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Rapid and quantitative assessment of crop lodging is important for understanding the causes of the phenomena, improving crop management, making better production and supporting loss estimates in general. Accurate information on the location and timing of crop lodging is valuable for farmers, agronomists, insurance loss adjusters, and policymakers. Lodging studies can be performed to assess the impact of lodging events or to model the risk of occurrence, both of which rely on information that can be acquired by field observations, from meteorological data and from remote sensing (RS). While studies applying RS data to assess crop lodging dates back three decades, there has been no comprehensive review of the status, potential, current approaches, and challenges in this domain. In this position paper, we review the trends in field/lab-based and RS-based studies for crop lodging assessment and discuss the strengths and weaknesses of current approaches. Theoretical background on crop lodging is presented, and the scope of RS in assessing plant characteristics associated with lodging is reviewed and discussed. The review focuses on RS-based studies, grouping them according to the platform deployed (i.e., ground-based, airborne and spaceborne), with an emphasis on analyzing the pros and cons of the technology. Finally, the challenges, research gaps, perspectives for future research, and an outlook on new sensors and platforms are presented to provide state-of-the-art and future scenarios of RS in lodging assessment. Our review reveals that the use of RS techniques in crop lodging assessment is still in an experimental stage. However, there is increasing interest within the RS scientific community (based on the increased rate of publications over time) to investigate its use for crop lodging detection and risk mapping. The existing satellitebased lodging assessment studies are very few, and the operational application of the current approaches over large spatial extents seems to be the biggest challenge. We identify opportunities for future studies that can develop quantitative models for estimating lodging severity and mapping lodging risk using RS data. 1.2. The role of remote sensing The past few decades have witnessed considerable growth in the use of sensors on-board Earth-Observation (EO) systems for agricultural monitoring applications. Today, crop biophysical properties such as leaf

In this article, we propose an automatic procedure for classification of UAV imagery to map weed presence in rice paddies at early stages of the growing cycle. The objective was to produce a weed map (common weeds and cover crop remnants) to support variable rate technologies for site-specific weed management. A multi-spectral orthomosaic, derived from images acquired by a Parrot Sequoia sensor mounted on a quadcopter, was classified through an unsupervised clustering algorithm; cluster labelling into 'weed'/'no weed' classes was achieved using geo-referenced observations. We tested the best set of input features among spectral bands, spectral indices and textural metrics. Weed mapping performance was assessed by calculating overall accuracy (OA) and, for the weed class, omission (OE) and commission errors (CE). Classification results were assessed under an 'alarmist' approach in order to minimise the chance of overestimating weed coverage. Under this condition, we found that best results are provided by a set of spectral indices (OA = 96.5%, weed CE = 2.0%). The output weed map was aggregated to a grid layer of 5 × 5 m to simulate variable rate management units; a weed threshold was applied to identify the portion of the field to be subject to treatment with herbicides. Ancillary information on weed and crop conditions were derived over the grid cells to support precision agronomic management of rice crops at the early stage of growth.

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