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With the continuous advancement of smart greenhouse technologies, digital and information-based environmental monitoring has emerged as a focal point of research. The development of high-performance gas sensors is central to achieving this objective. In recent years, MXene materials have been widely applied in the field of gas sensors due to their excellent ion mobility, favorable hydrophilicity, outstanding electronic conductivity, and unique physicochemical properties. Various MXene heterojunction structures have been synthesized for gas detection. This review aims to summarize the current state of research on Ti3C2Tx-based gas sensors, explore methods for synthesizing different morphologies of Ti3C2Tx heterojunction structures, and evaluate the sensing behaviors of these configurations to fully harness their potential for gas monitoring in greenhouse environments. Additionally, an in-depth analysis of the sensing mechanisms associated with Ti3C2Tx heterojunction structures will be provided, offering theoretical support for future investigations. The findings indicate that Ti3C2Tx-based nanomaterials demonstrate considerable promise as high-performance sensors for gas detection in greenhouse settings. This innovative research not only provides new insights into the development of gas sensor technologies but also serves as an important foundation for the digitization of environmental monitoring. [ABSTRACT FROM AUTHOR]

Low-intensity (≈2 lmol·m22·s21) photoperiodic lighting is often delivered at night to promote flowering of long-day greenhouse ornamentals when natural daylengths are short. An intermediate far-red (FR) fraction [percentage of FR light in red (R) + FR light] is necessary for the most rapid flowering in some crops, including snapdragon (Antirrhinum majus) and petunia (Petunia ×hybrida), compared with a low FR fraction. Specialty light-emitting diodes (LEDs) that include R+FR light with an intermediate FR fraction are effective at floral promotion but cost-prohibitive, whereas common warm-white (WW) LEDs with a low FR fraction can delay flowering. Because the duration to saturate flowering is longer than currently used (e.g., 4 to 8 hours) for some long-day plants, we conducted a replicated greenhouse experiment to determine how the WW or R+FR LED lighting duration influenced flowering. We grew snapdragon 'Liberty Classic Yellow', petunia 'Easy Wave Burgundy Star', and petunia 'Wave Purple Improved' under truncated 8-h natural short days with or without WW or R+FR (1:1) LEDs operating for 0, 4, 8, 12, or 16 hours in the middle of each night throughout the experiment. Snapdragon flowered 13 to 16 days earlier (21% to 28% earlier) under R+FR LEDs than under WW LEDs regardless of the lighting duration. Increasing the lighting duration from 0 to 16 hours decreased flowering time by up to 16 days and decreased plant height and leaf number at flowering under R+FR LEDs but not under WW LEDs. For petunia 'Easy Wave Burgundy Star', although WW LEDs delayed flowering by 6 to 13 days but promoted lateral branching compared with R+FR LEDs, the gap in flowering time narrowed as the lighting duration increased from 4 to 16 hours. Increasing the lighting duration improved the efficacy of WW LEDs but not R+FR LEDs. Flowering of petunia 'Wave Purple Improved' was unaffected as the lighting duration increased from 4 to 16 hours regardless of the lamp type and was delayed by 6 to 10 days under WW LEDs than under R+FR LEDs. For both petunia cultivars, flowering time was similar under 16-hour WW LEDs and 4-hour R+FR LEDs. In conclusion, increasing the nighttime lighting duration increased the efficacy of WW LEDs at promoting flowering of petunia and increased the efficacy of R+FR LED lamps at promoting flowering of snapdragon. Delivering WW LEDs all night long can minimize flowering delay in petunia compared with R+FR LEDs. In contrast, an intermediate FR fraction was indispensable to promote flowering of snapdragon, for which WW LEDs were ineffective. [ABSTRACT FROM AUTHOR]

Background: Tomato (Solanum lycopersicum L.) can overcome productivity challenges caused by plant-parasitic nematodes, especially Meloidogyne incognita. M. incognita is the most harmful polyphagous endoparasitic nematode of various cultivated crops globally and causes huge yield losses. The use of traditional chemical nematicides poses environmental and health risks, which is why safe alternatives are being explored. This study aimed to find alternatives to peat as a potting medium and evaluate their effectiveness in enhancing plant resilience against nematodes. Specifically, the study assessed the efficacy of four biocontrol agents (Trichoderma asperellum, Beauveria bassiana, Fusarium proliferatum and Bacillus mojavensis) in peat-based and compost-based potting media under open-field and greenhouse conditions. Result: The research analyzes their impact on plant growth performance and their capacity to mitigate the severity of galling, the primary symptom caused by M. incognita in tomato cultivation. The results showed insignificant difference between peat and compost-based media in open-field (F(1,101) = 0.001, p = 0.97) and greenhouse (F(1,90) = 2.53, p = 0.12) experiments. However, biocontrol agents differed significantly in effectiveness (open field: F(4,101) = 5.85, p < 0.001; greenhouse: F(4,90) = 15.88, p < 0.001). B. mojavensis reduced the gall index by 81.24% in compost-based medium, and B. bassiana reduced it by 68.86% in peat-based medium. Conclusion: This study unequivocally demonstrated the remarkable efficacy of F. proliferatum and T. asperellum in promoting aboveground plant development. The application of biocontrol agents F. proliferatum, T. asperellum, B. bassiana and B. mojavensis resulted in a substantial 25–81% reduction in nematode galling index compared to the untreated control. [ABSTRACT FROM AUTHOR]

