Search Results (51)

The global escalation of organic waste generation, coupled with rising protein demand and environmental pressure, necessitates innovative, circular approaches to resource management. Hermetia illucens (Black Soldier Fly, BSF) has emerged as a leading candidate for integrated waste-to-resource systems. This review examines BSF biological and genomic adaptations underpinning waste conversion efficiency, comparative performance of BSF bioconversion versus traditional treatments, nutritional and functional attributes, techno-economic, regulatory, and safety barriers to industrial scale-up. Peer-reviewed studies were screened for methodological rigor, and data on life cycle traits, conversion metrics, and product compositions were synthesized. BSF larvae achieve high waste reductions, feed-conversion efficiencies and redirect substrate carbon into biomass, yielding net CO₂ emissions as low as 12–17 kg CO₂ eq ton⁻¹, an order of magnitude below composting or vermicomposting. Larval biomass offers protein, lipids (notably lauric acid), micronutrients, chitin, and antimicrobial peptides, with frass serving as a nutrient-rich fertilizer. Pathogen and antibiotic resistance gene loads decrease during bioconversion. Key constraints include substrate heterogeneity, heavy metal accumulation, fragmented regulatory landscapes, and high energy and capital demands. BSF systems demonstrate superior environmental and nutritional performance compared to conventional waste treatments. Harmonized safety standards, feedstock pretreatment, automation, and green extraction methods are critical to overcoming scale-up barriers. Interdisciplinary innovation and policy alignment will enable BSF platforms to realize their full potential within circular bio-economies. Full article

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article

Quantitative greenhouse gas (GHG) budgets for Mediterranean pepper cultivation are still missing, limiting evidence-based nitrogen management. Furthermore, mitigation value of fertigation respect to granular fertilization in vegetable systems remains uncertain. This study therefore compared the GHG footprint and productivity of ‘papaccella’ pepper under two nitrogen fertilization methods: granular fertilization versus low-frequency fertigation with urea, each supplying about 63 kg N ha⁻¹. Eight automated static chambers coupled to a cavity ring-down spectrometer monitored soil CO₂ and N₂O fluxes throughout the season. Cumulative emissions did not differ between treatments (CO₂: 811 ± 6 g m⁻² vs. 881 ± 4 g m⁻²; N₂O: 0.038 ± 0.008 g m⁻² vs. 0.041 ± 0.015 g m⁻², fertigation vs. granular), and marketable yield remained at ~11 t ha⁻¹, leaving product-scaled global warming potential (GWP) unchanged. Although representing less than 2% of measured fluxes, “hot moments,” burst emissions exceeding four standard deviations (SD) from the mean, accounted for up to 4% of seasonal CO₂ and 19% of N₂O. Fertigation doubled the frequency of these events but reduced their peak magnitude, whereas granular application produced fewer but more extreme bursts (>11 SD). Results showed that fertigation did not mitigate GHGs emission nor improve productivity for Mediterranean pepper, mainly due to the low application frequency and the use of a urea fertilizer. Moreover, we can highlight that in horticultural systems, omitting ‘hot moments’ leads to systematic underestimation of emissions. Full article

Automation in agricultural machinery, underpinned by the integration of advanced technologies, is revolutionizing sustainable farming practices. Key enabling technologies include multi-source positioning fusion (e.g., RTK-GNSS/LiDAR), intelligent perception systems utilizing multispectral imaging and deep learning algorithms, adaptive control through modular robotic systems and bio-inspired algorithms, and AI-driven data analytics for resource optimization. These technological advancements manifest in significant applications: autonomous field machinery achieving lateral navigation errors below 6 cm, UAVs enabling targeted agrochemical application, reducing pesticide usage by 40%, and smart greenhouses regulating microclimates with ±0.1 °C precision. Collectively, these innovations enhance productivity, optimize resource utilization (water, fertilizers, energy), and mitigate critical labor shortages. However, persistent challenges include technological heterogeneity across diverse agricultural environments, high implementation costs, limitations in adaptability to dynamic field conditions, and adoption barriers, particularly in developing regions. Future progress necessitates prioritizing the development of lightweight edge computing solutions, multi-energy complementary systems (integrating solar, wind, hydropower), distributed collaborative control frameworks, and AI-optimized swarm operations. To democratize these technologies globally, this review synthesizes the evolution of technology and interdisciplinary synergies, concluding with prioritized strategies for advancing agricultural intelligence to align with the Sustainable Development Goals (SDGs) for zero hunger and responsible production. Full article

