{"title":"Advancing food security through drone-based hyperspectral imaging: applications in precision agriculture and post-harvest management","authors":"Debashish Kar, Sambandh Bhusan Dhal","doi":"10.1007/s10661-025-13650-1","DOIUrl":null,"url":null,"abstract":"<div><p>Ensuring global food security in the face of growing population, climate change, and resource limitations is a critical challenge. Hyperspectral imaging (HSI), particularly when combined with drone technology, offers innovative solutions to enhance agricultural productivity and food quality by providing detailed, real-time data on crop health, disease detection, water and nutrient management, and post-harvest quality control. This review highlights the applications of drone-based HSI in precision agriculture, where it enables early detection of crop stress, accurate yield prediction, and soil health assessment. In post-harvest management, HSI is utilized to monitor food freshness and ripeness and detect potential contaminants, improving food safety and reducing waste. While the benefits of HSI are significant, challenges such as managing large volumes of data, translating spectral information into actionable insights, and ensuring cost-effective access for smallholder farmers remain barriers to its widespread adoption. Looking forward, future directions include advancements in miniaturized sensors, integration with Internet of Things (IoT) devices and satellite data for comprehensive agricultural monitoring, and expanding HSI applications to precision animal sciences. Collaboration among researchers, policymakers, and industry will be crucial to scaling the impact of HSI on global food systems, ensuring sustainable and equitable access to technology.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 3","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Monitoring and Assessment","FirstCategoryId":"93","ListUrlMain":"https://link.springer.com/article/10.1007/s10661-025-13650-1","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Ensuring global food security in the face of growing population, climate change, and resource limitations is a critical challenge. Hyperspectral imaging (HSI), particularly when combined with drone technology, offers innovative solutions to enhance agricultural productivity and food quality by providing detailed, real-time data on crop health, disease detection, water and nutrient management, and post-harvest quality control. This review highlights the applications of drone-based HSI in precision agriculture, where it enables early detection of crop stress, accurate yield prediction, and soil health assessment. In post-harvest management, HSI is utilized to monitor food freshness and ripeness and detect potential contaminants, improving food safety and reducing waste. While the benefits of HSI are significant, challenges such as managing large volumes of data, translating spectral information into actionable insights, and ensuring cost-effective access for smallholder farmers remain barriers to its widespread adoption. Looking forward, future directions include advancements in miniaturized sensors, integration with Internet of Things (IoT) devices and satellite data for comprehensive agricultural monitoring, and expanding HSI applications to precision animal sciences. Collaboration among researchers, policymakers, and industry will be crucial to scaling the impact of HSI on global food systems, ensuring sustainable and equitable access to technology.
期刊介绍:
Environmental Monitoring and Assessment emphasizes technical developments and data arising from environmental monitoring and assessment, the use of scientific principles in the design of monitoring systems at the local, regional and global scales, and the use of monitoring data in assessing the consequences of natural resource management actions and pollution risks to man and the environment.