The accurate evaluation of crop damage by wild animals is crucial for farmers when seeking compensation from insurance companies or other institutions. One of the game species that frequently cause crop damage in Europe is the wild boar, which often feeds on maize. Other game species, such as roe deer and red deer, can also cause significant crop damage. This study aimed to assess the accuracy of crop damage evaluation based on remote sensing data derived from unmanned aerial vehicles (UAVs), especially a digital surface model (DSM) based on RGB imagery and NDVI (normalized difference vegetation index) derived from multispectral imagery, at two growth stages of maize. During the first growth stage, when plants are in the intensive growth phase and green, crop damage evaluation was conducted using both DSM and NDVI. Each variable was separately utilized, and both variables were included in the classification and regression tree (CART) analysis, wherein crop damage was categorized as a binomial variable (with or without crop damage). In the second growth stage, which was before harvest when the plants had dried, only DSM was employed for crop damage evaluation. The results for both growth stages demonstrated high accuracy in detecting areas with crop damage, but this was primarily observed for areas larger than several square meters. The accuracy of crop damage evaluation was significantly lower for smaller or very narrow areas, such as the width of a single maize row. DSM proved to be more useful than NDVI in detecting crop damage as it can be applied at any stage of maize growth.

Crop damage caused by wild animals, particularly wild boars (Sus scrofa), significantly impacts agricultural yields, especially in maize fields. This study evaluates two methods for assessing maize crop damage using UAV-acquired data: (1) a deep learning-based approach employing the Deepness plugin in QGIS, utilizing high-resolution RGB imagery; and (2) a method based on digital surface models (DSMs) derived from LiDAR data. Manual visual assessment, supported by ground-truthing, served as the reference for validating these methods. This study was conducted in 2023 in a maize field in Central Poland, where UAV flights captured high-resolution RGB imagery and LiDAR data. Results indicated that the DSM-based method achieved higher accuracy (94.7%) and sensitivity (69.9%) compared to the deep learning method (accuracy: 92.9%, sensitivity: 35.3%), which exhibited higher precision (92.2%) and specificity (99.7%). The DSM-based method provided a closer estimation of the total damaged area (9.45% of the field) compared to the reference (10.50%), while the deep learning method underestimated damage (4.01%). Discrepancies arose from differences in how partially damaged areas were classified; the deep learning approach excluded these zones, focusing on fully damaged areas. The findings suggest that while DSM-based methods are well-suited for quantifying extensive damage, deep learning techniques detect only completely damaged crop areas. Combining these methods could enhance the accuracy and efficiency of crop damage assessments. Future studies should explore integrated approaches across diverse crop types and damage patterns to optimize wild animal damage evaluation.

The emissions of particulates from burning agricultural fields threaten the environment and human health, contributing to air pollution and increasing the risk of respiratory and cardiovascular diseases. An analysis of total suspended particulate (TSP), PM2.5, and PM10 emissions from crop residue burning is presented in this study. A primary goal is to improve emission estimation accuracy by integrating satellite imagery from modes of Moderate Resolution Imaging Spectroradiometers (MODIS) and Visible Infrared Imaging Radiometers (VIIRS) with traditional data. Particulate emissions were estimated using Tier 1 and Tier 2 methodologies outlined in the EEA/EMEP Emission Inventory Guidebook based on thermal anomaly data from satellite observations. According to the findings, burning wheat, maize, barley, and rice residue accounts for most emissions, with significant variations identified in India, China, and the United States. The variations highlight the need for a location-specific approach to emission management. Particulate emissions cause adverse environmental and health impacts, which can be minimized by targeting mitigation strategies at key emission hotspots. The research provides important insights to inform policymakers and support developing strategies to reduce fine particulate agricultural emissions.

The objective of this study was to evaluate the accuracy of detecting crop damage caused by wild boar in rapeseed fields using UAV (unmanned aerial vehicle)-derived RGB (red, green and blue) imagery and deep learning segmentation models. The experiments were conducted on rapeseed crops at full maturity shortly before harvest in central-western Poland in 2021. Four convolutional neural network architectures—U-Net (U-shaped network), U-Net++, DeepLabV3+ (deep learning + labelling), and PSPNet (Pyramid Scene Parsing Network)—were benchmarked using two input configurations: RGB imagery alone and RGB combined with the topographic position index (TPI) derived from a digital surface model (DSM). Model performance was assessed using overall accuracy, F1-score (harmonic mean of precision and recall), and Intersection over Union (IoU), with class-specific metrics reported to provide a realistic evaluation of damaged-area detection. For RGB-only data, overall accuracy ranged from 0.957 to 0.972, while damaged-class F1 and IoU reached 0.752 and 0.603, respectively, for the best-performing model (U-Net). When RGB data were supplemented with TPI, overall accuracy and damaged-class metrics changed only slightly, indicating limited benefit from the topographic feature under these field conditions. Non-damaged crop areas were consistently well-classified (F1 > 0.977, IoU > 0.955). These results confirm that UAV-based RGB imagery enables reliable late-season assessment of wildlife-induced crop damage, and that reporting class-specific metrics in spatially independent test sets is essential for realistic performance evaluation.