Inconsistencies in product weight during fertilizer bagging can lead to material losses and reduced operational efficiency. This study investigates the use of machine learning to predict weight deviations in the Urea Bagging Unit at PT Petrokimia Gresik. Four algorithms were used: an Artificial Neural Network (ANN), Random Forest Regression (RFR), Linear Regression (LR), and Support Vector Regression (SVR). The dataset used consisted of nine numeric sensor variables. Among the models, RFR achieved the highest predictive accuracy (R² = 0.9638, RMSE = 0.0496, MAE = 0.0338). Feature importance analysis identified the clamping time and air pressure as the most influential variables. A Smart Bagging System was developed using the RFR model, integrating real-time monitoring and automated parameter adjustment. The simulation results show that the system can reduce overweight losses by up to 95%, with potential annual savings of approximately IDR 29 billion. While promising, these results are based on controlled conditions and a limited dataset; further field validation is recommended. The proposed system demonstrates the potential of machine learning to support cost-efficient, real-time process control in industrial bagging operations. This work aligns with SDG 9 and SDG 12 by promoting industrial innovation and reducing resource waste. Full article

The integration of smart sensor networks and Internet of Things (IoT) technologies has emerged as a key strategy for enhancing productivity and sustainability in agriculture and aquaculture under increasing climate and resource pressures. This review consolidates empirical findings on the performance of sensor-driven systems in optimizing the management of water, nutrients, and energy. Studies have demonstrated that IoT-based irrigation systems can reduce water use by up to 50% without compromising yields, while precision nutrient monitoring enables a 20–40% reduction in fertilizer inputs. In aquaculture, real-time monitoring and automated interventions have improved feed conversion ratios, reduced mortality by up to 40%, and increased yields by 15–50%. The integration of artificial intelligence (AI) into IoT frameworks further enhances predictive capabilities and operational responsiveness. Despite these benefits, widespread adoption remains constrained by high infrastructure costs, limited sensor robustness, and fragmented policy support. This paper provides a comprehensive evaluation of current technologies, adoption barriers, and strategic directions for advancing scalable, sustainable, and data-driven food production systems. Full article

In order to sustain food production under conditions of limited water and in arid regions using the least amount of irrigation water possible, two experiments were conducted during the years 2021 and 2022 in the Nubaria region, Egypt. The performance of an automated drip irrigation control system was evaluated as a potentially efficient and sustainable alternative to manual irrigation to increase the fertilization frequency (N P K) of lemon trees. This study underlines the importance of automatically applying and controlling the addition of irrigation water as a sustainable alternative to manual irrigation, while increasing the number of mineral fertilization times under sandy soil conditions to the largest possible number (12 times during the growing season of lemon trees) instead of three times. The application of automatic irrigation reduced the water stress on the roots of the lemon trees, in addition to increasing the efficiency of the addition. The latter led to the creation of a healthy environment in the area where the roots spread and increased the rate of absorption of irrigation water loaded with the necessary major elements, thus increasing the canopy volume of the lemon trees. This, in turn, led to an improvement in the efficiency of the photosynthesis process, resulting in an increase in the productivity, water productivity, and quality characteristics of lemon in sandy soil in dry areas. Increasing the number of times of mineral fertilization to 12 during the growing season led to a long-term increase in the concentrations of those minerals within the area of root spread, avoiding losing them by deep percolation, as occurs fertilization is carried out only three times per season. The highest values of the productivity and irrigation water saving were 47.6% and 47.4%, respectively, during the first season and 48.7% and 48.8%, respectively, during the second season. The highest values of water productivity and lemon fruit quality were also achieved under the same conditions. Therefore, this study recommends the automatic control of irrigation schedules, in addition to increasing the frequency of fertilization times, not only in lemon plantations, but also with most horticultural fruit trees grown in dry sandy lands. Full article

