Search Results (729)

Weed management has become a critical agricultural practice, as weeds compete with crops for nutrients, host pests and diseases, and cause major economic losses. The invasive weed Ambrosia artemisiifolia (common ragweed) is particularly problematic in Hungary, endangering crop productivity and public health through its fast proliferation and allergenic pollen. This review examines the current challenges and impacts of A. artemisiifolia while exploring sustainable approaches to its management through precision agriculture. Recent advancements in unmanned aerial vehicles (UAVs) equipped with advanced imaging systems, remote sensing, and artificial intelligence, particularly deep learning models such as convolutional neural networks (CNNs) and Support Vector Machines (SVMs), enable accurate detection, mapping, and classification of weed infestations. These technologies facilitate site-specific weed management (SSWM) by optimizing herbicide application, reducing chemical inputs, and minimizing environmental impacts. The results of recent studies demonstrate the high potential of UAV-based monitoring for real-time, data-driven weed management. The review concludes that integrating UAV and AI technologies into weed management offers a sustainable, cost-effective, mitigate the socioeconomic impacts and environmentally responsible solution, emphasizing the need for collaboration between agricultural researchers and technology developers to enhance precision agriculture practices in Hungary. Full article

This paper presents a low-cost, fully on-premise Edge Artificial Intelligence (AI) system designed to support real-time pest and disease detection in open-field chili pepper cultivation. The proposed architecture integrates AI-Thinker ESP32-CAM module (ESP32-CAM) image acquisition nodes (“Sticks”) with a Raspberry Pi 5–based edge server (“Module”), forming a plug-and-play Internet of Things (IoT) pipeline that enables autonomous operation upon simple power-up, making it suitable for aging farmers and resource-limited environments. A Leaf-First 2-Stage vision model was developed by combining YOLOv8n-based leaf detection with a lightweight ResNet-18 classifier to improve the diagnostic accuracy for small lesions commonly occurring in dense pepper foliage. To address network instability, which is a major challenge in open-field agriculture, the system adopted a dual-protocol communication design using Hyper Text Transfer Protocol (HTTP) for Joint Photographic Experts Group (JPEG) transmission and Message Queuing Telemetry Transport (MQTT) for event-driven feedback, enhanced by Redis-based asynchronous buffering and state recovery. Deployment-oriented experiments under controlled conditions demonstrated an average end-to-end latency of 0.86 s from image capture to Light Emitting Diode (LED) alert, validating the system’s suitability for real-time decision support in crop management. Compared to heavier models (e.g., YOLOv11 and ResNet-50), the lightweight architecture reduced the computational cost by more than 60%, with minimal loss in detection accuracy. This study highlights the practical feasibility of resource-constrained Edge AI systems for open-field smart farming by emphasizing system-level integration, robustness, and real-time operability, and provides a deployment-oriented framework for future extension to other crops. Full article

The contemporary agricultural market is profoundly volatile, where agricultural prices are based on a complex supply chain, climatic irregularity or unscheduled market demand. Prices of crops need to be predicted in a reliable and timely manner for farmers, policy-makers and other stakeholders to take evidence-based decisions ultimately for the benefit towards sustainable agriculture and economic sustainability. Objective: The objective of this study is to develop and evaluate a comprehensive machine learning model for predicting agricultural prices incorporating logistic, economic and environmental considerations. It is the desire to make agriculture more profitable by building simple and accurate forecasting models. Methods: An assorted dataset was collected, which covers major factors to constitute the dataset of temperature, rainfall, fertiliser use, pest and disease attack level, cost of transportation, market demand-supply ratio and regional competitiveness. The data was subjected to pre-processing and feature extraction for quality control/quality assurance. Several machine learning models (Linear Regression, Support Vector Machines, AdaBoost, Random Forest, and XGBoost) were trained and evaluated using performance metrics such as R2 score, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Results: Out of the model approaches that were analysed, predictive performance was superior for XGBoost (with an R2 Score of 0.94, RMSE of 12.8 and MAE of 8.6). To generate accurate predictions, the ability to account for complex non-linear relationships between market and environmental information was necessary. Conclusions: The forecast model of the XGBoost-based prediction system is reliable, of low complexity and widely applicable to large-scale real-time forecasting of agricultural monitoring. The model substantially reduces the uncertainty of price forecasting, and does so by including multivariate environmental and economic aspects that permit more profitable management practices in a schedule for future sustainable agriculture. Full article

