Search Results (627)

Precise control of feeding rates is critically important in intensive shrimp farming for cost reduction, optimization of farming strategies, and protection of the aquatic environment. However, current assessment of residual feed in feeding trays relies predominantly on manual visual inspection, which is inefficient, highly subjective, and difficult to standardize. The residual feed particles typically exhibit characteristics such as small size, high density, irregular shapes, and mutual occlusion, posing significant challenges for automated visual detection. To address these issues, this study proposes a lightweight detection model named YOLO-Shrimp. To enhance the network’s capability in extracting features from small and dense targets, a novel attention mechanism termed EnSimAM is designed. Building upon the SimAM structure, EnSimAM incorporates local variance and edge response to achieve multi-scale feature perception. Furthermore, to improve localization accuracy for small objects, an enhanced weighted intersection over union loss function, EnWIoU, is introduced. Additionally, the lightweight RepGhost module is adopted as the backbone of the model, significantly reducing both the number of parameters and computational complexity while maintaining detection accuracy. Evaluated on a real-world aquaculture dataset containing 3461 images, YOLO-Shrimp achieves mAP@0.5 and mAP@0.5:0.95 scores of 70.01% and 28.01%, respectively, while reducing the parameter count by 19.7% and GFLOPs by 14.6% compared to the baseline model. Full article

This paper presents the design and implementation of an IoT-based automation system for indoor hydroponic plant cultivation using the Nutrient Film Technique. The system employs an ESP32-based controller with multiple sensors and actuators. These enable real-time monitoring and control of pH, TDS, temperature, humidity, light, tank level, and flow conditions. A modular five-layer architecture was developed. It combines the MING stack, which includes MQTT communication, InfluxDB time-series storage, Node-RED flow processing, and Grafana visualization. The system also includes a Flutter-based mobile app for remote access. Key features include temperature-compensated calibration, hysteresis-based control algorithms, dual-mode operation, TLS/ACL security, and automated alarm mechanisms. These features enhance reliability and safety. Experimental results showed stable pH/TDS regulation, dependable actuator and alarm responses, and secure long-term data logging. The proposed open-source and low-cost platform is scalable. It provides a solution for small-scale producers and urban farming, bridging the gap between academic prototypes and production-grade smart agriculture systems. In comparison to related works that mainly focus on monitoring, this study advances the state of the art. It combines continuous time-series logging, secure communication, flow verification, and integrated safety mechanisms to provide a reproducible testbed for future smart agriculture research. Full article

The convergence of food insecurity, water scarcity, and environmental degradation has intensified the global search for sustainable agricultural models. Integrated Microalgal–Aquaponic Systems (IAMS) have emerged as a novel multi-trophic platform that unites aquaculture, hydroponics, and microalgal cultivation into a closed-loop framework for resource-efficient food production and water recovery. This critical review synthesizes empirical findings and engineering advancements published between 2008 and 2024, evaluating IAMS performance relative to traditional agriculture and recirculating aquaculture systems (RAS). Reported under controlled laboratory and pilot-scale conditions, IAMS have achieved nitrogen and phosphorus recovery efficiencies exceeding 95% while potentially reducing water consumption by up to 90% compared to conventional farming. The integration of microalgal photobioreactors enhances nutrient retention, may contribute to internal carbon capture, and enables the generation of diversified co-products, including biofertilizers and protein-rich aquafeeds. Nevertheless, significant barriers to commercial scalability persist, including the biological complexity of maintaining multi-trophic synchrony, high initial capital expenditure (CAPEX), and regulatory ambiguity regarding the safety of waste-derived algal biomass. Technical challenges such as photobioreactor upscaling, biofouling control, and energy optimization are critically discussed. Finally, the review evaluates the alignment of IAMS with UN Sustainable Development Goals 2, 6, and 13, and outlines future research priorities in techno-economic modeling, automation, and policy development to facilitate the transition of IAMS from pilot-scale innovations to viable industrial solutions. Full article

