Search Results (253)

We present a model that integrates the mapping of the phenology and climatic suitability for the spotted lanternfly (SLF), Lycorma delicatula (White, 1845) (Hemiptera: Fulgoridae), to provide guidance on when and where to conduct surveillance and management of this highly invasive pest. The model was designed for use in the Degree-Day, Establishment Risk, and Phenological Event Maps (DDRP) platform, which is an open-source decision support tool to help to detect, monitor, and manage invasive threats. We validated the model using presence records and phenological observations derived from monitoring studies and the iNaturalist database. The model performed well, with more than >99.9% of the presence records included in the potential distribution for North America, a large proportion of the iNaturalist observations correctly predicted, and a low error rate for dates of the first appearance of adults. Cold and heat stresses were insufficient to exclude the SLF from most areas of the conterminous United States (CONUS), but an inability for the pest to complete its life cycle in cold areas may hinder establishment. The appearance of adults occurred several months earlier in warmer regions of North America and Europe, which suggests that host plants in these areas may experience stronger feeding pressure. The near-real-time forecasts produced by the model are available at USPest.org and the USA National Phenology Network to support decision making for the CONUS. Forecasts of egg hatch and the appearance of adults are particularly relevant for surveillance to prevent new establishments and for managing existing populations. Full article

This review comprehensively examines the application of deep learning technologies across the entire soybean production chain, encompassing areas such as disease and pest identification, weed detection, crop phenotype recognition, yield prediction, and intelligent operations. By systematically analyzing mainstream deep learning models, optimization strategies (e.g., model lightweighting, transfer learning), and sensor data fusion techniques, the review identifies their roles and performances in complex agricultural environments. It also highlights key challenges including data quality limitations, difficulties in real-world deployment, and the lack of standardized evaluation benchmarks. In response, promising directions such as reinforcement learning, self-supervised learning, interpretable AI, and multi-source data fusion are proposed. Specifically for soybean automation, future advancements are expected in areas such as high-precision disease and weed localization, real-time decision-making for variable-rate spraying and harvesting, and the integration of deep learning with robotics and edge computing to enable autonomous field operations. This review provides valuable insights and future prospects for promoting intelligent, efficient, and sustainable development in soybean production through deep learning. Full article

Frequent corn leaf diseases and pests pose serious threats to agricultural production. Traditional manual detection methods suffer from significant limitations in both performance and efficiency. To address this, the present paper proposes a novel biotic condition screening (BCS) model for the detection of corn leaf diseases and pests, called BCS_YOLO, based on the You Only Look Once version 11n (YOLOv11n). The proposed model enables accurate detection and classification of various corn leaf pathologies and pest infestations under challenging agricultural field conditions. It achieves this thanks to three key newly designed modules—a Self-Perception Coordinated Global Attention (SPCGA) module, a High/Low-Frequency Feature Enhancement (HLFFE) module, and a Local Attention Enhancement (LAE) module. The SPCGA module improves the model’s ability to perceive fine-grained targets by fusing multiple attention mechanisms. The HLFFE module adopts a frequency domain separation strategy to strengthen edge delineation and structural detail representation in affected areas. The LAE module effectively improves the model’s discrimination ability between targets and backgrounds through local importance calculation and intensity adjustment mechanisms. Conducted experiments show that BCS_YOLO achieves 78.4%, 73.7%, 76.0%, and 82.0% in $precision$, recall, $F1score$, and $mAP@50$, respectively, representing corresponding improvements of 3.0%, 3.3%, 3.2%, and 4.6% compared to the baseline model (YOLOv11n), while also outperforming the mainstream object detection models. In summary, the proposed BCS_YOLO model provides a practical and scalable solution for efficient detection of corn leaf diseases and pests in complex smart-agriculture scenarios, demonstrating significant theoretical and application value. Full article

Cruciferous vegetables are popular in Asian dishes. However, striped flea beetles prefer to feed on leaves, which can damage the appearance of crops and reduce their economic value. Due to the lack of pest monitoring, the occurrence of pests is often irregular and unpredictable. Regular and quantitative spraying of pesticides for pest control is an alternative method. Nevertheless, this requires manual execution and is inefficient. This paper presents a system powered by solar energy, utilizing batteries and supercapacitors for energy storage to support the implementation of edge AI devices in outdoor environments. Raspberry Pi is utilized for artificial intelligence image recognition and the Internet of Things (IoT). YOLOv5 is implemented on the edge device, Raspberry Pi, for detecting striped flea beetles, and StyleGAN3 is also utilized for data augmentation in the proposed system. The recognition accuracy reaches 85.4%, and the results are transmitted to the server through a 4G network. The experimental results indicate that the system can operate effectively for an extended period. This system enhances sustainability and reliability and greatly improves the practicality of deploying smart pest detection technology in remote or resource-limited agricultural areas. In subsequent applications, drones can plan routes for pesticide spraying based on the distribution of pests. Full article