Tick-borne diseases (TBDs) pose a significant threat to livestock productivity and rural livelihoods in South Africa, particularly among resource-poor communal farmers. This narrative review synthesises findings from case studies on communal farmers’ knowledge, attitudes, and practices (KAPs) toward TBDs and their control. The analysis reveals that while many farmers can identify TBDs and their symptoms, significant gaps exist in understanding acaricide resistance and effective tick management. Socioeconomic factors, including age, gender, education, and access to veterinary services, strongly influence knowledge and practices. Indigenous ethnoveterinary practices are commonly used alongside conventional methods, although their efficacy remains understudied. The review emphasises the importance of integrated pest management, participatory approaches, and targeted awareness campaigns. A One Health framework is recommended to enhance surveillance, collaboration, and sustainable TBD control. Empowering farmers through training and inclusive communication strategies is crucial for mitigating the impacts of TBDs on communal farming systems. Full article

Coconut palms (Cocos nucifera L.) are a critical economic and ecological resource in Wenchang City, Hainan. Accurate mapping of their spatial distribution is essential for precision agricultural planning and effective pest and disease management. However, in tropical monsoon regions, persistent cloud cover, spectral similarity with other evergreen species, and redundancy among high-dimensional features hinder the performance of optical classification. To address these challenges, we developed a scalable multi-source remote sensing framework on the Google Earth Engine (GEE) with an emphasis on species-oriented feature design rather than generic feature stacking. The framework integrates Sentinel-1 SAR, Sentinel-2 MSI, and SRTM topographic data to construct a 42-dimensional feature set encompassing spectral, polarimetric, textural, and topographic attributes. Using Random Forest (RF) importance ranking and out-of-bag (OOB) error analysis, an optimal 15-feature subset was identified. Four feature combination schemes were designed to assess the contribution of each data source. The fused dataset achieved an overall accuracy (OA) of 92.51% (Kappa = 0.8928), while the RF-OOB optimized subset maintained a comparable OA of 92.83% (Kappa = 0.8975) with a 64% reduction in dimensionality. Canopy Water Index (CWI), Green Chlorophyll Index (GCI), and VV-polarized backscattering coefficient (${\mathsf{\sigma }}_{\mathrm{V}\mathrm{V}}$) were identified as the most discriminative features. Independent UAV validation (0.07 m resolution) in a 50 km² area of Chongxing Town confirmed the model’s robustness (OA = 90.17%, Kappa = 0.8617). This study provides an efficient and robust framework for large-scale monitoring of tropical economic forests such as coconut palms. Full article

The excessive and random production of summer shoots poses significant challenges to pest and disease management and the improvement of fruit quality in citrus orchards. Although heavy fruit load has been observed to reduce summer shoot numbers, the mechanism is not well understood. This study combined a field investigation with a de-fruiting experiment to demonstrate that significant negative correlation exists between fruit load and summer shoot numbers in citrus orchard. Metabolomic analysis further indicated that fruits at the cell expansion stage function as dominant carbohydrate sinks, attracting more soluble sugars. De-fruiting significantly elevated sugar content and upregulated the transcript levels of sink strength-related genes (Sucrose synthase, CsSUS4/5/6) by more than 3.0-fold in the axillary buds. Additionally, exogenous application of sugar-related DAMs (differentially accumulated metabolites), such as sucrose, significantly promoted axillary bud outgrowth. Taken together, our findings confirm that heavy fruit load suppresses shoot branching, primarily through competing for soluble sugars. This provides a physiological basis for managing summer shoots by regulating fruit load, offering a practical strategy to enhance citrus orchard management and the effectiveness of pest and disease control programs. Full article