Smart agriculture is transforming traditional farming by integrating advanced sensor-based systems, intelligent control technologies, and sustainable energy solutions to meet the growing global demand for food while reducing environmental impact. This review presents a comprehensive analysis of recent innovations in smart agriculture, focusing on the deployment of IoT-based sensors, wireless communication protocols, energy-harvesting methods, and automated irrigation and fertilization systems. Furthermore, the paper explores the role of artificial intelligence (AI), machine learning (ML), computer vision, and big data analytics in monitoring and managing key agricultural parameters such as crop health, pest and disease detection, soil conditions, and water usage. Special attention is given to decision-support systems, precision agriculture techniques, and the application of remote and proximal sensing technologies like hyperspectral imaging, thermal imaging, and NDVI-based indices. By evaluating the benefits, limitations, and emerging trends of these technologies, this review aims to provide insights into how smart agriculture can enhance productivity, resource efficiency, and sustainability in modern farming systems. The findings serve as a valuable reference for researchers, practitioners, and policymakers working towards sustainable agricultural innovation. Full article

Corn (Zea mays) production systems are described as high risk for emissions of greenhouse gases (GHG) due to large fertilizer inputs. Conservation practices, such as cover crop (CC), can limit the effects of agricultural activities on GHGs while increasing carbon and nitrogen storage. The objective of the study was to assess the effects of cover crops, i.e., with CC and no-CC, on GHG (i.e., CO₂, CH₄, and N₂O) emissions and global warming potential (GWP) in furrow-irrigated corn in the Lower Mississippi River Valley. Gas sampling was conducted with an automated system that measured GHGs four times daily during the 2024 growing season in furrow-irrigated corn on a loam soil in Mississippi. Only CO₂ emissions differed (p < 0.05) by CC treatment, with soil respiration ~30% greater from CC than no-CC. Season-long emissions ranged from −0.22 to 0.30 kg CH₄ ha⁻¹ season⁻¹, 5.53 to 7.28 kg N₂O ha⁻¹ season⁻¹, with a GWP between 12,888 and 15,053 kg CO₂ eq. ha⁻¹ season⁻¹ from no-CC and CC, respectively. The role of CC as a conservation practice needs to be evaluated with soil and plant parameters. The beneficial effects of CC on soil physical and chemical properties likely outweigh a predictable increase in GHG emissions. Full article

In agriculture, manuring offers several benefits, which include improving soil fertility, structure, water retention, and aeration; all these factors favor plant health and productivity. However, improper handling and application of manure can pose risks, such as spread of pathogens and water pollution. Mitigation of such risks requires not only proper storage and composting practices, but also compliance with correct application periods and techniques. Spaceborne Earth observation can contribute to mapping manure applications and identifying possible critical situations, yet manure detection from satellite data is still a largely open question. The aim of this research is an automated, machine learning (ML)-based approach to detecting manure application on crop fields in time sequences of spaceborne, multi-source optical Earth Observation data. In the first stage of this research, multispectral data alone was considered; a pool of different spectral indexes were analyzed to identify the ones most impacted by manure application. Increments of the selected indexes from one satellite acquisition to the next were used as features to train and test various machine learning models. Two agricultural areas—one in Spain and one in Italy—were considered. Fair levels of accuracy were achieved when training and testing were carried out in the same geographical context, whereas ML models trained on one context and tested on the other reported significantly lower—albeit still acceptable—accuracy levels. In the stage that followed, thermal data was integrated and used alongside multispectral indexes. This addition led to significant improvements in accuracy levels, despite possible thermal-to-multispectral sampling mismatch in time series. Our results appear to indicate that ML-based approaches to manuring detection from space require training on the targeted geographical context, although transfer learning can probably be leveraged and only fine-tuning training will be needed. Spaceborne thermal data, where available, should be included in the input data pool to improve the quality of the final result. The proposed method is meant as a first step towards a suite of techniques that should enable large-scale, consistent monitoring of agricultural activities to check compliance with environmental regulations and provide enhanced traceability information for food products. Full article