Peste des petits ruminants is a highly contagious transboundary viral disease that poses a serious threat to small ruminant populations worldwide. In 2024, seven outbreaks of PPR were recorded in sheep flocks from Timiș County, marking the second confirmed incursions of peste des petits ruminants virus (PPRV) in Romania. This study aimed to document the clinical presentation, pathological findings, and diagnostic confirmation with these field outbreaks. Comprehensive field investigations were carried out between July and September 2024, including clinical examinations, post mortem analysis, serological screening, and molecular detection using reverse transcription polymerase chain reaction (RT-PCR). A total of 13,203 sheep were evaluated, with an overall mortality rate of 12.77%. Characteristic clinical signs included mucopurulent nasal discharge, oral erosions, respiratory distress, and diarrhea. Gross lesions observed during necropsy included hemorrhagic bronchopneumonia, bile-stained liver, catarrhal enteritis, and mucosal hemorrhages. Serological testing revealed flock-level seroprevalence rates ranging from 46.7% to 80.0%, with higher rates observed in older animals. RT-PCR confirmed PPRV infection in all affected flocks. Our findings provide strong evidence of virulent PPRV circulation in an area where the virus had not been reported before. The results highlight an urgent need to strengthen surveillance systems, enhance diagnostic capacity, and foster cross-border collaboration. These field-based insights can contribute to both national and international efforts aimed at controlling and ultimately eradicating the disease. Full article

Precise detection of canopy parameters is vital as it offers essential information for pest management in orchards. Among these parameters, leaf area density stands out as a key indicator of orchard canopies. A detection algorithm for leaf area density was proposed, and a leaf area density detection system for orchard canopies was designed based on the algorithm. By processing the point cloud data acquired by using LiDAR together with the algorithm, the total leaf area of the fitted leaves was calculated. Through an orthogonal regression experiment conducted on a laboratory-simulated canopy, this research established a mathematical calculation model ($R^2$ = 0.96) for determining the leaf area density of an orchard canopy. The leaf area density of an orchard canopy can be calculated using the total leaf area of the fitted leaves and an established mathematical model. To assess the accuracy of the detection system, both laboratory-simulated canopy experiments and real orchard canopy experiments were conducted. The results revealed that the absolute value of the mean relative error in the laboratory-simulated canopy experiments was 11.58%, and the absolute value of the mean relative error in the orchard canopy experiments was 16.75%. The research results have confirmed the feasibility of the LiDAR point cloud data processing algorithm. Furthermore, this algorithm can provide theoretical support for the subsequent development of intelligent plant protection equipment in orchards. Full article

Precision pesticide application is a key focus in orchard management, with targeted spraying serving as a core technology to optimize pesticide delivery and reduce environmental pollution. However, its accurate implementation relies on high-precision environmental sensing technologies to enable the precise identification of target objects and dynamic regulation of spraying strategies. This paper systematically reviews the application of orchard environmental sensing technologies in targeted spraying. It first focuses on key sensors used in environmental sensing, providing an in-depth analysis of their operational mechanisms and advantages in orchard environmental perception. Subsequently, this paper discusses the role of multi-source data fusion and artificial intelligence analysis techniques in improving the accuracy and stability of orchard environmental sensing, supporting crown structure modeling, pest and disease monitoring, and weed recognition. Additionally, this paper reviews the practical paths of environmental sensing-driven targeted spraying technologies, including variable spraying strategies based on canopy structure perception, precise pesticide application methods combined with intelligent pest and disease recognition, and targeted weed control technologies relying on weed and non-target area detection. Finally, this paper summarizes the challenges faced by multi-source sensing and targeted spraying technologies in light of current research progress and industry needs, and explores potential future developments in low-cost sensors, real-time data processing, intelligent decision making, and unmanned agricultural machinery. Full article

