Search Results (18)

Livestock buildings in rural areas are increasingly recognized for their environmental impact, yet few studies provide applied, scenario-based evaluations to guide retrofit interventions. While the existing literature acknowledges the environmental burden of livestock facilities, it often lacks operationally grounded analyses applicable to real-world agricultural contexts. This paper proposes an original integration of experimental climatic monitoring and life cycle assessment (LCA) to evaluate retrofit scenarios for energy efficiency in real poultry farming contexts. Based on an accurate climatic monitoring campaign conducted on-site during the spring and summer periods, relevant data were collected on air temperature, humidity, wind speed, and solar radiation affecting two poultry tunnels in central Italy, highlighting the need for thermal mitigation. The comparison between the observed operational scenario and the hypothesized improved scenario, involving energy supply from photovoltaic sources, evaluated using the PVGIS tool, demonstrated a significant reduction in environmental impact, with a 33.4% decrease in global warming potential and a 26.1% reduction in energy consumption. This study combines experimental on-site climatic data collection with comparative environmental evaluation using LCA methodology. The LCA approach, which guided the entire study, highlighted how the energy efficiency gained through solar panels adequately offsets their production and maintenance costs over the long term. These findings offer a replicable model for energy retrofits in rural livestock facilities, contributing to both environmental goals and rural resilience. Full article

Winter catch crops are promoted in the European Union under the Common Agricultural Policy to improve soil health and reduce nitrate leaching from agricultural fields. Currently, Member States often monitor farmers’ adoption through on-site inspections for a limited subset of parcels. Because of its potential for region-wide coverage, this study investigates the potential of Sentinel-2 satellite time series to classify catch crops at the field level in Flanders (Belgium). The first objective was to classify catch crops and identify the optimal model and time-series input for this task. The second objective was to apply these findings in a real-world scenario, aiming to provide reliable early-season predictions in a separate target year, testing early-season performance and temporal transferability. The following three models were compared: Random Forest (RF), Time Series Forest (TSF), and a One-Dimensional Convolutional Neural Network (1D-CNN). The results showed that, with a limited field-based training dataset, RF produced the most robust results across different time-series inputs, achieving a median F1-score of >88% on the best dataset. Additionally, the early-season performance of the models was delayed in the target year, reaching the F1-score threshold of 85% at least one month later in the season compared to the training years, with large timing differences between the models. Full article

Detecting early plant diseases autonomously poses a significant challenge for self-navigating robots and automated systems utilizing Artificial Intelligence (AI) imaging. For instance, Botrytis cinerea, also known as gray mold disease, is a major threat to agriculture, particularly impacting significant crops in the Cucurbitaceae and Solanaceae families, making early and accurate detection essential for effective disease management. This study focuses on the improvement of deep learning (DL) segmentation models capable of early detecting B. cinerea on Cucurbitaceae crops utilizing Vision Transformer (ViT) encoders, which have shown promising segmentation performance, in systemic use with the Cut-and-Paste method that further improves accuracy and efficiency addressing dataset imbalance. Furthermore, to enhance the robustness of AI models for early detection in real-world settings, an advanced imagery dataset was employed. The dataset consists of healthy and artificially inoculated cucumber plants with B. cinerea and captures the disease progression through multi-spectral imaging over the course of days, depicting the full spectrum of symptoms of the infection, ranging from early, non-visible stages to advanced disease manifestations. Research findings, based on a three-class system, identify the combination of U-Net++ with MobileViTV2-125 as the best-performing model. This model achieved a mean Dice Similarity Coefficient (mDSC) of 0.792, a mean Intersection over Union (mIoU) of 0.816, and a recall rate of 0.885, with a high accuracy of 92%. Analyzing the detection capabilities during the initial days post-inoculation demonstrates the ability to identify invisible B. cinerea infections as early as day 2 and increasing up to day 6, reaching an IoU of 67.1%. This study assesses various infection stages, distinguishing them from abiotic stress responses or physiological deterioration, which is crucial for accurate disease management as it separates pathogenic from non-pathogenic stress factors. The findings of this study indicate a significant advancement in agricultural disease monitoring and control, with the potential for adoption in on-site digital systems (robots, mobile apps, etc.) operating in real settings, showcasing the effectiveness of ViT-based DL segmentation models for prompt and precise botrytis detection. Full article