The establishment of a young farmer in the rural economy is a key stage in the process of farm succession in the rural development environment. In this matter, Pillar II of the Common Agricultural Policy (CAP) has a distinct approach related to financing the initiatives of this establishment. A young farmer can obtain funds for their agricultural activity by submitting a funding project proposal to the national agency. The success of a funding project proposal depends on various factors. In this paper, a model of prediction and classification using supervised learning algorithms, primarily Random Forest (RF) and Logistic Regression (LR), was developed to predict project selection outcomes and identify the key determinants of success. This was developed in relation to proposals submitted in the period 2014–2021 through Sub-Measure 6.1 and through the intervention for the young farmer installation intervention under the 2023–2027 CAP Strategic Plan (DR-30 (2023–2027)—Young Farmer Installation, indicated in this paper as DR 30) for the period of 2023–2027. Using the historical data related to this proposal, several models that use automated learning were developed in order to predict the success of a proposal based on specific determinants. In addition, a classification model was used to determine patterns in the proposal data, obtaining several project proposal clusters with common characteristics. The variables and selection criteria with the greatest impact on the final score and probability of acceptance were identified, highlighting the differences between sub-measures and the implications for generational renewal policies in rural areas. The novelty of this study lies in the integration of predictive modeling, classification, and clustering within a unified, policy-oriented analytical framework applied to real administrative data. The results reveal that project selection outcomes are driven primarily by formal scoring components, while structural characteristics such as farm economic size and planned investment play a secondary but consistent role across programming periods. These findings provide actionable insights for refining selection criteria and advisory mechanisms under the Common Agricultural Policy. Full article

Individual animal identification is a cornerstone of animal welfare practices and is of crucial importance for food safety and the protection of humans from zoonotic diseases. It is also a key prerequisite for enabling automated processes in modern dairy farming. With newly emerging technologies, visual animal identification based on machine learning offers a more efficient and non-invasive method with high automation potential, accuracy, and practical applicability. However, a common challenge is the limited variability of training datasets, as images are typically captured in controlled environments with uniform backgrounds and fixed poses. This study investigates the impact of foreground segmentation and background removal on the performance of convolutional neural networks (CNNs) for cow identification. A dataset was created in which training images of dairy cows exhibited low variability in pose and background for each individual, whereas the test dataset introduced significant variation in both pose and environment. Both a fine-tuned CNN backbone and a model trained from scratch were evaluated using images with and without background information. The results demonstrate that although training on segmented foregrounds extracts intrinsic biometric features, background cues carry more information for individual recognition. Full article

Ruminant livestock production faces increasing pressure to reduce environmental impacts while maintaining productivity and food security. This comprehensive review examines current strategies and emerging technologies for enhancing environmental sustainability in ruminant systems. The review synthesizes recent advances across four interconnected domains: genetic and genomic approaches for breeding environmentally efficient animals, rumen microbiome manipulation, nutritional strategies for emission reduction, and precision management practices. Specifically, genetic and genomic strategies demonstrate significant potential for long-term sustainability improvements through selective breeding for feed efficiency, methane reduction, and enhanced longevity. Understanding host–microbe interactions and developing targeted interventions have also shown promising effects on optimizing fermentation efficiency and reducing methane production. Key nutritional interventions include dietary optimization strategies that improve feed efficiency, feed additives, and precision feeding systems that minimize nutrient waste. Furthermore, management approaches encompass precision livestock farming technologies including sensor-based monitoring systems, automated feeding platforms, and real-time emission measurement tools that enable data-driven decision making. Integration of these approaches through system-based frameworks offers the greatest potential for achieving substantial environmental improvements while maintaining economic viability. In addition, this review identifies key research gaps including the need for standardized measurement protocols, long-term sustainability assessments, and economic evaluation frameworks. Future directions emphasize the importance of interdisciplinary collaboration, policy support, and technology transfer to accelerate adoption of sustainable practices across diverse production systems. Full article