Non‐native annual grasses are degrading rangelands in the western United States and of vital management importance. Novel management strategies are needed to extend current approaches. The aim of this study was to determine if biofumigation was a viable strategy to manage non‐native annual grasses (cheatgrass, Bromus tectorum and ventenata, Ventenata dubia). We tested the effect of Brassica juncea as ground seed meal, seed meal leachate, mustard straw, mustard straw leachate and cereal straw at increasing rates on the two non‐native species and two native perennial grasses (Idaho fescue, Festuca idahoensis and bluebunch wheatgrass, Pseudoroegneria spicata) in a growth chamber experiment. A solarization split treatment was applied using a clear cover to determine if solarization enhanced the biofumigant effect. We recorded the number of emergent seedlings after a 3‐week growth period, determined the effective dose 50%, and the above‐ and belowground biomass. Emergence was inhibited for all species using ground seed meal and seed meal leachate, with lower rates and higher consistency achieved with ground seed meal. Three species were inhibited using mustard straw leachate (not F. idahoensis). Mustard straw reduced emergence in all species but was not different from cereal straw. Solarization did not enhance the effects of the biofumigant for seed meal or mustard straw; conversely, emergence increased from the seed meal and mustard straw leachates under solarization. Responses in biomass varied across species and treatment. Biofumigation applied as ground seed meal may be a viable option for integrated weed management in rangelands, but field experimentation is necessary. [ABSTRACT FROM AUTHOR]

As the foundation for implementing closed-loop detection within the realm of visual SLAM (simultaneous localization and mapping), visual place recognition (VPR) has great potential in various applications of greenhouse robot navigation and other fields. However, the existing VPR cannot fully meet the actual requirements of greenhouse scenes due to the complexity and constant variations in the greenhouse environment. In particular, the local feature aggregation paradigm strongly depends on the induction bias of training samples in VPR models, which leads to the issue of information redundancy during feature aggregation. In this study, a greenhouse VPR was presented, according to the optimal transport of local feature aggregation. The process of aggregating local features into a global descriptor was framed as an optimal transport problem, where the cost matrix was predicted through an MLP (multi-layer perceptron). Thus, a cost matrix was dynamically generated using the local features that was extracted from the greenhouse scene images. Additionally, a 'dustbin' cluster was introduced into the cost matrix to allocate the redundant features. Taking the cost matrix as the input, the Sinkhorn algorithm was employed to determine an optimal solution to the assignment matrix. Furthermore, the soft assignment of local features to various clusters was achieved through the assignment matrix. Ultimately, the assignment was concatenated to form a global descriptor for the scene image, which was used for place recognition. A deep neural network (DNN) was optimized and designed to serve as the backbone for local feature extraction of greenhouse scene images, by combining the advantages of CNN (convolutional neural network) and Transformer. Furthermore, cosine similarity was used as the metric function to calculate the similarity measure between scene image global descriptors, so as to perform descriptor matching. A series of experiments were conducted in a tomato greenhouse. The experimental results showed that the improved model achieved better performance. The top-1 recall rate (R@1) for place recognition was achieved at 88.96%, which was 29.67, 2.97, and 2.89 percentage points higher than the those of NetVLAD, MixVPR, and EigenPlaces models, respectively. When compared to the aggregators employed in MixVPR and NetVLAD, our aggregator achieved improvements in R@1 by 1.09 and 21.65 percentage points, respectively, showcasing its effectiveness. Compared with the CNN, the improved network achieved an increase of 5.45 percentage points in R@1. There was even more pronounced R@1 improvement (reaching 10.48 percentage points), compared with a Transformer network. Simultaneously, our network resulted in a 1.6-fold increase in computation speed compared to the previous Transformer. In addition, the experiments further demonstrated that the improved model exhibited excellent performance of place recognition and strong robustness when dealing with factors, such as small sampling distance shifts, small viewpoint shifts, and different sunlight intensities. The greenhouse VPR achieved a place recognition rate of no less than 81.94% in actual greenhouses, indicating its practical application potential. The method based on optimal transport of local feature aggregation and global descriptor generation was effective for place recognition, and the image local feature extraction network can boost the performance of place recognition. These findings can provide technical support to the visual systems of intelligent agricultural machinery in the greenhouse. [ABSTRACT FROM AUTHOR]