Digital twins based on real-world scenarios are heavily reliant on extensive on-site data, representing a significant investment in information technology. This study aims to maximize the capabilities of visual sensors, like cameras in controlled-environment agriculture, by acquiring more target-specific information at minimal additional cost. This approach not only reduces investment but also increases the utilization rate of existing equipment. Utilizing YOLOv7, this paper introduces a system with rotatable pan-tilt cameras for the comprehensive monitoring of large-scale greenhouse ventilation systems. To mitigate the computational load on edge servers at greenhouse sites caused by an abundance of video-processing tasks, a Region of Interest (ROI) extraction method based on tracking is adopted. This method avoids unnecessary calculations in non-essential areas. Additionally, we integrate a self-encoding approach into the training phase, combining object detection and embedding to eliminate redundant feature extraction processes. Experimental results indicate that ROI extraction significantly reduces the overall inference time by more than 50%, and by employing LSTM to classify the state of the fan embedding sequences, a 100% accuracy rate was achieved. Full article

Smart Agriculture has gained significant attention in recent years due to its benefits for both humans and the environment. However, the high costs associated with commercial devices have prevented some agricultural lands from reaping the advantages of technological advancements. Traditional methods, such as reflectance spectroscopy, offer reliable and repeatable solutions for soil property sensing, but the high costs and redundancy of preprocessing steps limit their on-site applications in real-world scenarios. Recently, RF-based soil sensing systems have opened a new dimension in soil property analysis using IoT-based systems. These systems are not only portable, but also significantly cheaper than traditional methods. In this paper, we carry out a comprehensive review of state-of-the-art soil property sensing, divided into four areas. First, we delve into the fundamental knowledge and studies of reflectance-spectroscopy-based soil sensing, also known as traditional methods. Secondly, we introduce some RF-based IoT soil sensing systems employing a variety of signal types. In the third segment, we introduce the details of sample pretreatment, inference methods, and evaluation metrics. Finally, after analyzing the strengths and weaknesses of the current work, we discuss potential future aspects of soil property sensing. Full article

Grain size is one of the most frequently selected traits during domestication and modern breeding. The continued discovery and characterization of new genes and alleles in controlling grain size are important in safeguarding the food supply for the world’s growing population. Previously, a small grain size was observed in a rice restorer line ‘Fuhui212’, while the underlying genetic factors controlling this trait were unknown. In this study, by combining QTL mapping, variant effect prediction, and complementation experiments, we recovered a novel allele RGA1-FH that explains most of the phenotypic changes. The RGA1-FH allele contains an A-to-T splicing site variant that disrupts the normal function of RGA1. While population analysis suggests extremely strong artificial selection in maintaining a functional allele of RGA1, our study is the first, to the best of our knowledge, to prove that a dysfunctional RGA1 allele can also be beneficial in real agricultural production. Future breeding programs would benefit from paying more attention to the rational utilization of those overlooked ‘unfavored’ alleles. Full article

As a result of climate change and global warming, plant diseases and pests are drawing attention because they are dispersing more quickly than ever before. The tomato leaf miner destroys the growth structure of the tomato, resulting in 80 to 100 percent tomato loss. Despite extensive efforts to prevent its spread, the tomato leaf miner can be found on most continents. To protect tomatoes from the tomato leaf miner, inspections must be performed on a regular basis throughout the tomato life cycle. To find a better deep neural network (DNN) approach for detecting tomato leaf miner, we investigated two DNN models for classification and segmentation. The same RGB images of tomato leaves captured from real-world agricultural sites were used to train the two DNN models. Precision, recall, and F1-score were used to compare the performance of two DNN models. In terms of diagnosing the tomato leaf miner, the DNN model for segmentation outperformed the DNN model for classification, with higher precision, recall, and F1-score values. Furthermore, there were no false negative cases in the prediction of the DNN model for segmentation, indicating that it is adequate for detecting plant diseases and pests. Full article

