Redirigiendo al acceso original de articulo en 20 segundos...
Inicio  /  Agriculture  /  Vol: 13 Par: 10 (2023)  /  Artículo
ARTÍCULO
TITULO

Applying RGB-Based Vegetation Indices Obtained from UAS Imagery for Monitoring the Rice Crop at the Field Scale: A Case Study in Portugal

Romeu Gerardo and Isabel P. de Lima    

Resumen

Nowadays, Unmanned Aerial Systems (UASs) provide an efficient and relatively affordable remote sensing technology for assessing vegetation attributes and status across agricultural areas through wide-area imagery collected with cameras installed on board. This reduces the cost and time of crop monitoring at the field scale in comparison to conventional field surveys. In general, by using remote sensing-based approaches, information on crop conditions is obtained through the calculation and mapping of multispectral vegetation indices. However, some farmers are unable to afford the cost of multispectral images, while the use of RGB images could be a viable approach for monitoring the rice crop quickly and cost-effectively. Nevertheless, the suitability of RGB indices for this specific purpose is not yet well established and needs further investigation. The aim of this work is to explore the use of UAS-based RGB vegetation indices to monitor the rice crop. The study was conducted in a paddy area located in the Lis Valley (Central Portugal). The results revealed that the RGB indices, Visible Atmospherically Resistant Index (VARI) and Triangular Greenness Index (TGI) can be useful tools for rice crop monitoring in the absence of multispectral images, particularly in the late vegetative phase.

 Artículos similares

       
 
Dorijan Radocaj, Ante ?iljeg, Rajko Marinovic and Mladen Juri?ic    
Vegetation indices provide information for various precision-agriculture practices, by providing quantitative data about crop growth and health. To provide a concise and up-to-date review of vegetation indices in precision agriculture, this study focused... ver más
Revista: Agriculture

 
Allimuthu Elangovan, Nguyen Trung Duc, Dhandapani Raju, Sudhir Kumar, Biswabiplab Singh, Chandrapal Vishwakarma, Subbaiyan Gopala Krishnan, Ranjith Kumar Ellur, Monika Dalal, Padmini Swain, Sushanta Kumar Dash, Madan Pal Singh, Rabi Narayan Sahoo, Govindaraj Kamalam Dinesh, Poonam Gupta and Viswanathan Chinnusamy    
Phenomics technologies have advanced rapidly in the recent past for precision phenotyping of diverse crop plants. High-throughput phenotyping using imaging sensors has been proven to fetch more informative data from a large population of genotypes than t... ver más
Revista: Agriculture

 
Naledzani Ndou, Kgabo Humphrey Thamaga, Yonela Mndela and Adolph Nyamugama    
Crop characterization is considered a prerequisite to devising effective strategies for ensuring successful implementation of sustainable agricultural management strategies. As such, remote-sensing technology has opened an exciting horizon for crop chara... ver más
Revista: Agriculture

 
Yang Li, Bo Zhao, Jizhong Wang, Yanjun Li and Yanwei Yuan    
Accurate yield estimation before the wheat harvest is very important for precision management, maintaining grain market stability, and ensuring national food security. In this study, to further improve the accuracy of winter wheat yield estimation, machi... ver más
Revista: Agriculture

 
Xin Yang, Shichen Gao, Qian Sun, Xiaohe Gu, Tianen Chen, Jingping Zhou and Yuchun Pan    
Lodging depresses the grain yield and quality of maize crop. Previous machine learning methods are used to classify crop lodging extents through visual interpretation and sensitive features extraction manually, which are cost-intensive, subjective and in... ver más
Revista: Agriculture