Search Results (67)

According to United Nations projections, the global population is expected to reach 9.7 billion by 2050, with 70% residing in urban areas, while arable land availability continues to decline. Vertical farming (VF) offers a promising pathway for sustainable urban food production by utilizing vertical space and controlled environments. Among emerging approaches, the adaptive vertical farm (AVF) introduces movable shelving systems that adjust to plant growth stages, allowing a higher number of cultivation shelves to be accommodated within the same rack height. In this study, we developed a computational model to quantify and compare the energy consumption of AVF and conventional VF systems under industrial-scale conditions. The reference scenario considered 272 multilevel racks, each hosting 8 shelves in the VF and 15 shelves in the AVF, with Lactuca sativa as the test crop. Energy consumption for thermohygrometric control and lighting was estimated under different sowing schedules, with crop growth dynamics simulated using scheduling algorithms. Plant heat loads were calculated through the Penman–Monteith model, enabling a robust estimation of evapotranspiration and its impact on indoor climate control. Simulation results show that the AVF achieves an average 22% reduction in specific energy consumption for climate control compared to the VF, independently of sowing strategies. Moreover, the AVF nearly doubles the number of cultivation shelves within the same footprint, increasing the cultivable surface area by over 400% compared to traditional flat indoor systems. This work provides the first quantitative assessment of AVF energy performance, demonstrating its potential to simultaneously improve land-use efficiency and reduce energy intensity, thereby supporting the sustainable integration of vertical farming in urban food systems. Full article

Indoor horticulture requires a substantial quantity of electricity to meet crops extended photoperiodic requirements for optimal photosynthetic rate. Simultaneously, global electricity costs have grown dramatically in recent years, endangering the sustainability and profitability of indoor vertical farms and/or modern greenhouses that use artificial lighting systems to accelerate crop development and growth. This study investigates the growth rate and physiological development of cherry tomato plants cultivated in a pilot indoor vertical farm at the Agricultural University of Athens’ Laboratory of Farm Structures (AUA) under continuous and disruptive lighting. The leaf physiological traits from multiple photoperiodic stress treatments were analyzed and utilized to estimate the plant’s tolerance rate under varied illumination conditions. Four different photoperiodic treatments were examined and compared, firstly plants grew under 14 h of continuous light (C-14L10D/control), secondly plants grew under a normalized photoperiod of 14 h with intermittent light intervals of 10 min of light followed by 50 min of dark (NI-14L10D/stress), the third treatment where plants grew under 14 h of a load-shifted energy demand response intermittent lighting schedule (LSI-14L10D/stress) and finally plants grew under 13 h photoperiod following of a load-shifted energy demand response intermittent lighting schedule (LSI-13L11D/stress). Plants were subjected also under two different light spectra for all the treatments, specifically WHITE and Blue/Red/Far-red light composition. The aim was to develop flexible, energy-efficient lighting protocols that maintain crop productivity while reducing electricity consumption in indoor settings. Results indicated that short periods of disruptive light did not negatively impact physiological responses, and plants exhibited tolerance to abiotic stress induced by intermittent lighting. Post-harvest data indicated that intermittent lighting regimes maintained or enhanced growth compared to continuous lighting, with spectral composition further influencing productivity. Plants under LSI-14L10D and B/R/FR spectra produced up to 93 g fresh fruit per plant and 30.4 g dry mass, while consuming up to 16 kWh less energy than continuous lighting—highlighting the potential of flexible lighting strategies for improved energy-use efficiency. Full article