Due to their higher specific area and, in most cases, higher adsorption capacity, nanomaterials are noteworthy and attractive adsorbents. Agricultural products that are locally available are the best option for removing methylene blue (MB) dye from aqueous solutions. Because it is self-anionic, FT-IR and SEM investigations of biosorption have confirmed the role of the functional group and its contribution to the formation of pores that bind cationic dye. It is endothermic if the adsorption of MB by an adsorbent is high as the temperature increases; on the other hand, exothermic if it is high as the temperature decreases. A basic medium facilitates adsorption with respect to pH; adsorption is proportional to the initial concentration at a certain level before equilibrium; after equilibrium, adsorption decreases. A pseudo-second-order model applies for certain agricultural products. As per plotted graph for the solid-phase concentration against the liquid-phase concentration, the Langmuir adsorption isotherm model is favored; this model describes a situation in which a number of molecules are adsorbed by an equal number of available surface sites, and there is no interaction between adsorbate molecules once all sites are occupied. In contrast, the Freundlich model depicts non-ideal multi-layer sorption onto heterogeneous surfaces via numerical analysis; with a value of n = 1, the result is a linear isotherm. If the value of n < 1 or n > 1, then it is chemical or physical adsorption, respectively. Based on an EDX analysis, relevant elements are confirmed. BET analysis confirms the surface area. Nanoproducts categorized as agricultural products exhibit the aforementioned tendency. Even though nanoparticles show positive outcomes in terms of higher adsorption, a high specific area for the targeted pollutant is needed in real-world applications. In the relevant sections herein, the behavior of thermodynamic parameters, such as enthalpy, entropy, and Gibbs free energy, are examined. There is some question as to which form of agricultural waste is the most effective adsorption medium. There is no direct answer because every form of agricultural waste has its own distinct chemical and physical characteristics, such as porosity, surface area, and strength. Full article

The release of chemicals and microorganisms from various sources, such as industry, agriculture, animal farming, wastewater treatment plants, and flooding, into water systems have caused water pollution in several parts of our world, endangering aquatic ecosystems and individual health. World Health Organization (WHO) has introduced strict standards for the maximum concentration limits for nutrients and chemicals in drinking water, surface water, and groundwater. It is crucial to have rapid, sensitive, and reliable analytical detection systems to monitor the pollution level regularly and meet the standard limit. Electrochemical biosensors are advantageous analytical devices or tools that convert a bio-signal by biorecognition elements into a significant electrical response. Thanks to the micro/nano fabrication techniques, electrochemical biosensors for sensitive, continuous, and real-time detection have attracted increasing attention among researchers and users worldwide. These devices take advantage of easy operation, portability, and rapid response. They can also be miniaturized, have a long-life span and a quick response time, and possess high sensitivity and selectivity and can be considered as portable biosensing assays. They are of special importance due to their great advantages such as affordability, simplicity, portability, and ability to detect at on-site. This review paper is concerned with the basic concepts of electrochemical biosensors and their applications in various water quality monitoring, such as inorganic chemicals, nutrients, microorganisms’ pollution, and organic pollutants, especially for developing real-time/online detection systems. The basic concepts of electrochemical biosensors, different surface modification techniques, bio-recognition elements (BRE), detection methods, and specific real-time water quality monitoring applications are reviewed thoroughly in this article. Full article