This study addressed the core challenge of intelligent pest and disease monitoring and early warning in smart horticultural production by proposing a multimodal deep learning framework based on multi-parameter environmental sensor arrays. The framework integrates visual information with electrical signals to overcome the inherent limitations of conventional single-modality approaches in terms of real-time capability, stability, and early detection performance. A long-term field experiment was conducted over 18 months in the Hetao Irrigation District of Bayannur, Inner Mongolia, using three representative horticultural crops—grape (Vitis vinifera), tomato (Solanum lycopersicum), and sweet pepper (Capsicum annuum)—to construct a multimodal dataset comprising illumination intensity, temperature, humidity, gas concentration, and high-resolution imagery, with a total of more than $2.6\times {10}^6$ recorded samples. The proposed framework consists of a lightweight convolution–Transformer hybrid encoder for electrical signal representation, a cross-modal feature alignment module, and an early-warning decision module, enabling dynamic spatiotemporal modeling and complementary feature fusion under complex field conditions. Experimental results demonstrated that the proposed model significantly outperformed both unimodal and traditional fusion methods, achieving an accuracy of $0.921$, a precision of $0.935$, a recall of $0.912$, an F1-score of $0.923$, and an area under curve (AUC) of $0.957$, confirming its superior recognition stability and early-warning capability. Ablation experiments further revealed that the electrical feature encoder, cross-modal alignment module, and early-warning module each played a critical role in enhancing performance. This research provides a low-cost, scalable, and energy-efficient solution for precise pest and disease management in intelligent horticulture, supporting efficient monitoring and predictive decision-making in greenhouses, orchards, and facility-based production systems. It offers a novel technological pathway and theoretical foundation for artificial-intelligence-driven sustainable horticultural production. Full article

Unmanned Aerial Vehicles (UAVs) have become essential instruments for precision agriculture and forest monitoring, offering rapid, high-resolution data collection over wide areas. This review synthesizes global advances (2015–2024) in UAV-derived vegetation indices (VIs), combining bibliometric and content analyses of 472 peer-reviewed publications. The study identifies key research trends, dominant indices, and technical progress achieved through RGB, multispectral, hyperspectral, and thermal sensors. Results show an exponential growth of scientific output, led by China, the USA, and Europe, with NDVI, NDRE, and GNDVI remaining the most widely applied indices. New indices such as GSI, RBI, and MVI demonstrate enhanced sensitivity for stress and disease detection in both crops and forests. UAV-based monitoring has proven effective for yield prediction, water-stress evaluation, pest identification, and biomass estimation. Despite significant advances, challenges persist regarding illumination correction, soil background influence, and limited forestry applications. The paper concludes that UAV-derived vegetation indices—when integrated with machine learning and multi-sensor data—represent a transformative approach for the sustainable management of agricultural and forest ecosystems. Full article

This study explores the use of remote sensing and machine learning (ML) for early detection of Pyricularia oryzae (rice blast) in ‘Bomba’ rice. Conducted in Spain’s Albufera Natural Park over four seasons (2021–2024), 94 fields were monitored using Sentinel-2 imagery and Topcon Yield Trakk data. Principal Component Analysis (PCA) identified key spectral bands (B03, B04, B05, B07, B08, B11) at early stages (35 and 55 DAS). Three ML classifiers—K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Machines (SVMs)—were tested to categorize fields by yield-based infection levels. RF achieved the best performance (up to 94% Accuracy), showing high robustness across band combinations and dates. KNN was more input-sensitive, and SVM performed weakest. Integrating multispectral and multitemporal data enhanced accuracy. Overall, RF and remote sensing proved reliable tools for early disease detection, supporting Precision Agriculture and real-time pest management. Full article

