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Floods are among the most serious natural disasters worldwide; they cause enormous crop losses every year and threaten world food security. Many studies have focused on flood impact assessments for administrative districts, but fewer have focused on postdisaster impact assessments for specific crops. Therefore, this study used remote sensing data, including the normalized difference vegetation index (NDVI), elevation data, slope data, and precipitation data, combined with crop growth period data to construct a crop flood damage assessment index (CFAI). First, the analytic hierarchy process (AHP) was used to assign weights to the impact parameters; then, the Weighted Composite Score Method was used to calculate the CFAI; and finally, the impact was classified as sub-slight, slight, moderate, sub-severe, or severe based on the magnitude of the CFAI. This method was used for the Missouri River floods of 2019 in the United States and the Henan flood of 2021 in China. Due to the lack of measured data, the disaster vegetation damage index (DVDI) was used to compare the results. Compared with the DVDI, the CFAI underestimated the evaluation results. The CFAI can respond well to the degree of crop impact after flooding, providing new ideas and reference standards for agriculture-related departments.

Hailstorms are a frequent natural weather disaster in the Canadian Prairies that can cause catastrophic damage to field crops. Assessment of damage for insurance claims requires insurance inspectors to visit individual fields and estimate damage on individual plants. This study computes temporal profiles and estimates the severity of hail damage to crops in 54 fields through the temporal analysis of vegetation indices calculated from Sentinel-2 images. The damage estimation accuracy of eight vegetative indices in different temporal analyses of delta index (pre-and post-hail differences) or area under curve (AUC) index (time profiles of index affected by hail) was compared. Hail damage was accurately quantified by using the AUC of 32 days of Normalized Difference Vegetation Indices (NDVI), Normalized Difference Water Index (NDWI), and Plant Senescence Radiation Index (PSRI). These metrics were well correlated with ground estimates of hail damage in canola (r = −0.90, RMSE = 8.24), wheat (r = −0.86, RMSE = 12.27), and lentil (r = 0.80, RMSE = 17.41). Thus, the time-series changes in vegetation indices had a good correlation with ground estimates of hail damage which may allow for more accurate assessment of the extent and severity of hail damage to crop land.

The conflict between humans and wildlife often arises from crop raiding and has a significant impact on both subsistence humans’ livelihoods and long-term wildlife survival in developing countries. The study aimed to identify crop-raiding wild animals, the prevalence of crop damage, and the conflict resolution mechanism. Data were collected by questionnaire, interview, and direct field observation to estimate the extent of the crop loss and species of an animal involved in crop-raiding. The findings identified Anubis Baboon (Papio anubis), Vervet Monkey (Chlorocebus pygerythrus), and Grivet Monkey (Chlorocebus aethiops) as the major crop pests, followed by Porcupines (Hystrix cristata), Birds and Mongoose (Helogale hirtula). Foraging typically Maize (Zea may) followed by Teff (Eragrostis tef), Enset (Ensete ventricosum), and Barley (Hordeum vulgare). Deforestation, illegal agricultural activities, and farmland distance to the forest were identified as causes of the conflict. In addition, scarecrow, chasing, and permanent guardians have been identified as traditional crop damage prevention techniques of the local people of the area. Therefore, to alleviate the existing impact of crop damage or loss by crop-pest or crop-raiding animals in the area adopting various most suitable approaches along with the awareness and involvement of local farmers would be a critical step.

PurposeThis paper proposes a low-cost and low-effort solution for determining the area of corn crops damaged by the wildlife facility utilising field images collected by an unmanned aerial vehicle (UAV). The proposed solution allows for the determination of the percentage of the damaged crops and their location.MethodsThe method utilises image segmentation models based on deep convolutional neural networks (e.g., UNet family) and transformers (SegFormer) trained on over 300 hectares of diverse corn fields in western Poland. A range of neural network architectures was tested to select the most accurate final solution.ResultsThe tests show that despite using only easily accessible RGB data available from inexpensive, consumer-grade UAVs, the method achieves sufficient accuracy to be applied in practical solutions for agriculture-related tasks, as the IoU (Intersection over Union) metric for segmentation of healthy and damaged crop reaches 0.88.ConclusionThe proposed method allows for easy calculation of the total percentage and visualisation of the corn crop damages. The processing code and trained model are shared publicly.