In the present and projected context of an increasing worldwide demand for food, the intensification of climate change effects on agriculture, and the depletion and degradation of natural resources, global actions must be taken to assure future food security for all people. Improper practices along the food supply chain, from primary production to consumption, generate huge quantities of food waste. Building a circular bioeconomy that feeds recycled materials back into the economy and minimizes the loss of resources will be an important step in introducing the world’s food system to a sustainable path. The present case study describes an enclosed on-site composting system for food waste, operated in real-life conditions. The composting equipment was installed for a restaurant with specific needs in November 2020, located near a shopping center in Bucharest, the capital city of Romania. The physical, chemical, and biological characteristics of the compost came from a mix of food waste from a retail restaurant and sawdust pellets used as absorbent material, and these were analyzed to monitor compost quality and establish valorization opportunities. Two different monitoring campaigns were developed and the biological parameters were analyzed. The second monitoring campaign indicated that the compost was contaminated with Escherichia coli and Salmonella spp. When handled correctly and according to instructions, the composting process eliminates pathogens that may be present in food waste, such as Escherichia coli, Salmonella spp., etc., resulting in a high-quality compost that can be valorized in agriculture such as fertilizer or soil improver. Our results demonstrated that even when maintaining the same composition of raw materials in the composter, the quality and properties of the compost are greatly influenced by its operating conditions. Quality management procedures must be enforced and procedures must be strictly followed for the compost to be considered compliant. Compost that does not meet the requirements according to the regulation in force is again subjected to composting. If, after repeating the operation, the compost is still noncompliant, it is declared nonrecyclable waste, and must follow the specific procedure for such waste. Full article

The semantic web is an emerging technology that helps to connect different users to create their content and also facilitates the way of representing information in a manner that can be made understandable for computers. As the world is heading towards the fourth industrial revolution, the implicit utilization of artificial-intelligence-enabled semantic web technologies paves the way for many real-time application developments. The fundamental building blocks for the overwhelming utilization of semantic web technologies are ontologies, and it allows sharing as well as reusing the concepts in a standardized way so that the data gathered from heterogeneous sources receive a common nomenclature, and it paves the way for disambiguating the duplicates very easily. In this context, the right utilization of ontology capabilities would further strengthen its presence in many web-based applications such as e-learning, virtual communities, social media sites, healthcare, agriculture, etc. In this paper, we have given the comprehensive review of using the semantic web in the domain of healthcare, some virtual communities, and other information retrieval projects. As the role of semantic web is becoming pervasive in many domains, the demand for the semantic web in healthcare, virtual communities, and information retrieval has been gaining huge momentum in recent years. To obtain the correct sense of the meaning of the words or terms given in the textual content, it is deemed necessary to apply the right ontology to fix the ambiguity and shun any deviations that persist on the concepts. In this review paper, we have highlighted all the necessary information for a good understanding of the semantic web and its ontological frameworks. Full article

UAVs (Unmanned Aerial Vehicles) have been developed and adopted for various fields including military, IT, agriculture, construction, and so on. In particular, UAVs are being heavily used in the field of disaster relief thanks to the fact that UAVs are becoming smaller and more intelligent. Search for a person in a disaster site can be difficult if the mobile communication network is not available, and if the person is in the GPS shadow area. Recently, the search for survivors using unmanned aerial vehicles has been studied, but there are several problems as the search is mainly using images taken with cameras (including thermal imaging cameras). For example, it is difficult to distinguish a distressed person from a long distance especially in the presence of cover. Considering these challenges, we proposed an autonomous UAV smart search system that can complete their missions without interference in search and tracking of castaways even in disaster areas where communication with base stations is likely to be lost. To achieve this goal, we first make UAVs perform autonomous flight with locating and approaching the distressed people without the help of the ground control server (GCS). Second, to locate a survivor accurately, we developed a genetic-based localization algorithm by detecting changes in the signal strength between distress and drones inside the search system. Specifically, we modeled our target platform with a genetic algorithm and we re-defined the genetic algorithm customized to the disaster site’s environment for tracking accuracy. Finally, we verified the proposed search system in several real-world sites and found that it successfully located targets with autonomous flight. Full article