AI enhances aquatic resource management by automating species detection, optimizing feed, forecasting water quality, protecting species interactions, and strengthening the detection of illegal, unreported, and unregulated fishing activities. However, these advancements are inconsistently employed, subject to domain shifts, limited by the availability of labeled data, and poorly benchmarked across operational contexts. Recent developments in technology and applications in fisheries genetics and monitoring, precision aquaculture, management, and sensing infrastructure are summarized in this paper. We studied automated species recognition, genomic trait inference, environmental DNA metabarcoding, acoustic analysis, and trait-based population modeling in fisheries genetics and monitoring. We used digital-twin frameworks for supervised learning in feed optimization, reinforcement learning for water quality control, vision-based welfare monitoring, and harvest forecasting in aquaculture. We explored automatic identification system trajectory analysis for illicit fishing detection, global effort mapping, electronic bycatch monitoring, protected species tracking, and multi-sensor vessel surveillance in fisheries management. Acoustic echogram automation, convolutional neural network-based fish detection, edge-computing architectures, and marine-domain foundation models are foundational developments in sensing infrastructure. Implementation challenges include performance degradation across habitat and seasonal transitions, insufficient standardized multi-region datasets for rare and protected taxa, inadequate incorporation of model uncertainty into management decisions, and structural inequalities in data access and technology adoption among smallholder producers. Standardized multi-region benchmarks with rare-taxa coverage, calibrated uncertainty quantification in assessment and control systems, domain-robust energy-efficient algorithms, and privacy-preserving data partnerships are our priorities. These integrated priorities enable transition from experimental prototypes to a reliable, collaborative infrastructure for sustainable wild capture and farmed aquatic systems. Full article

Prompt identification of clinical signs and early treatment of hoof problems are essential to effectively manage and reduce lameness in dairy farms. This study aimed to evaluate the influence of days in milk (DIM), parity, and milk yield (MY) on the mean temperature (MT) of the hind hooves in healthy cows, with the perspective of implementing infrared thermography (IRT) as an automated tool for early lameness detection. Thermal images were collected from 156 milking cows, capturing both cranial and caudal surfaces of each hind foot. Significant differences were found between primiparous and multiparous cows across all analyzed surfaces. Moreover, cows with higher milk production exhibited significantly higher MT in the caudal left hoof and on both cranial surfaces. The variable DIM (group 1 = cows with ≤202 DIM; group 2 = cows with >202 DIM) did not significantly affect MT on caudal surfaces; however, on the cranial view, MT of the right hoof was higher in group 2, while group 1 tended to show higher MT in the left hoof (p = 0.051). In conclusion, hoof MT increases in multiparous and high-producing cows. Additionally, during the first 200 days of lactation, cranial hoof surface temperatures tend to rise. Future studies should include continuous monitoring using automated systems to record variations throughout the day. Full article

Artificial intelligence (AI) shows great promise for transforming the agriculture sector and can enable the development of many modern farming practices over conventional methods. Nowadays, AI agents and agentic AI have attained popularity due to their autonomous structure and working mechanism. This research work proposes an agentic AI framework that integrates multiple agents developed for farming land to promote climate-smart agriculture and support United Nations (UN) sustainable development goals (SDGs). The developed structure has four agents: Agent A for monitoring soil properties, Agent B for weather sensing, Agent C for disease detection vision sensing in rice crops, and Agent D, a multi-agent supervisor agent chatbot connected with the other agents. The overall objective was to connect all agents on a single platform to obtain sensor data and perform a predictive analysis. This will help farmers and landowners obtain information about weather conditions, soil properties, and vision-based disease detection so that appropriate measures can be taken on agricultural land for rice crops. For soil properties (nitrogen, phosphorus, and potassium) from Agent A and climate data (temperature and humidity) from Agent B, we deployed the long short-term memory (LSTM), gated recurrent unit (GRU), and one-dimensional convolutional neural network (1D-CNN) predictive models, which achieved an accuracy of 93.4%, 94%, and 96% for Agent A; a 0.27 mean absolute error (MAE) for temperature; and a 2.9 MAE for humidity on the Agent B data. For Agent C, we used vision transformer (ViT), MobileViT, and RiceNet (with a diffusion model layer as a feature extractor) models to detect disease. The models achieved accuracies of 95%, 98.5%, and 85.4% during training respectively. Overall, the proposed framework demonstrates how agentic AI can be used to transform conventional farming practices into a digital process, thereby supporting smart agriculture. Full article