Early crop yield prediction is a major challenge in precision agriculture, and efficient and rapid yield prediction is highly important for sustainable fruit production. The accurate detection of major fruit characteristics, including flowering, green fruiting, and ripening stages, is crucial for early yield estimation. Currently, most crop yield estimation studies based on the YOLO model are only conducted during a single stage of maturity. Combining multi-growth period data for crop analysis is of great significance for crop growth detection and early yield estimation. In this study, a new network model, YOLOv8-RL, was proposed using citrus multigrowth period characteristics as a data source. A citrus yield estimation model was constructed and validated by combining network identification counts with manual field counts. Compared with YOLOv8, the number of parameters of the improved network is reduced by 50.7%, the number of floating-point operations is decreased by 49.4%, and the size of the model is only 3.2 MB. In the test set, the average recognition rate of citrus flowers, green fruits, and orange fruits was 95.6%, the mAP@.5 was 94.6%, the FPS value was 123.1, and the inference time was only 2.3 milliseconds. This provides a reference for the design of lightweight networks and offers the possibility of deployment on embedded devices with limited computational resources. The two estimation models constructed on the basis of the new network had coefficients of determination R² values of 0.91992 and 0.95639, respectively, with a prediction error rate of 6.96% for citrus green fruits and an average error rate of 3.71% for orange fruits. Compared with network counting, the yield estimation model had a low error rate and high accuracy, which provided a theoretical basis and technical support for the early prediction of fruit yield in complex environments. Full article

Reliable early-stage yield forecasts are essential in precision viticulture, enabling timely interventions such as harvest planning, canopy management, and crop load regulation. Since grape yield is directly related to the number and size of bunches, the early detection of inflorescences and bunches, carried out even before flowering, provides a valuable foundation for estimating potential yield far in advance of veraison. Traditional yield prediction methods are labor-intensive, subjective, and often restricted to advanced phenological stages. This study presents a deep learning-based approach for detecting grapevine inflorescences and bunches during early development, assessing how phenological stage and illumination conditions influence detection performance using the YOLOv11 architecture under commercial field conditions. A total of 436 RGB images were collected across two phenological stages (pre-bloom and fruit-set), two lighting conditions (daylight and artificial night-time illumination), and six grapevine cultivars. All images were manually annotated following a consistent protocol, and models were trained using data augmentation to improve generalization. Five models were developed: four specific to each condition and one combining all scenarios. The results show that the fruit-set stage under daylight provided the best performance (F1 = 0.77, R² = 0.97), while for inflorescences, night-time imaging yielded the most accurate results (F1 = 0.71, R² = 0.76), confirming the benefits of artificial lighting in early stages. These findings define optimal scenarios for early-stage organ detection and support the integration of automated detection models into vineyard management systems. Future work will address scalability and robustness under diverse conditions. Full article

Compost teas (CTs) can be considered natural microbial consortia, able to enhance biostimulation and defense in crops. This study focuses on two plant-derived CTs and their potential use as eco-friendly biofertilizers for chickpeas and peas, with the broader aim to protect soil fertility. Our experiments demonstrated that the two CTs have biostimulatory or inhibitory effects depending on dilution, target plant species, CT microbial load and metabolism, and age of CT preparation. Peas exhibited positive responses to treatments, while chickpeas could be negatively affected depending on CT concentration. The CT microbial load positively affected biostimulation for both plant species. The metabolic profiles of the CT-associated microbial communities were evaluated using the Biolog EcoPlate™ system. Spearman’s correlation analysis allowed us to ascertain a positive interaction between root elongation and the microbial consumption of specific substrates, namely polymers, erythritol, and L-serine. On the contrary, phenolic compound consumption showed a negative correlation. In chickpeas, root and collar necrosis, estimated with the McKinney index, increased after treatment with CTs at the highest concentration, confirming a phytotoxic effect; but diagnostic analyses demonstrated that the necrosis was also partially attributed to pathogenic Fusarium spp. On the other hand, proper dilutions of treatments determined a decrease in necrosis severity, indicating putative CT biocontrol properties. Full article