Pantoea stewartii subsp. stewartii (Pss) is a Gram-negative bacterium causing Stewart wilt, a severe disease in maize. Native to North America, it has spread globally through the maize seed trade. Resistant maize varieties and insecticides are crucial to mitigate the disease’s economic impact. Pss is a quarantine pest, requiring phytosanitary certification for the seed trade in European countries. Accurate diagnostic tests, including real-time PCR, are fundamental to detect Pss and distinguish it from other bacteria, like Pantoea stewartii subsp. indologenes (Psi), a non-quarantine bacteria associated with maize seeds. Population genetics is a valuable tool for studying adaptation, speciation, population structure, diversity, and evolution in plant bacterial pathogens. In this study, the key activities of interlaboratory comparisons are reported to assess diagnostic sensitivity (DSE), diagnostic specificity (DSP) and accuracy (ACC) for different real-time PCR able to detect Pss in seeds. The results of complete sequencing of Italian bacterial isolates are presented. This study enhances our understanding of molecular methods for diagnosing and identifying pathogens in maize seeds, improving knowledge of Pss genomes to prevent their spread and trace possible entry routes from endemic to non-endemic areas. Full article

Due to the ongoing global spread of the small hive beetle (SHB), Aethina tumida, there is a significant need for detection and practical management strategies against this pest. The standard inspection strategies for SHBs involve (1) detailed visual examination of the colony, which is challenging in areas with defensive bees, or (2) sampling beetles via traps, which requires repeated visits to the apiary and can be difficult for beekeepers with apiaries in rural areas. In this study, we modified the inspection sequence to examine the in-hive distribution of the beetle and assess whether a limited, yet targeted, inspection could provide valuable information on beetle infestation. We conducted our modified sampling in three different countries: Hawai’i (USA), Mexico, and Costa Rica. We found that targeted screening of the top areas of the hive (cover and top-side frames) provided reliable information about the relative prevalence of SHBs in a colony. The results also suggested that SHBs do not naturally congregate on a bare bottom board but migrate downward during inspection. Trap placement on the bottom floor of the hive may underestimate beetle presence in low to medium pest levels. The proposed inspection protocol is not influenced by the genetic origin of the bees (Africanized or European) and could be a practical alternative for assessing SHB infestation levels in honeybee colonies. Full article

The integration of machine learning (ML) into self-governing spraying systems is one of the major developments in digital precision agriculture that is significantly improving resource efficiency, sustainability, and production. This study looks at current advances in machine learning applications for automated spraying in agricultural mechanisation, emphasising the new innovations, difficulties, and prospects. This study provides an in-depth analysis of the three main categories of autonomous sprayers—drones, ground-based robots, and tractor-mounted systems—that incorporate machine learning techniques. A comprehensive review of research published between 2014 and 2024 was conducted using Web of Science and Scopus, selecting relevant studies on agricultural robotics, sensor integration, and ML-based spraying automation. The results indicate that supervised, unsupervised, and deep learning models increasingly contribute to improved real-time decision making, performance in pest and disease detection, as well as accurate application of agricultural plant protection. By utilising cutting-edge technology like multispectral sensors, LiDAR, and sophisticated neural networks, these systems significantly increase spraying operations’ efficiency while cutting waste and significantly minimising their negative effects on the environment. Notwithstanding significant advances, issues still exist, such as the requirement for high-quality datasets, system calibration, and flexibility in a range of field circumstances. This study highlights important gaps in the literature and suggests future areas of inquiry to develop ML-driven autonomous spraying even more, assisting in the shift to more intelligent and environmentally friendly farming methods. Full article

In recent decades, forests have experienced an increasing trend in the number of pest outbreaks worldwide, apparently driven by strong annual variability in precipitation, higher air temperatures, and strong winds. Pest outbreaks have negative ecological, economic, and environmental impacts on forest ecosystems, such as reduced biodiversity, carbon sequestration, and overall forest health. Traditional monitoring methods of these disturbances, while accurate, are time-consuming and limited in scope. Remote sensing, particularly UAV (Unmanned Aerial Vehicle)-based technologies, offers a precise and cost effective alternative for monitoring forest health. This study evaluates the temporal and spatial progression of bark beetle damage in a fir-dominated forest in the Zao Mountains, Japan, using UAV RGB imagery and DL (Deep Learning) models (YOLO - You Only Look Ones), over a four-year period (2021–2024). Trees were classified into six health categories: Healthy, Light Damage, Medium Damage, Heavy Damage, Dead, and Fallen. The results revealed a significant decline in healthy trees, from 67.4% in 2021 to 25.6% in 2024, with a corresponding increase in damaged and dead trees. Light damage emerged as a potential early indicator of forest health decline. The DL model achieved an accuracy of 74.9% to 82.8%. The results showed the effectiveness of DL in detecting severe damage but highlighted that challenges in distinguishing between healthy and lightly damaged trees still remain. The study highlights the potential of UAV-based remote sensing and DL for monitoring forest health, providing valuable insights for targeted management interventions. However, further refinement of the classification methods is needed to improve accuracy, particularly in the precise detection of tree health categories. This approach offers a scalable solution for monitoring forest health in similar ecosystems in other subalpine areas of Japan and the world. Full article