This article explores contemporary scientific approaches to improving the efficiency of agricultural operations in Ukraine. It has been identified that insufficient attention has been given to optimizing the activities of agricultural enterprises. A model for optimizing crop areas, considering crop rotations in crop production or mixed-type enterprises, has been developed to ensure an increase in crop yields. The model incorporates factors such as soil health, pest management, and the economic feasibility of different cropping systems. By applying crop rotation principles, the model aims to achieve a balanced and sustainable agricultural practice, promoting both productivity and environmental sustainability. The findings highlight the importance of considering ecological factors and economic optimization in agricultural planning. The model demonstrates how the rotation of crops can prevent soil depletion and improve overall land use efficiency, thereby boosting the agricultural output of enterprises. The proposed approach is distinguished by its uniqueness, as it leverages advanced economic–mathematical methodologies and state-of-the-art information–analytical tools to enable the automation of the crop rotation planning process. The implementation of this approach can lead to more sustainable farming practices, enhanced soil fertility, and increased profitability. Full article

Sustainable agriculture faces major issues with resource efficiency, nutrient distribution, and plant health. Traditional soil-based and soilless farming systems encounter issues including excessive water use, insufficient nutrient uptake, nitrogen deficiency, and restricted plant development. According to the previous literature, aeroponic systems accelerate plant growth rates, improve root oxygenation, and significantly enhance water use efficiency, particularly when paired with both low- and high-pressure misting systems. However, despite these advantages, they also present certain challenges. A major drawback is the inefficiency of nitrogen fixation, resulting in insufficient nutrient availability and heightened plant stress from uncontrolled misting, which ultimately reduces yield. Many studies have investigated plasma uses in both soil-based and soilless plant cultures; nevertheless, however, its function in aeroponics remains unexplored. Therefore, the present work aims to thoroughly investigate and review the integration of plasma-activated water (PAW) and plasma-activated mist (PAM) in aeroponics systems to solve important problems. A review of the current literature discloses that PAW and PAM expand nitrogen fixation, promote nutrient efficiency, and modulate microbial populations, resulting in elevated crop yields and enhanced plant health, akin to soil-based and other soilless systems. Reactive oxygen and nitrogen species (RONS) produced by plasma treatments improve nutrient bioavailability, root development, and microbial equilibrium, alleviating critical challenges in aeroponics, especially within fine-mist settings. This review further examines artificial intelligence (AI) and the Internet of Things (IoT) in aeroponics. Models driven by AI enable the accurate regulation of fertilizer concentrations, misting cycles, temperature, and humidity, as well as real-time monitoring and predictive analytics. IoT-enabled smart farming systems employ sensors for continuous nutrient monitoring and gas detection (e.g., NO₂, O₃, NH₃), providing automated modifications to enhance aeroponic efficiency. Based on a brief review of the current literature, this study concludes that the future integration of plasma technology with AI and IoT may address the limitations of aeroponics. The integration of plasma technology with intelligent misting and data-driven control systems can enhance aeroponic systems for sustainable and efficient agricultural production. This research supports the existing body of research that advocates for plasma-based innovations and intelligent agricultural solutions in precision farming. Full article