The increasing variability of climatic conditions poses significant challenges for agricultural and livestock systems worldwide. In regions with warm climates, such as northern Colombia, the effects of changing temperature, precipitation, and evapotranspiration are particularly pronounced, influencing the productivity and sustainability of native pastures. To address these challenges, modeling tools provide a valuable means of understanding and predicting forage production dynamics under diverse climatic scenarios, enabling farmers to make informed decisions that enhance resilience and sustainability. This research was conducted in Córdoba, Colombia, with the objective of evaluating the impact of climatic variations in temperature, precipitation, and evapotranspiration on forage production in native pastures in hot climates in northern Colombia. Modeling tools were used to assess the potential yield of pastures based on climate conditions, enabling the understanding and addressing of challenges associated with climatic fluctuations in estimated production. To plan animal grazing, climate variability from 2018 to 2021, a period influenced by the El Niño–Southern Oscillation (ENSO) phenomenon, was analyzed. This type of integrated analysis, which combines meteorological data, soil, crops, and evaluation of animal load per unit area, is an ideal and practical approach to addressing productivity challenges associated with climatic variability in livestock production in the warm climate of Colombia. The results confirmed the significant impact of climatic conditions on forage production, leading to the conclusion that simulation tools for water use in Bothriochloa “Colosuana” pastures are relevant for efficient water resource management, particularly during the dry season and drought events. This allows for anticipating the impacts of climate change on agriculture and livestock, facilitating timely and sustainable decision-making by farmers. Full article

Remote sensing (RS) spectral time series provide a substantial source of information for the regular and cost-efficient monitoring of the Earth’s surface. Important monitoring tasks include land use and land cover classification, change detection, forest monitoring and crop type identification, among others. To develop accurate solutions for RS-based applications, often supervised shallow/deep learning algorithms are used. However, such approaches usually require fixed-length inputs and large labeled datasets. Unfortunately, RS images acquired by optical sensors are frequently degraded by aerosol contamination, clouds and cloud shadows, resulting in missing observations and irregular observation patterns. To address these issues, efforts have been made to implement frameworks that generate meaningful representations from the irregularly sampled data streams and alleviate the deficiencies of the data sources and supervised algorithms. Here, we propose a conceptually and computationally simple representation learning (RL) approach based on autoencoders (AEs) to generate discriminative features for crop type classification. The proposed methodology includes a set of single-layer AEs with a very limited number of neurons, each one trained with the mono-temporal spectral features of a small set of samples belonging to a class, resulting in a model capable of processing very large areas in a short computational time. Importantly, the developed approach remains flexible with respect to the availability of clear temporal observations. The signal derived from the ensemble of AEs is the reconstruction difference vector between input samples and their corresponding estimations, which are averaged over all cloud-/shadow-free temporal observations of a pixel location. This averaged reconstruction difference vector is the base for the representations and the subsequent classification. Experimental results show that the proposed extremely light-weight architecture indeed generates separable features for competitive performances in crop type classification, as distance metrics scores achieved with the derived representations significantly outperform those obtained with the initial data. Conventional classification models were trained and tested with representations generated from a widely used Sentinel-2 multi-spectral multi-temporal dataset, BreizhCrops. Our method achieved $77.06\%$ overall accuracy, which is $\sim 6\%$ higher than that achieved using original Sentinel-2 data within conventional classifiers and even $\sim 4\%$ better than complex deep models such as OmnisCNN. Compared to extremely complex and time-consuming models such as Transformer and long short-term memory (LSTM), only a $3\%$ reduction in overall accuracy was noted. Our method uses only $6.8k$ parameters, i.e., $\sim 400x$ fewer than OmnicsCNN and $\sim 27x$ fewer than Transformer. The results prove that our method is competitive in terms of classification performance compared with state-of-the-art methods while substantially reducing the computational load. Full article