This paper presents a simulation study investigating the possible dispersal of the red palm weevil, a highly destructive pest affecting various palm species, across the island of Corfu, Greece. The simulation incorporates ecological modeling and geographical data to analyze the dynamics and the spread of red palm weevil populations over time and space. Key findings indicate that factors such as tree density and spatial distribution significantly influence infestation rates, with densely populated areas being more susceptible to rapid spreading. The study underscores the importance of early detection and targeted interventions to control red palm weevil populations and to mitigate their impact on affected regions. This research contributes to the development of effective pest management strategies that could potentially be adapted to address similar invasive species challenges in other agricultural contexts. Full article

This paper explores the transformative potential of Foundation Models (FMs) in agriculture, driven by the need for efficient and intelligent decision support systems in the face of growing global population and climate change. It begins by outlining the development history of FMs, including general FM training processes, application trends and challenges, before focusing on Agricultural Foundation Models (AFMs). The paper examines the diversity and applications of AFMs in areas like crop classification, pest detection, and crop image segmentation, and delves into specific use cases such as agricultural knowledge question-answering, image and video analysis, decision support, and robotics. Furthermore, it discusses the challenges faced by AFMs, including data acquisition, training efficiency, data shift, and practical application challenges. Finally, the paper discusses future development directions for AFMs, emphasizing multimodal applications, integrating AFMs across the agricultural and food sectors, and intelligent decision-making systems, ultimately aiming to promote the digitalization and intelligent transformation of agriculture. Full article

The globose scale (GS) Sphaerolecanium prunastri (Boyer de Fonscolombe) (Hemiptera: Coccidae) is a serious pest affecting plants within the Rosaceae, notably wild apricot, Armeniaca vulgaris (Lamarck). Following its initial detection in 2019, more than 80% of valleys with wild apricots have become affected in the Ili River Basin of the Tianshan Mountains in Xinjiang, China. This study assessed GS population dynamics post invasion and its effects on the growth and reproductive traits of wild apricot trees from 2019 to 2024. Nymph densities have decreased but remain high, with densities per 20 cm of shoots of 986 (1^st-instar nymphs) and 120 (2^nd-instar nymphs) in 2024, respectively. Damage has declined, with high damage rankings decreasing from 24% to 11% of wild apricot trees. However, the mortality of trees was higher (25%) in infested than non-infested areas (13%). Interestingly, GS feeding stimulated the growth of spring shoots but significantly reduced the reproductive capacity of wild apricots. Heavily infested trees exhibited increased shoot length (2–3 times), decreased fruit yield (20-fold), lower flowering percentage (8-fold), and reduced flower bud density (2-fold) compared to non-infested trees. Overall, despite a decrease in damage severity, wild apricot forests remain threatened by GS. Implementing integrated pest management (IPM) strategies is essential for effective GS management and the recovery of wild apricot forests. Full article

Invasive species pose a growing threat to global biodiversity, agricultural productivity, and ecosystem health, as climate change worsens their spread. This study focused on modelling the current and projected distribution of the Japanese beetle (Popillia japonica Newman), an invasive pest with potentially devastating impacts on crops and natural vegetation across Europe. Using the MaxEnt species distribution model, we integrated beetle occurrence data with bioclimatic variables, analyzing current and future climate scenarios based on Shared Socio-economic Pathways (SSP1-2.6, SSP2-4.5, SSP5-8.5) for near-term (2021–2040) and mid-term (2041–2060) periods. By reclassifying the model results, we identified European regions with negligible, low, medium, and high exposure to this invasive pest under climate change pathways. The results identified regions in central Europe covering an area of 83,807 km² that are currently at medium to high risk of Japanese beetle infestation. Future projections suggest northward expansion with suitable areas potentially increasing to 120,436 km² in the worst-case scenario, particularly in northern Italy, southern Germany, the Western Balkans, and parts of France. These spatially explicit findings can inform targeted monitoring, early detection, and management strategies to mitigate the economic and ecological threats posed by the Japanese beetle. Integrating species distribution modelling with climate change scenarios is imperative for science-based policies to tackle the growing challenge of biological invasions. This research provides a framework for assessing invasion risks at the European scale and guiding adaptive responses in agricultural and natural systems. Full article