Search

Going beyond previous work, this paper presents a systematic literature review that explores the deployment of satellites, drones, and ground-based sensors for yield prediction in agriculture. It covers multiple aspects of the topic, including crop types, key sensor platforms, data analysis techniques, and performance in estimating yield. To this end, datasets from Scopus and Web of Science were analyzed, resulting in the full review of 269 out of 1429 retrieved publications. Our study revealed that China (93 articles, >1800 citations) and the USA (58 articles, >1600 citations) are prominent contributors in this field; while satellites were the primary remote sensing platform (62%), followed by airborne (30%) and proximal sensors (27%). Additionally, statistical methods were used in 157 articles, and model-based approaches were utilized in 60 articles, while machine learning and deep learning were employed in 142 articles and 62 articles, respectively. When comparing methods, machine learning and deep learning methods exhibited high accuracy in crop yield prediction, while other techniques also demonstrated success, contingent on the specific crop platform and method employed. The findings of this study serve as a comprehensive roadmap for researchers and farmers, enabling them to make data-driven decisions and optimize agricultural practices, paving the way towards a fully digitized yield prediction. [ABSTRACT FROM AUTHOR]

In this study, the development of a suitable methodology for establishing and monitoring indicators of Ecosystem Health (EH) and its responses to wildfire using Earth Observation (EO) data synergistically with Geographical Information Systems (GIS) is investigated. The proposed methodology combined GIS and Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data to assess ecosystem characteristics, including its vigor, organization and resilience, for a case study in Central Greece. These parameters were quantified primarily by utilizing EO-based techniques focusing on the analysis of the Normalized Difference Vegetation Index (NDVI). Topographic features, including slope, aspect and a Compound Topographic Index (CTI) were also derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM), and integrated in a modeling scheme to assess EH. The developed modeling scheme illustrates the effect of wildfires on EH accurately, demonstrating correlations between areas of past wildfires and their associated recovery. Our findings thus provide useful information to land managers and policy makers of fire affected regions alike, and could provide important contributions to the potential development of an operational estimation of EH recovery after wildfire. [ABSTRACT FROM AUTHOR]

Oil palm (Elaeis guineensis) trees are an important contributor of recent economic development in Southeast Asia. The high product yield, and the consequent high profitability, has led to a widespread expansion of plantations in the greater region. However, oil palms are susceptible to diseases that can have a detrimental effect. In this study we use hyper- and multi-spectral remote sensing to detect diseased oil palm trees in Krabi province, Thailand. Proximate spectroscopic measurements were used to identify and discern differences in leaf spectral radiance; the results indicate a relatively higher radiance in visible and near-infrared for the healthy leaves in comparison to the diseased. From a total of 113 samples for which the geolocation and the hyperspectral radiance were recorded, 59 and 54 samples were healthy and diseased oil palm trees, respectively. Moreover, a WorldView-2 satellite image was used to investigate the usability of traditional vegetation indices and subsequently detecting diseased oil palm trees. The results show that the overall maximum likelihood classification accuracy is 85.98%, the Kappa coefficient 0.71 and the producer's accuracy for healthy and diseased oil palm trees 83.33 and 78.95, respectively. We conclude that high spatial and spectral resolutions can play a vital role in monitoring diseases in oil palm plantations. [ABSTRACT FROM AUTHOR]