Tropical rainforests are of vital importance to the environment, as they contribute to weather patterns, biodiversity and even human wellbeing. Hence, in the face of tropical deforestation, it becomes exigent to quantify and assess the contribution of ecosystem services associated with tropical rainforests to the environment and especially to the people. This study adopted a nuanced approach, different from traditional economic valuations, to estimate the water-related ecosystem services (WRESs) received by the people from 2010 to 2020 in the Hainan Tropical Rainforest National Park (HTRNP). The study focused on water yield, soil conservation, and water purification using InVEST, the SCS-CN_GIS model, and spatial analysis. The results show (1) significant land cover changes within the HTRNP, as forest decreased by 4433 ha and water bodies increased by 4047 ha, indicating the active presence of human activities. However, land cover changes were more pronounced within the 5 km buffer area around the HTRNP, suggesting the effectiveness of the tropical rainforest conservation efforts in place. (2) The water yield of the HTRNP in the years studied decreased by 307.03 km³, based on the water yields in 2010 and 2020, which were 5625.7 km³ and 5318.7 km³, respectively. (3) Change detection showed that runoff mitigation in the rainforest has a negative mean (−0.21), indicating a slight overall decrease in soil conservation and runoff mitigation in the rainforest from 2010 to 2020; however, the higher curve number indicates areas susceptible to surface runoff. (4) The ecological effectiveness of water purification to absorb and reduce nitrogen load was better in 2020 (145,529 kg/year), as it was reduced from 506,739 kg/year in 2010, indicating improved water quality. (5) Population growth is more pronounced in areas with high water yields. Overall, the proposed framework has shown that the water yield potential of the HTRNP can meet the water consumption demands of people and industries situated within the buffer area. However, analysis of the study shows that it does not meet the crop water requirements. This study provides insights for decision makers in identifying potential beneficiaries and the essence of effective area-based conservation measures, and the proposed framework can be applied to any area of interest, offering a different approach in ecosystem services assessment. Full article

Soil nutrient availability in meadows has been poorly studied from the management point of view, despite its great impact. In this study, three different types of meadows have been analysed, as follows: intensive meadows, with high livestock load and inorganic fertilization; semi-extensive meadows, with medium livestock load and organic fertilization; and extensive meadows, with low livestock load and low fertilization rates. We looked at the nitrogen, phosphorus, potassium and carbon balances of each meadow type during two different years. Nitrogen was more stable in semi-extensive and extensive meadows, due to its organic form. In contrast, intensive meadows showed higher nitrogen variability depending on climate. Phosphorus is seen as the limiting nutrient, and it accumulates less in the soil than what is estimated in the crop balance, being more balanced in extensive meadows. Potassium has a strong response to temperature, being more available in June than in February, but crop balance was always negative for extensive meadows, and its soil concentration decreases each year, which could cause long-term potassium deficiency. Carbon accumulation was more stable in extensive meadows, where there was accumulation regardless of the year, whereas intensive and semi-extensive meadows become carbon emitters during the drought year. Full article

As a crucial step in food production, tillage and land preparation play a pivotal role in achieving sustainable crop production and improving the soil environment. However, accurate assessment of the load that agricultural machinery implements during the operation process has always been a vexing problem that needs urgent solutions. In this paper, an extrapolation and reconstruction framework for the time-domain load is constructed based on the probability-weighted moments (PWM) estimation and the peaks-over-threshold function, and the load spectrum is obtained for agriculture general power machinery. Firstly, the load acquisition system was developed, the traction resistance and output torque of the tractor were measured, and the collected load signals were preprocessed. Next, the mean excess function and PWM estimation are introduced to select the optimal threshold and generalized Pareto distribution (GPD) fitting parameters and the extreme load distribution that exceeds the threshold range is fitted. The extreme points in the original data are replaced by generating new extreme points that follow the GPD distribution, and the extrapolation of the load spectrum is achieved. Finally, the real extrapolated load spectrum was validated based on statistical characteristics and rainflow counting analysis, and the correlation coefficient between the fitting data and the extreme load samples was greater than 0.99. It can retain the load sequence characteristics of the original load to a great extent, truly reflecting the load state during the operation of agricultural machinery. Meanwhile, the characteristics of the load spectrum can be accurately obtained, such as extreme, mean, and amplitude values, and the real load during deep loosening and rotary tillage are accurately described. The values provide more authentic and reliable data support for the subsequent selection of optimal operating parameters, reliability design of the power transmission system, and the life assessment of the agricultural implements. Full article