Efficient monitoring of large-scale poultry farms requires the timely identification of dead broilers, as delays can accelerate disease transmission, leading to significant economic loss. Nevertheless, manual inspection remains the dominant practice, resulting in a labor-intensive, inconsistent, and poorly scalable workflow. Although recent advances in computer vision have introduced automated alternatives, most existing approaches face difficulties in crowded settings where live and dead broilers share similar visual patterns, and occlusions frequently occur. To address these problems, we propose a transformer-based multi-task segmentation framework designed to operate reliably in visually complex farm environments. The model constructs a unified feature representation that supports precise segmentation of dead broilers, while an auxiliary dead broiler counting task contributes additional supervisory features that enhance segmentation performance across diverse scene configurations. Experimental evaluations indicate that the proposed method yields accurate and stable segmentation results under various farm conditions, including densely populated and visually intricate scenes. Moreover, its overall segmentation accuracy consistently surpasses that of existing approaches, demonstrating the effectiveness of integrating transformer-based global modeling with the auxiliary regression objective. Full article

Analog electronics, i.e., circuits that process continuously varying signals, have quietly powered the backbone of agricultural automation long before the advent of modern digital technologies. Yet, the accelerating focus on digitalization, IoT, and AI in precision agriculture has largely overshadowed the enduring, indispensable role of analog components in sensing, signal conditioning, power conversion, and actuation. This paper provides a comprehensive state-of-the-art review of analog electronics applied to agricultural systems. It revisits historical milestones, from early electroculture and soil-moisture instrumentation to modern analog front-ends for biosensing and analog electronics for alternatives source of energy and weed control. Emphasis is placed on how analog electronics enable real-time, low-latency, and energy-efficient interfacing with the physical world, a necessity in farming contexts where ruggedness, simplicity, and autonomy prevail. By mapping the trajectory from electroculture experiments of the 18th-century to 21st-century transimpedance amplifiers, analog sensor nodes, and low-noise instrumentation amplifiers in agri-robots, this work argues that the true technological revolution in agriculture is not purely digital but lies in the symbiosis of analog physics and biological processes. Full article

Predictive maintenance (PdM) often fails to progress beyond pilot projects because machine learning-based anomaly detection requires expert knowledge, extensive tuning, and labeled fault data. This paper presents an automated prototype that builds and evaluates multiple anomaly detection models with minimal manual configuration. The prototype automates feature creation, model training, hyperparameter search, and ensemble construction, while allowing domain experts to control how anomaly alerts are triggered and how detected events are reviewed. Developed in a multi-year photovoltaic (PV) solar farm case study, it targets operational anomalies such as sudden drops, underperformance periods, and abnormal drifts, using expert validation and synthetic benchmarks to shape and evaluate anomaly categories. Experiments on the real PV data, a synthetic PV benchmark, and a machine temperature dataset from the Numenta Anomaly Benchmark show that no single model performs best across datasets. Instead, diverse base models and both rule-based and stacked ensembles enable robust configurations tailored to different balances between missed faults and false alarms. Overall, the prototype offers a practical and accessible path toward PdM adoption by lowering technical barriers and providing a flexible anomaly detection approach that can be retrained and transferred across industrial time-series datasets. Full article