Between 2023 and 2025, we conducted experiments at the Laboratory Field of the Bio-technical Faculty in Ljubljana to study alternative methods for controlling wireworms in potato fields. The trials were arranged in three blocks with five first-order (Brassica carinata, Brassica juncea, Nemakil 330, Rasti Soil Tonic G, positive control) and five second-order treatments (entomopathogenic nematodes, entomopathogenic fungi, zeolite combined with half-doses of these products, positive control with tefluthrin, and negative control), giving twenty-five treatments per block. Foliar pests and diseases were managed with contact plant protection products. We measured total tuber yield and divided it into three size classes, then assessed wireworm damage (holes per tuber). The purpose of the soil excavations in the first-order treatments was to verify the abundance of wireworms in the soil. Most combinations reduced wireworm abundance. The lowest tuber damage comparable to the positive control occurred when using zeolite with half-doses of entomopathogenic nematodes and fungi. The highest yields across all three weather-distinct years resulted from combining Rasti Soil Tonic with zeolite and half-dose entomopathogenic products. Although Nemakil 330 increased soil phosphorus, it neither improved yield nor reduced wireworm damage. Overall, the tested environmentally acceptable methods show promising insecticidal potential for sustainable wireworm control in potatoes. Full article

With the large-scale invasion of alien species, forest ecosystems are facing severe challenges, and the health of trees is increasingly threatened. Accurately detecting and counting trees affected by such invasive species has become a critical issue in forest conservation and resource management. Traditional detection methods usually rely only on the information of a single modality of an image, lack linguistic or semantic guidance, and often can only model a specific diseased tree situation during training, making it difficult to achieve effective differentiation and generalization of multiple diseased tree types, which limits their practicality. To address the above challenges, we propose an end-to-end multimodal diseased tree detection model. In the visual encoder of the model, we introduce rotational positional encoding to enhance the model’s ability to perceive detailed structures of trees in images. This design enables more accurate extraction of features related to diseased trees, especially when processing images with complex environments. At the same time, we further introduce a cross-attention mechanism between image and text modalities, so that the model can realize the deep fusion of visual and verbal information, thus improving the detection accuracy based on understanding and recognizing the semantics of the disease. Additionally, this method possesses strong generalization capabilities, enabling effective recognition based on textual descriptions even when samples are not available. Our model achieves optimal results on the Larch Casebearer dataset and the Pests and Diseases Tree dataset, verifying the effectiveness and generalizability of the method. Full article

Azole compounds are extensively utilized in plant protection products for managing pests and diseases in both agriculture and horticulture. Moreover, azoles are the most extensively used class of fungicides worldwide. In addition to being effective against human pathogenic fungi, they are used in the food and agricultural industries to prevent and control fungal infections in crops. Unfortunately, the extensive use of azoles and subsequent overexposure have led to undesirable effects on ecosystems and non-target aquatic and terrestrial organisms. In the last decade alone, the European Union (EU) has prohibited numerous pesticides, many of which are based on azoles. Numerous azoles, especially triazoles, pyrazoles, imidazoles, and oxazoles, are still approved as active ingredients in plant protection products in the EU due to their excellent activity and minimal environmental and health impacts. However, for some, the expiry date is as close as March 2026. A computational approach for estimating their effectiveness against harmful and non-target organisms in soil, as well as detailed research into the molecular mechanism of action, is used for further evaluation of the compounds. This review provides an overview of azole pesticides and a summary of recent knowledge addressing their toxicity, future prospects, methods, and strategies to overcome their limitations. Full article

Watercress is a nutrient-dense, aquatic leafy vegetable with significant public health and economic potential. Hydroponically cultivated watercress can offer greater nutritional benefits due to the controlled delivery of specific nutrients. From an agronomist’s perspective, watercress has the advantage of optimized environmental resource efficiency, achieved through reduced energy, chemical, and water consumption, as well as its short cultivation cycle. Glucosinolates (GSLs) in watercress enhance sustainable agriculture by naturally protecting crops from pests and diseases, reducing the need for chemical inputs. They also increase market value and shelf-life, supporting resource-efficient and profitable farming. Within the pharmaceutical space, GSLs are well-known for their chemo preventive and anti-inflammatory properties. This review aims to summarize research findings, critically evaluate existing studies to highlight current knowledge, and identify research gaps, and to guide future investigations. The synthesis of the reviewed literature demonstrates that increased sulphate generally improves GSL content. However, not many studies have looked specifically at how magnesium sulphate (MgSO₄) affects watercress. This review highlights the specific impact of MgSO₄ on GSL production in watercress, which could provide valuable insights for optimizing nutrient management in hydroponic systems and enhancing the health benefits of this nutrient-dense crop. Full article