The ecologically fragile agro-pastoral ecotone in northern China is characterized by relatively poor arable land quality. Yunzhou District in Datong City, which is situated within this transitional zone, boasts over 600 years of Hemerocallis citrina Baroni cultivation. Exploring the effects of soil physicochemical properties on daylily yield and related agronomic traits is essential for enhancing the ecological and economic value of dominant crops in ecologically fragile areas. Therefore, in this study, we focused on the daylily, a characteristic cash crop that is grown in the agro-pastoral ecotone in Yunzhou District. Physicochemical property measurement and yield estimation were performed using soil samples collected from 37 sites, with Spearman’s correlation analysis, one-way analysis of variance with multiple comparisons, path analysis, and stepwise regression analysis used to analyze the generated data. The results showed the following: (1) The pathway analysis of daylily yield with each agronomic trait showed that the BN and PH directly affected the yield of daylily with direct pathway coefficients of 0.844 and 0.7, respectively, whereas the SN indirectly affected the yield of daylily through the BD and PH, with indirect pathway coefficients of 0.827 and 0.566, respectively. (2) A total of four principal components were extracted for the soil factors, of which SMC, ST and BD had large loadings on PC1; OM, TN and pH had large loadings on PC2; AK had large loadings on PC3; and AP had large loadings on PC4. (3) From the principal component regression and stepwise regression, it can be seen that SMC is the most critical factor affecting the yield of daylily, as well as the related agronomic traits, and the results also show that yield prediction was affected by OM, ST, and AK, while BN was influenced by OM and ST, and SN and PH were influenced by AP. Comparing the goodness of fit and significance of the two models, it can be concluded that the stepwise regression model is the optimal model for this study. Full article

Implementing best management practices (BMPs) has proven to be an efficient method for reducing non-point source (NPS) pollutants. Agricultural NPS pollution is considered to be a major contributor to water quality impairment. This study aims to assess the variation in hydrologic and water quality outputs at field and watershed scales when BMPs are implemented using modeling approaches. The Yazoo River Watershed (YRW) is the largest watershed basin in the state of Mississippi with approximately 50% agricultural land. Runoff generated from agricultural areas carries sediments and nutrients. The Merigold watershed (MW) is a sub-basin of the YRW and a field-scale watershed with most of the land use being agriculture. It is essential to quantify the streamflow, sediment, total nitrogen (TN), and total phosphorus (TP) when BMPs are implemented. BMPs such as vegetative filter strips (VFS) and cover crops (CC) were tested in this study. The Soil and Water Assessment Tool (SWAT) model was applied to quantify the watershed’s hydrologic and water quality outputs. SWAT model accuracy assessment was performed by calibration and validation process using the Nash and Sutcliffe Efficiency Index (NSE). Model performance was satisfactory for monthly streamflow, with NSE values in the range of 0.62 to 0.81, and for daily sediments, TN, and TP load estimation, with NSE values of 0.21, 0.20, and 0.47, respectively. CC was planted after harvesting the main crop. Therefore, it is essential to quantify the seasonal reduction in pollutants. Water quality was improved after BMP implementation, and an overall decrease in streamflow, sediment, TN, and TP loads was observed for both MW and YRW during dry and wet seasons. Previous studies regarding seasonal assessments with CC implementation in the MW and YRW were limited. Therefore, the results from this study could be a unique addition to the scientific literature. Full article

