Advances in Precision Farming: a contribute for estimating crop health and water stress by comparing UAV Multispectral and Thermal Imagery

Abstract

Abstract. Current climate change is largely due to the continuing increase in the anthropogenic greenhouse effect, with major environmental repercussions, especially in agriculture. The increase of global warming, salinity of water resources and frequency of extreme weather events has devastating consequences on the primary sector, in particular on the photosynthetic activity of crops and, therefore, their agricultural yield. The current climate crisis, in fact, leads to an increase in water requirements, the proliferation of weeds, and the depletion of nutrients in the soil, necessitating the massive use of fertilisers, herbicides and pesticides, which, in turn, trigger substantial alterations in ecosystem balances. In response to these critical issues, precision agriculture (PA) constitutes a data-driven approach based on the interpretation of multispectral and thermal datasets obtained by different remote sensing techniques and the use of latest-generation sensors to recognise the state of health of crops and, therefore, optimise agricultural production with a more rational and sustainable management of resources.This paper presents the results of a survey campaign carried out in October 2023 on two citrus fields located in south-eastern Sicily (Italy) to highlight the health status of crops just before the harvesting period. By using multispectral and thermal sensors installed on a drone, different vegetation indices have been calculated to identify, in each field, the areas with the highest photosynthetic activity and the zones characterised by a lack of water or other nutrients, on which targeted agronomic interventions should be planned as a priority.

Plant phenotyping relevance

UAVマルチスペクトル・熱画像を用いて、柑橘作物の健康状態、光合成活性、水ストレスを推定するセンシング手法の適用が中心であり、単なるルーチン測定ではない。

Code and data availability

The supplied article blocks describe UAV multispectral/thermal surveys of two Sicilian citrus fields and vegetation index calculations in Agisoft Metashape and QGIS, but contain no data availability statement, no public dataset deposit, no author code/scripts or workflows, and no trained models or checkpoints. No paper

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