White lupin (Lupinus albus) represents an important legume crop in Europe and other parts of the world due to its high protein content and potential for low-input agriculture. However, most cultivars are susceptible to anthracnose caused by Colletotrichum lupini, a seed- and air-borne fungal pathogen that causes severe yield losses. The aim of this work was to develop a C. lupini-specific quantitative real-time TaqMan PCR assay that allows for quick and reliable detection and quantification of the pathogen in infected seed and plant material. Quantification of C. lupini DNA in dry seeds allowed us to distinguish infected and certified (non-infected) seed batches with DNA loads corresponding to the disease score index and yield of the mother plants. Additionally, C. lupini DNA could be detected in infected lupin shoots and close to the infection site, thereby allowing us to study the disease cycle of this hemibiotrophic pathogen. This qPCR assay provides a useful diagnostic tool to determine anthracnose infection levels of white lupin seeds and will facilitate the use of seed health assessments as a strategy to reduce the primary infection source and spread of this disease. Full article

Nitrogen (N) is one of the key nutrients supplied in agricultural production worldwide. Over-fertilization can have negative influences on the field and the regional level (e.g., agro-ecosystems). Remote sensing of the plant N of field crops presents a valuable tool for the monitoring of N flows in agro-ecosystems. Available data for validation of satellite-based remote sensing of N is scarce. Therefore, in this study, field spectrometer measurements were used to simulate data of the Sentinel-2 (S2) satellites developed for vegetation monitoring by the ESA. The prediction performance of normalized ratio indices (NRIs), random forest regression (RFR) and Gaussian processes regression (GPR) for plant-N-related traits was assessed on a diverse real-world dataset including multiple crops, field sites and years. The plant N traits included the mass-based N measure, N concentration in the biomass (N_conc), and an area-based N measure approximating the plant N uptake (NUP). Spectral indices such as normalized ratio indices (NRIs) performed well, but the RFR and GPR methods outperformed the NRIs. Key spectral bands for each trait were identified using the RFR variable importance measure and the Gaussian processes regression band analysis tool (GPR-BAT), highlighting the importance of the short-wave infrared (SWIR) region for estimation of plant N_conc—and to a lesser extent the NUP. The red edge (RE) region was also important. The GPR-BAT showed that five bands were sufficient for plant N trait and leaf area index (LAI) estimation and that a surplus of bands effectively reduced prediction performance. A global sensitivity analysis (GSA) was performed on all traits simultaneously, showing the dominance of the LAI in the mixed remote sensing signal. To delineate the plant-N-related traits from this signal, regional and/or national data collection campaigns producing large crop spectral libraries (CSL) are needed. An improved database will likely enable the mapping of N at the agro-ecosystem level or for use in precision farming by farmers in the future. Full article

The river Teign in Devon has come under scrutiny for failing to meet environmental quality standards for ecotoxic metals due to past mining operations. A disused mine known as Bridford Barytes mine, has been found to contribute a significant source of Zn, Cd and Pb to the river. Recently, studies have been focused on the remediation of such mine sites using low-cost treatment methods to help reduce metal loads to the river downstream. This paper explores the metal removal efficiency of red mud, a waste product from the aluminium industry, which has proven to be an attractive low-cost treatment method for adsorbing toxic metals. Adsorption kinetics and capacity experiments reveal metal removal efficiencies of up to 70% within the first 2 h when red mud is applied in pelletized form. Further, it highlights the potential of biochar, another effective adsorbent observed to remove >90% Zn using agricultural feedstock. Compliance of the Teign has been investigated by analysing dissolved metal concentrations and bioavailable fractions of Zn to assess if levels are of environmental concern. By applying a real-world application model, this study reveals that compressed pellets and agricultural biochar offer an effective, low-cost option to reducing metal concentrations and thus improving the quality of the river Teign. Full article