Inconsistent yield of subtropical table grape across seasons is often associated with low carbohydrate reserves during flowering. In an attempt to increase TNC and thus yield, sucrose was injected into treated trunks during periods of high carbohydrate demand (i.e., between budburst and flowering). Total non-structural carbohydrate (TNC) concentration dynamics were assessed in the grapevine root and trunk tissues of both control and treated vines. In the control (untreated) vines, the TNC concentration in root and trunk tissues was 13.5% and 7.5% w/dw at leaf fall and 7.2% and 3.7% w/dw at flowering, respectively. This decrease in carbohydrate reserve was estimated at ~500 g/vine and is associated with the re-establishment of the plant canopy in early spring. Carbohydrate reserves remained stable or rose slightly between flowering and harvest and recovered between crop harvest and leaf fall. In treated vines, a constantly pressurised low-pressure in situ trunk injection system (69 kPa) with 5% w/v sucrose solution over 45 days from budburst (to flowering), in each of the two seasons, delivered a widely variable amount of sucrose into each vine with variation ascribed to the amount of internal dead wood in the trunk. In the best circumstances, an average of 150 g sucrose/vine/season was injected, and sucrose-injected vines had higher trunk TNC reserve (4.1% compared to 3.6% w/dw in the control) at flowering. A δ¹³C (‰) analysis confirmed the presence of injected sucrose in the shoot at flowering. However, the correlation between the amount of loaded sucrose and δ13C in young shoot tissue was poor, indicative of variable partitioning patterns. Inflorescence number per vine and berry yield were markedly higher in sucrose-injected vines, but differences were not significant given the high variation between vines. The addition of KCl to the sucrose solution and use of the healthy vines are recommended to increase sucrose loading using the injection method to address inconsistent yielding of subtropical table grape. Full article

Weed harrowing is commonly used to manage weeds in organic farming but is also applied in conventional farming to replace herbicides. Due to its whole-field application, weed harrowing after crop emergence has relatively poor selectivity and may cause crop damage. Weediness generally varies within a field. Therefore, there is a potential to improve the selectivity and consider the within-field variation in weediness. This paper describes a decision model for precision post-emergence weed harrowing in cereals based on experimental data in spring barley and nonlinear regression analysis. The model predicts the optimal weed harrowing intensity in terms of the tine angle of the harrow for a given weediness (in terms of percentage weed cover), a given draft force of tines, and the biological weed damage threshold (in terms of percentage weed cover). Weed cover was measured with near-ground RGB images analyzed with a machine vision algorithm based on deep learning techniques. The draft force of tines was estimated with an electronic load cell. The proposed model is the first that uses a weed damage threshold in addition to site-specific values of weed cover and soil hardness to predict the site-specific optimal weed harrow tine angle. Future field trials should validate the suggested model. Full article

Site-specific measurements of the crop yield during harvesting are essential for successfully implementing precision management techniques. This study aimed to estimate the mass of radish tubers using the impact principle under simulated vibration and sloped-field harvesting conditions with a laboratory test bench. These conditions included the conveyor speed (CS), impact plate layout (IP), falling height onto the impact plate (FH), the plate angle relative to the horizontal (PH), the field slope, and the vibration of the harvesting machine. Two layouts of impact-type sensors were fabricated and tested, one with a single load cell (SL) and the other with two load cells (DL). An adjustable slope platform and a vibration table equipped with vibration blades were utilized to simulate the slope and vibration effects, respectively. Calibrations were conducted to verify the accuracy of the sensor outputs, processed with the finite impulse response and moving average filters. Radish mass was estimated using an asymmetrically trimmed mean method. The relative percentage error (RE), standard error (SE), coefficient of determination (R²), and analysis of variance (ANOVA) were used to assess the impact plate performance. The results indicated that the SE for both impact plates was less than 4 g in the absence of vibration and slope conditions. The R² for the single and double impact plates ranged from 0.58 to 0.89 and 0.69 to 0.81, respectively. The FH had no significant impact, while the PH significantly affected the mass measurements for both impact plates. On the other hand, the CS significantly affected the plate performance, except for the double-load-cell impact plate. Both vibration and slope affected the mass measurements, with RE values of 9.89% and 13.92%, respectively. The RE for filtered radish signals was reduced from 9.13% to 5.42%. The tests demonstrated the feasibility of utilizing the impact principle to assess the mass of radishes, opening up possibilities for the development of yield-monitoring systems for crops harvested in a similar manner. Full article