Potato (Solanum tuberosum L.) is the third most important food crop worldwide and a cornerstone of food security across the Andean region. However, its production is increasingly threatened by the potato psyllid Bactericera cockerelli (Šulc), the vector of Candidatus Liberibacter solanacearum, the causal agent of the purple-top complex associated with zebra chip disease, which severely reduces both tuber yield and quality. This study was conducted from September 2024 to February 2025 in the province of Huancabamba, Peru, to evaluate the efficacy of biological and chemical control agents against B. cockerelli under field conditions. A randomized complete block design was implemented with five treatments and four replicates, totaling 20 experimental units, each consisting of 20 potato plants (S. tuberosum L.), of which 10 plants were evaluated. Treatments included an untreated control (T0), a chemical control (thiamethoxam + lambda-cyhalothrin, abamectin, and imidacloprid) (T1), and three biological control agents: Beauveria bassiana CCB LE-265 (>1.5 × 10¹⁰ conidia g⁻¹) (T2), Paecilomyces lilacinus strain 251 (1.0 × 10¹⁰ conidia g⁻¹) (T3), and Metarhizium anisopliae (1.0 × 10¹⁰ conidia g⁻¹) (T4). Foliar applications targeted eggs, nymphs, and adults of the psyllid. Results indicated that B. cockerelli mortality across developmental stages was lower under biological treatments compared with T1, which achieved the lowest probability of purple-top symptom expression (46%) and a zebra chip incidence of 60.60%. Among the biological agents, M. anisopliae (T4) reduced incidence to 56.60%, while P. lilacinus (T3) demonstrated consistent suppression of nymphal populations. In terms of yield, T1 achieved the highest tuber weight (198.86 g plant⁻¹) and number of tubers (7.74 plant⁻¹), followed by T3 (5.08) and T4 (4.24). Nevertheless, all treatments exhibited low yields and small tuber sizes, likely due to unfavorable environmental conditions and the presence of the invasive pest. Overall, chemical control was more effective than biological agents; however, the latter showed considerable potential for integration into sustainable pest management programs. Importantly, vector suppression alone does not guarantee the absence of purple-top complex symptoms or zebra chip disease in potato tubers. Full article

Every year, up to 40% of the crops in the world are lost to pests. Plants have suffered from prolonged biotic stresses and abiotic stresses, which cause significant changes in complex crop ecosystems, necessitating intensive pest management strategies that have often been accompanied by the struggle against plant pests. Plant pests and diseases control methods heavily reliant on chemical pesticides have caused many adverse effects. One innovative method involves using ultrafine bubble (UFB) waters, which can enable pesticide reduction action for the plant pest control. The classification and six properties of UFBs were summarized, and the generation approaches of UFBs were introduced based on physical and chemical methods. The applications of UFBs and ozone UFB waters in plant protection practices were comprehensively reviewed, in which UFB waters against the plant pests and the soilborne, airborne and waterborne diseases were analyzed, and the abiotic stresses of crops in high-salinity soil and contaminated soil, drought, and soil with heavy metals were reviewed. Despite promising applications, UFB technology has limitations. Aiming at pesticide reduction and replacement using UFB waters, the mechanism of UFB water controlling plant pests and diseases, the molecular mechanism of UFB water affecting plant pest resistance, the plant growth in harsh polluted environments, the UFB behavior with hydrophobic and hydrophilic surfaces of crops, and the building of an integrated intelligent crop growth system were proposed. Full article