While agricultural intensification and expansion are major factors driving loss and degradation of natural habitat and species decline, some wildlife species also benefit from agriculturally managed habitats. This may lead to high population densities with impacts on both human livelihoods and wildlife conservation. Cranes are a group of 15 species worldwide, affected both negatively and positively by agricultural practices. While eleven species face critical population declines, numbers of common cranes (Grus grus) and sandhill cranes (Grus canadensis) have increased drastically in the last 40 years. Their increase is associated with higher incidences of crane foraging on agricultural crops, causing financial losses to farmers. Our aim was to synthesize scientific knowledge on the bilateral effects of land use change and crane populations. We conducted a systematic literature review of peer‐reviewed publications on agriculture‐crane interactions (n = 135) and on the importance of agricultural crops in the diet of cranes (n = 81). Agricultural crops constitute a considerable part of the diet of all crane species (average of 37%, most frequently maize (Zea mays L.) and wheat (Triticum aestivum L.)). Crop damage was identified in only 10% of all agriculture‐crane interactions, although one‐third of interactions included cranes foraging on cropland. Using a conceptual framework analysis, we identified two major pathways in agriculture‐crane interactions: (1) habitat loss with negative effects on crane species dependent on specific habitats, and (2) expanding agricultural habitats with superabundant food availability beneficial for opportunistic crane species. The degree to which crane species can adapt to agricultural land use changes may be an important factor explaining their population response. We conclude that multi‐objective management needs to combine land sparing and land sharing strategies at landscape scale. To support viable crane populations while guaranteeing sustainable agricultural production, it is necessary to include the perspectives of diverse stakeholders and streamline conservation initiatives and agricultural policy accordingly. While eleven crane species face critical population declines, the drastic increase in common cranes (Grus grus) and sandhill cranes (Grus canadensis) is associated with higher incidences of cranes damaging agricultural crops. We conducted a systematic literature review of peer‐reviewed publications on agriculture‐crane interactions (n = 135) and on the importance of agricultural crops in the diet of cranes (n = 81). A conceptual framework identified two major pathways: (1) habitat loss with negative effects on crane species dependent on specific habitats, and (2) expanding agricultural habitats with superabundant food availability beneficial for opportunistic crane species.

The increasing frequency of hydrometeorological extremes, such as torrential rainfall, strong winds, and hailstorms, often causes widespread mechanical damage to crops. This study evaluates the potential of cost-effective unmanned aerial vehicle (UAV) photogrammetry with a standard RGB camera for quantifying crop damage. A maize field with mechanical damage caused by wild boar activity was used as an analogue for storm-induced damage. Two approaches were applied: (i) a 3D structural method based on Canopy Surface Models (CSMs) derived from Structure-from-Motion (SfM) photogrammetry, and (ii) automated image classification using a Support Vector Machine (SVM) combined with Object-Based Image Analysis (OBIA). The accuracy of the damage assessment was compared using two terrain inputs: a UAV-derived DEM (UAV DEM) and the official Czech national LiDAR-based DEM (DEM 5G). The results showed high consistency between both methods and datasets. The relative crop damage rate was 29.25% with the UAV DEM and 26.76% with the DEM 5G, with a spatial agreement exceeding 95%. Jaccard similarity coefficients confirmed strong concordance (0.8953 and 0.9207). The findings highlight the applicability of UAV-based 3D structural analysis for late-stage crop monitoring, when spectral indices lose reliability. They also emphasise that the official DEM 5G can serve as a suitable substitute for a UAV-derived DEM in damage assessment. The methodology thus represents a rapid, cost-effective, and operationally feasible solution for agricultural monitoring, insurance claims, and environmental management.

