion.</p>\",\"PeriodicalId\":20423,\"journal\":{\"name\":\"Precision Agriculture\",\"volume\":\"9 1\",\"pages\":\"\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2024-05-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Precision Agriculture\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1007/s11119-024-10145-w\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Precision Agriculture","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s11119-024-10145-w","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
Practical methods for aerial image acquisition and reflectance conversion using consumer-grade cameras on manned and unmanned aircraft
Consumer-grade cameras have emerged as a cost-effective alternative to conventional scientific cameras in precision agriculture applications. However, there is a lack of information on their appropriate use and calibration. This study focused on developing practical methodologies for determining optimal camera settings and converting image digital numbers (DNs) to reflectance. Two Nikon D7100 and two Nikon D850 cameras with visible and near-infrared (NIR) sensitivity were deployed on both manned and unmanned aircraft for image acquisition. To optimize camera settings, including exposure time and aperture, an approach that considered flight parameters and image histograms was employed. Linear and nonlinear regression analyses based on multiple nonlinear models were performed to accurately characterize the reflectance-DN relationship across all four bands (blue, green, red and NIR) based on seven calibration tarps. The results revealed that the exponential model with vertical translation was the optimal model for reflectance conversion for both camera types. Based on the optimized camera parameters and the optimal model type, this study provided an extensive analysis of the models and their root mean square errors (RMSE) derived from all 952 possible 2- to 6-tarp combinations for all bands in both camera types. This analysis led to the selection of optimal tarp combinations based on the desired level of accuracy for each of the five multi-tarp configurations. As the number of tarps increased to 4, 5, or 6, the RMSE values stabilized for all bands, indicating 4-tarp combinations were the optimal choice. These findings hold significant practical implications for practitioners in precision agriculture seeking guidance for configuring consumer-grade cameras effectively while ensuring accurate reflectance conversion.
期刊介绍:
Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming.
There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to:
Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc.
Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc.
Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc.
Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc.
Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc.
Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.