Robotic technologies are affording opportunities to revolutionize the production of specialty crops (fruits, vegetables, tree nuts, and horticulture). They offer the potential to automate tasks and save inputs such as labor, fertilizer, and pesticides. Specialty crops are well known for their high economic value and nutritional benefits, making their production particularly impactful. While previous review papers have discussed the evolution of agricultural robots in a general agricultural context, this review uniquely focuses on their application to specialty crops, a rapidly expanding area. Therefore, we aimed to develop a state-of-the-art review to scientifically contribute to the understanding of the following: (i) the primary areas of robots’ application for specialty crops; (ii) the specific benefits they offer; (iii) their current limitations; and (iv) opportunities for future investigation. We formulated a comprehensive search strategy, leveraging Scopus^® and Web of Science™ as databases and selecting “robot” and “specialty crops” as the main keywords. To follow a critical screening process, only peer-reviewed research papers were considered, resulting in the inclusion of 907 papers covering the period from 1988 to 2024. Each paper was thoroughly evaluated based on its title, abstract, keywords, methods, conclusions, and declarations. Our analysis revealed that interest in agricultural robots for specialty crops has significantly increased over the past decade, mainly driven by technological advancements in computer vision and recognition systems. Harvesting robots have arisen as the primary focus. Robots for spraying, pruning, weed control, pollination, transplanting, and fertilizing are emerging subjects to be addressed in further research and development (R&D) strategies. Ultimately, our findings serve to reveal the dynamics of agricultural robots in the world of specialty crops while supporting suitable practices for more sustainable and resilient agriculture, indicating a new era of innovation and efficiency in agriculture. Full article

Soil fertility is declining in low-input agriculture due to insufficient fertilizer application by small-scale farmers. On the other hand, concerns are rising regarding the environmental pollution of both air and water in high-input agriculture due to the excessive use of N fertilizers in short growing seasons for vegetable crops, which is directly linked to the health of human beings and environmental safety. This study aimed to determine genotypic differences in the Nitrogen Use Efficiency (NUE) levels of different leafy vegetable species (Arugula, Spinach, Cress, Parsley, and Dill) grown hydroponically under two different N rates, low N (0.3 mM) and high N (3.0 mM), and to identify the plant traits that are contributing to NUE. A nutrient solution experiment was conducted between March and April 2024 by using an aerated Deep-Water Culture (DWC) technique in a fully automated climate room with a completely randomized block design (CRBD) with three replications for five weeks. The results indicated that shoot growth, as well as root morphological and leaf physiological responses, was significantly (p < 0.001) affected by genotype, the N rate, and genotype–N rate interactions. Shoot growth in some vegetable species (Arugula, Spinach, and Cress) was significantly higher under a low N than a high N rate, illustrating that they have a great capability for NUE under low N stress conditions. Similar results were also recorded for the root growth of the N-efficient species under low N rates. The NUE levels of these species were closely associated with leaf physiological (leaf area, leaf chlorophyll index (SPAD), photosynthesis, and total leaf chlorophyll (a + b) and carotenoids) and root morphological (root length, root volume, and average root diameter) characteristics. These plant traits could be useful indicators for the selection and breeding of ‘N-efficient’ leafy vegetable species for sustainable low-input agriculture systems in the future. However, further investigation should be carried out at the field level to confirm their commercial production viability. Full article

Controlled environment agriculture is a promising alternative to conventional production methods, as it is less affected by climate change and is often more sustainable, especially in circular and recycling frameworks such as aquaponics. A major cost factor in such facilities, however, is the need for skilled labor. Depending on available resources, there are endless possibilities on how to choose ingredients to realize a desired nutrient solution. At the same time, the composition of the desired solution is subject to fluctuations in fish water quality, fertilizer availability, weather, and plant development. In high-evaporation scenarios, e.g., summer, nutrient solutions might be mixed multiple times per day. This results in a complex, multi-variable task that is time-consuming to solve manually, yet requires frequent resolution. This work aims to help solve this challenge by providing methods to automate the nutrient mixing procedure. A simple mass-balance-based model of a nutrient mixing tank with connections to different water sources, drains, and fertilizers is provided. Using methods of static optimization, a program was developed which, in consideration of various process constraints and optimization variables, is able to calculate the necessary steps to mix the desired solution. The program code is provided in an open-source repository. The flexibility of the method is demonstrated in simulation scenarios. The program is easy to use and to adapt, and all necessary steps are explained in this paper. This work contributes to a higher automation level in CEA. Full article