Climate change poses significant challenges to agriculture, leading to increased crop damage owing to extreme weather conditions. Detecting and analyzing such damage is crucial for mitigating its effects on crop yield. This study proposes a novel autoencoder (AE)-based model, termed “Memory Ganomaly,” designed to detect and analyze weather-induced crop damage under conditions of significant class imbalance. The model integrates memory modules into the Ganomaly architecture, thereby enhancing its ability to identify anomalies by focusing on normal (undamaged) states. The proposed model was evaluated using apple and peach datasets, which included both damaged and undamaged images, and was compared with existing robust Convolutional neural network (CNN) models (ResNet-50, EfficientNet-B3, and ResNeXt-50) and AE models (Ganomaly and MemAE). Although these CNN models are not the latest technologies, they are still highly effective for image classification tasks and are deemed suitable for comparative analyses. The results showed that CNN and Transformer baselines achieved very high overall accuracy (94–98%) but completely failed to identify damaged samples, with precision and recall equal to zero under severe class imbalance. Few-shot learning partially alleviated this issue (up to 75.1% recall in the 20-shot setting for the apple dataset) but still lagged behind AE-based approaches in terms of accuracy and precision. In contrast, the proposed Memory Ganomaly delivered a more balanced performance across accuracy, precision, and recall (Apple: 80.32% accuracy, 79.4% precision, 79.1% recall; Peach: 81.06% accuracy, 83.23% precision, 80.3% recall), outperforming AE baselines in precision and recall while maintaining comparable accuracy. This study concludes that the Memory Ganomaly model offers a robust solution for detecting anomalies in agricultural datasets, where data imbalance is prevalent, and suggests its potential for broader applications in agricultural monitoring and beyond. While both Ganomaly and MemAE have shown promise in anomaly detection, they suffer from limitations—Ganomaly often lacks long-term pattern recall, and MemAE may miss contextual cues. Our proposed Memory Ganomaly integrates the strengths of both, leveraging contextual reconstruction with pattern recall to enhance detection of subtle weather-related anomalies under class imbalance.

Wide span tractors have a wide transversal bar, on which different implements can be mounted, while the supporting wheels follow the set traffic-lanes. The stability of wide span tractor movement is influenced by unbroken small angular deviations and transversal displacements of the machine due to several factors. These deflections from the set trajectories affect the working implements, especially the peripheral ones, which can cut the plants if wide span tractors are used to manage row crops. In this context, it needs to consider a safeguard zone that allows to reduce the probability of contact between working implements and plants. The aim of this paper was to determine the quantitative effect of transverse displacements of the working implements and the suitable size of the aforesaid safeguard zone. The magnitude of the inner and outer displacements of the working implements depends significantly on their location in relation to the center of the wide span tractor. For working implements located outside the center of the tractor, the outer safeguard zone should be larger than the inner zone. The probability of crop damage by working implements can be reduced by automated control of wide span tractor movement.

Chemical spill accidents lead to environmental problems, especially for plants. Plant vegetation assessment is necessary after a chemical accident; however, conventional methods can be inaccurate and time-consuming. This study used the vegetation index (VI) extracted from unmanned aerial vehicle (UAV) multispectral imagery for crop damage assessment after chemical exposure. The chemical accident simulations were conducted by exposure of rice at five growth stages to four levels of toluene. The VI was measured at five days after damage and 67 days after planting. Physiological characteristics (chlorophyll content and grain yield) were also measured. As a result, the mean normalized difference VI (NDVI) of toluene-exposed rice was significantly decreased with respect to toluene exposure concentration increases at most growth stages. Recovery after toluene exposure was lower in rice exposed to higher concentrations at the earlier growth stages. The chlorophyll content and grain yield were also decreased after toluene exposure with respect to increasing toluene concentrations and showed positive correlations with the NDVI. It indicates that the NDVI is capable of reflecting the plant response to chemical exposure. Thus, the results demonstrated that the VI based on UAV multispectral imagery is feasible as an alternative for crop monitoring, damage assessment after chemical exposure, and yield prediction.

Having an additional tool for swiftly determining the extent of flood damage to crops with confidence is beneficial. This study focuses on estimating rice crop damage caused by flooding in Candaba, Pampanga, using open-source satellite data. By analyzing the correlation between Normalized Difference Vegetation Index (NDVI) measurements from unmanned aerial vehicles (UAVs) and Sentinel-2 (S2) satellite data, a cost-effective and time-efficient alternative for agricultural monitoring is explored. This study comprises two stages: establishing a correlation between clear sky observations and NDVI measurements, and employing a combination of S2 NDVI and Synthetic Aperture Radar (SAR) NDVI to estimate crop damage. The integration of SAR and optical satellite data overcomes cloud cover challenges during typhoon events. The accuracy of standing crop estimation reached up to 99.2%, while crop damage estimation reached up to 99.7%. UAVs equipped with multispectral cameras prove effective for small-scale monitoring, while satellite imagery offers a valuable alternative for larger areas. The strong correlation between UAV and satellite-derived NDVI measurements highlights the significance of open-source satellite data in accurately estimating rice crop damage, providing a swift and reliable tool for assessing flood damage in agricultural monitoring.