Advancements in UAV remote sensing for agricultural yield estimation: A systematic comprehensive review of platforms, sensors, and data analytics

IF 3.8 Q2 ENVIRONMENTAL SCIENCES
Shubham Anil Gade , Mallappa Jadiyappa Madolli , Pedro García‐Caparrós , Hayat Ullah , Suriyan Cha-um , Avishek Datta , Sushil Kumar Himanshu
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Abstract

Traditional yield estimation approaches are quite tedious, time-consuming, and labor-intensive. Unmanned aerial vehicles (UAVs) present an exciting opportunity to estimate crop yield with high spatial and temporal resolution in agriculture. The objective of this article is to review current studies and research works in agriculture that employ the use of different UAV platforms, sensors, data acquisition, machine learning and photogrammetry techniques, and vegetation indices in UAV-based crop yield prediction. Furthermore, the article also explores the challenges and limitations in yield estimation. Hundred different studies from Google Scholar, Scopus, and Web of Science are presented and reviewed. The result demonstrated that most of the studies are centered on China and USA. Supervised learning models are widely used and exhibit better accuracy in yield estimation. The normalized difference vegetation index (NDVI) is preferred by researchers and emerges as a widely used vegetation index (60 studies). The study concluded that UAV-based crop remote sensing can be an effective method for improving yield estimation. The integration of multimodal data, including textural, structural, thermal, and meteorological features, along with key spectral bands such as near-infrared (NIR) and red-edge (RE), has demonstrated potential for improving the accuracy of yield estimation models. Moreover, supervised models have shown great suitability for cereal crops. Random Forest and linear regression emerge as reliable options for estimating yields of major crops, such as wheat, rice, and maize. However, challenges in yield estimation with UAV-based remote sensing include regulatory constraints, weather conditions, data storage and management, high initial costs, and technical limitations.

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用于农业产量估算的无人机遥感进展:平台、传感器和数据分析的系统综合综述
传统的产量估算方法非常繁琐、耗时和费力。无人驾驶飞行器(uav)在农业中提供了一个令人兴奋的机会,以高空间和时间分辨率估计作物产量。本文的目的是回顾目前在农业中使用不同无人机平台、传感器、数据采集、机器学习和摄影测量技术以及基于无人机的作物产量预测中的植被指数的研究和研究工作。此外,本文还探讨了产量估算的挑战和局限性。来自谷歌Scholar, Scopus和Web of Science的数百项不同的研究进行了介绍和审查。结果表明,大多数研究集中在中国和美国。监督学习模型在产量估计中应用广泛,具有较好的准确性。归一化植被指数(normalized difference vegetation index, NDVI)作为一种被广泛应用的植被指数(60项研究)受到了研究者的青睐。研究表明,基于无人机的作物遥感是提高作物产量估算的有效方法。多模态数据的整合,包括纹理、结构、热、气象特征,以及关键的光谱波段,如近红外(NIR)和红边(RE),已经证明了提高产量估计模型准确性的潜力。此外,监督模型对谷类作物的适用性也很好。随机森林和线性回归成为估计小麦、水稻和玉米等主要作物产量的可靠选择。然而,基于无人机的遥感产量估算面临的挑战包括监管限制、天气条件、数据存储和管理、高昂的初始成本和技术限制。
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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