Accurate estimation of live weight is essential for efficient management and precision control in poultry production. This study evaluated the potential of supervised machine learning (ML) algorithms and digital image analysis for non-invasive prediction of live weight in Anadolu-T broilers, a locally developed genotype in Türkiye. A total of 4200 records were collected from 100 broilers (50 males and 50 females) over 42 days, including daily measurements of back length, back width, and live weight. Five ML algorithms—Random Forest (RF), k-Nearest Neighbors (KNN), Support Vector Regression (SVR), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR)—were trained and validated to estimate live weight based on morphometric traits. Among all algorithms, KNN achieved the highest accuracy (R² = 0.982, RMSE = 111.509 g, MAPE = 8.205%), followed by RF and XGB, which also produced stable and reliable predictions. The image-based models using log-transformed regression between body surface pixel area and live weight yielded similar accuracy (R² = 0.989, RMSE = 101.197 g, MAPE = 7.266%), confirming that projected surface area can effectively represent growth progression. The results demonstrate that integrating ML algorithms with digital imaging offers a practical, cost-effective, and non-invasive approach for real-time broiler weight estimation. This approach supports the advancement of precision poultry farming through automated, data-driven growth monitoring. Full article

Subclinical mastitis is a major but often overlooked constraint to dairy productivity, causing economic losses through reduced milk yield and quality. In Romania, comprehensive data on the bacterial etiology and antimicrobial resistance (AMR) of subclinical mastitis are limited. This study aimed to characterize the etiological agents and antimicrobial susceptibility profiles of major bacterial pathogens isolated from subclinical mastitis cases in dairy cows from Western Romania. Between 2021 and 2022, milk samples were collected from 117 lactating cows diagnosed with subclinical mastitis on three dairy farms. Bacterial isolation and differentiation were performed on ChromID^® CPS^® Elite Agar, and isolates were confirmed by standard biochemical tests. Antimicrobial susceptibility testing of Staphylococcus aureus and Escherichia coli isolates was conducted using the automated Vitek^®2 system, interpreted according to CLSI veterinary standards. Multidrug resistance (MDR) was defined as resistance to at least one agent in three or more antimicrobial classes. Bacterial growth occurred in 51 of 117 samples (43.6%). S. aureus subsp. aureus predominated (28.2%), followed by E. coli (4.3%), Klebsiella pneumoniae subsp. pneumoniae (2.3%), and Streptococcus uberis (2.3%). Mixed infections occurred in 6.0% of positive samples. Among S. aureus, the highest resistance rates were to fosfomycin (58.3%), penicillin (44.4%), clindamycin (44.4%), and tetracycline (41.7%), with 47.2% MDR isolates. E. coli showed resistance to amoxicillin/clavulanic acid (88.9%), ampicillin (55.6%), and cefotaxime (55.6%), with 66.6% MDR. S. aureus remains the leading cause of subclinical mastitis in Western Romania. The high MDR rates highlight the need for targeted antimicrobial stewardship, culture-based therapy, and continuous AMR monitoring under the “One Health” framework. Full article

In the face of escalating global challenges, including climate change, food insecurity, freshwater scarcity, soil degradation, and rapid urbanization, soilless farming systems, such as hydroponics, aquaponics, and bioponics, have emerged as innovative and sustainable farming solutions. Combined with precision agriculture technologies, these systems enable real-time optimization of inputs through smart sensors, automation, and predictive modeling, significantly reducing resource consumption while improving crop yields. This review provides a unique contribution by integrating and comparing the three major soilless systems within a single framework and by highlighting, for the first time, their potential synergies with precision agriculture. It critically examines soilless cultivation systems and their relationship with precision agriculture, assessing the agronomic, environmental, and economic benefits as well as the main challenges, including high initial costs, high energy consumption, the complexity of managing biological inputs, the lack of standardized protocols, and limited accessibility for small-scale producers. The review highlights the need to integrate renewable energy sources, develop biodegradable substrates, apply life cycle assessment methodologies, and implement adequate training and regulatory frameworks to promote wider adoption and sustainability. Full article