This study presents AgroInsect, a hybrid edge-cloud framework for agricultural pest monitoring that integrates on-device deep learning inference with server-side geostatistical mapping into a unified, field-deployable smartphone workflow. Three lightweight YOLO variants (YOLOv5n, YOLOv8n, and YOLOv11n) were trained and benchmarked under three TensorFlow Lite quantization schemes (Float32, Float16, and INT8), evaluating detection accuracy (mAP@0.5, mAP@0.5:0.95), inference latency, energy consumption, and thermal behavior on a mid-range Android device. YOLOv11n-INT8 was selected for deployment, achieving mAP@0.5 of 79.3% with a model size of only 2.98 MB and an estimated battery drain of 8.0% h-1. The system detects four pest species of agronomic relevance to soybean and maize crops in Brazil (Diabrotica speciosa, Dalbulus maidis, Diceraeus spp., and Spodoptera frugiperda) directly on the device, while geolocation is automatically extracted from image EXIF metadata and validated against farm boundaries using the Haversine formula. Detection records are synchronized to a cloud database, where Ordinary Kriging (PyKrige) generates continuous pest density surfaces that are returned to the mobile interface for visualization. Field evaluation achieved 95.1% overall accuracy and F1-scores of 0.94 or higher for all species. Kriging validation under dense synthetic sampling yielded R2 up to 0.9656 (species-dependent), with performance degrading under sparse conditions (R2 = 0.41-0.68 at 200 & times; 200 grids). Kriging was further validated against Inverse Distance Weighting (IDW) using leave-one-out cross-validation on real field detections (n = 79), which showed comparable point-prediction accuracy between the two methods, while Kriging preserved more spatial variance and captured a larger share of the observed infestation hotspot. Our approach integrates on-device pest detection, automated georeferenced validation, and server-side geostatistical mapping within a single operational pipeline, offering a practical tool for Integrated Pest Management in low-connectivity rural environments.
Microplastic (MP) pollution has emerged as a primary threat to aquatic and terrestrial ecosystems, including globally recognized protected areas. This review critically evaluates the presence, distribution, and impacts of MPs in regions classified as UNESCO World Natural Heritage Sites. The analysis encompasses 60 studies published up to December 2024, highlighting temporal trends and geographic gaps. Findings reveal that of the 312 UNESCO-recognized areas, only 28 have been the focus of MP-related studies, primarily in coastal and aquatic ecosystems. Notable sites include the Galápagos Islands, the Great Barrier Reef, and the Fernando de Noronha Archipelago. MPs, predominantly synthetic fibers composed of polyethylene (PE) and polypropylene (PP), were detected across various environmental matrices and organisms, including sediments, surface waters, and marine and freshwater species. Furthermore, human activities such as unregulated tourism and improper waste disposal exacerbate the dispersion of MPs, compromising the ecological integrity of these regions. This review identifies methodological gaps and proposes more integrated approaches to enhance monitoring and mitigation efforts, emphasizing the importance of global and regional public policies. By contributing to a broader understanding of MPs’ impacts in areas of high ecological and cultural value, this study highlights the need for coordinated actions that balance environmental conservation with economic sustainability.
Bioactive peptides (BP) have been investigated as potential functional feed additives due to their reported immunomodulatory and metabolic properties. This study evaluated the effects of dietary BP supplementation on growth performance, non-carcass components, serum biochemical profile and intestinal morphology in Texel lambs. Forty-five weaned lambs (31 castrated and 14 intact) were housed in pairs and fed for 58 days following a 20-day adaptation period. At the beginning of the experimental period, lambs averaged approximately 30 kg body weight, reaching about 45 kg at the end of the trial. Animals were assigned to three treatments: a control diet without BP and diets supplemented with 1–2 g BP per animal per day. Lambs were fed a total mixed ration based on corn silage and concentrate. Performance parameters, non-carcass traits, serum biochemical variables and distal jejunum morphometry were assessed. Data were analyzed using linear mixed models in software R. Dietary BP supplementation did not affect final body weight, average daily gain, feed efficiency, non-carcass components or intestinal morphometric parameters (P > 0.05). A significant increase in serum albumin concentration was observed in BP-supplemented groups; however, no consistent changes were detected in other biochemical indicators. Under the conditions of this study, BP supplementation at practical inclusion levels did not improve growth performance or intestinal morphology in Texel lambs. Future studies should explore different dosages, feeding durations and stress conditions to further evaluate the potential benefits of BP supplementation in ruminants.
Context Residual feed intake (RFI) and residual intake and gain (RIG) are widely used metrics to assess feed efficiency in sheep. However, their practical implementation in breeding programs remains limited owing to the high cost and complexity of individual feed intake measurements. Consequently, alternative indicators, such as physiological responses and infrared thermography (IRT), have been proposed as potential proxies for identifying animals with superior feed efficiency under different environmental conditions.Aims This study aimed to evaluate the relationships between RFI and RIG classifications and physiological parameters, as well as surface body temperatures obtained via IRT, in Texel ewes exposed to natural heat stress.Methods Thirty-nine young Texel ewes were monitored for 57 days in a covered facility equipped with an automated feeding and watering system with individual intake recording. Animals were classified into low, medium, and high-efficiency groups according to their RFI and RIG values. Physiological responses, including respiratory rate (RR), heart rate (HR), rectal temperature (RT), and the heat tolerance coefficient (HTC), were recorded. Additionally, surface temperatures of the eye, muzzle, hooves, and vulva were measured using infrared thermography (IRT). Statistical analyses included ANOVA and principal component analysis (PCA) to explore associations among traits.Key results No significant differences were detected among RFI or RIG classes for RR, HR, RT, or HTC. Similarly, IRT-derived surface temperatures did not differ across efficiency classifications. PCA showed that RR and HTC explained the greatest proportion of total variance, whereas RFI and RIG contributed to other independent components.Conclusions Neither physiological parameters nor IRT-based surface temperatures were effective indicators of feed efficiency in Texel ewes under natural heat stress.Implications The results indicated that RFI and RIG cannot be accurately inferred from physiological or IRT variables under field conditions. Future research should integrate additional phenotypic and behavioral indicators to identify reliable, low-cost biomarkers for metabolic efficiency in sheep.
The increasing demand for sustainable livestock production systems has intensified scientific interest in feed efficiency traits, particularly residual feed intake (RFI), as a strategy to improve productivity while reducing environmental impacts. This study provides a comprehensive bibliometric assessment of global scientific production on RFI and feed efficiency in ruminants. A total of 2632 documents indexed in Scopus and Web of Science databases between 2015 and 2024 were analyzed, covering 374 scientific sources and involving 8,406 authors. Scientific output showed a strong and consistent upward trend throughout the study period, with an annual growth rate of 48.9