This paper proposes a novel scheme of biomass-regenerated two-stage desiccant-supported greenhouse cooling in tropical and subtropical regions. The system's goal is to provide the ideal thermal environment inside a greenhouse for the growth of different kinds of Orchids. Two-stage desiccant-based cooling is used in the proposed system to obtain a low humidity ratio inside the greenhouse. The desiccant is regenerated using a biomass-based heating system. The first law analysis and the exergy analysis of the system's individual components have been included in the study. The results of the thermal model (greenhouse temperature) have been compared with those of a reference model study available in the literature. The mean absolute error for the humidity ratio and greenhouse air temperature is 4.3% and 4.5%, respectively. The model's predictions are in good agreement with the results of the reference model. The performance study reveals that the maximum greenhouse air temperature can be restricted within 26 °C even during the peak sunshine hours for a typical day in May which represents the peak summer season in India. The proposed system COPth varies from a minimum of 0.54 to a maximum of 1.02 for a typical hot and humid day of July. The proposed system can exhibit a maximum exergy efficiency of 33% at 6 AM for a representative day in July. The maximum exergy destruction occurs at the DW1 (39.25%) and regeneration heater (21.69%). The payback period of the proposed system is 8.2 years, considering a 15-year life span. [ABSTRACT FROM AUTHOR]

Introduction: To ensure higher productivity, fertilizers have been excessively applied to the fruit greenhouse soil yearly, thus resulting in the increasing risks of residual nitrate leaching in the North China Plain. Methods: In this study, a water and solute transport HYDRUS-1D model was used to evaluate the effects of using sweet corn as a catch crop on deep water drainage and nitrate leaching in a sweet cherry greenhouse soil. A three-year (2019–2021) field experiment was conducted during the rainfall season from July to September in the post-harvest of sweet cherry, when the plastic cover was removed each year. In the experiment, the five treatments were designed. The three nitrate residue levels denoted by CKR, N1R, and N2R, represented nitrate residue amounts in the soil profile of three nitrogen fertilizer levels(0, 280 and 420kg ha-1) before the harvest of sweet cherry(March to June). Two other treatments with and without sweet corn as a catch crop based on the treatments of N1R and N2R were denoted by N1RC and N2RC, respectively. The data of both the spatial and temporal distribution of water and nitrate content during the rainy seasons of 2019, 2020 and 2021 in the field experiment were collected to calibrate and validate the model. Results: The simulated results have showed that using sweet corn as a catch crop increased the evapotranspiration rate, the upward flux of water and nitrate at a 100 cm soil depth reached a maximum of 1.5 mm d-1 and 1.0 kg N ha-1d-1, respectively, and the downward movement of water and nitrate leached to deeper soil layers was reduced. Compared with CKR, the treatments with catch crops (N1RC and N2RC) reduced the amount of water drainage by 16.4% -47.7% in the 0-180cm soil profile. The average amounts of nitrate leaching in the 1.8 m soil profile during the three-year experiment were 88.1, 113.3, and 58.2 kg N ha−1 for the treatment without catch crop (N1R and N2R) and 32.3, 54.8, and 31.4 kg N ha−1 for the treatment with catch crop (N1RC and N2RC), respectively. The treatments (N1RC and N2RC) with catch crops decreased the amount of nitrate leaching by 29.6%-69.1% compared with the treatments without catch crops (N1R and N2R). Discussion: Sweet corn as summer catch crop can reduce nitrate leaching in the sweet cherry greenhouses. Our study has provided an effective method to reduce the risk of nitrate leaching for sweet cherry greenhouses in the North China Plain. [ABSTRACT FROM AUTHOR]

Irrigation is one of the most important cultural practices in sustainable cabbage cultivation. While most studies on irrigation in cabbage have focused on conventional deficit irrigation (DI) practices, some plants' water requirements under the partial root drying (PRD) technique are not yet very clear. In this study, the possible responses of cabbage, such as growth, some quality, yield, yield parameters, water use efficiency (WUE), irrigation water use efficiency (IWUE), and yield response factor (ky), were investigated at four irrigation water levels (125%, 100%, 75%, and 50%) with DI and PRD techniques for 2 years. Irrigation treatments were carried out by the drip irrigation method, and the amount of irrigation water for the control (I-100) was calculated using the measurements taken from the Class-A evaporation container. A total of eight irrigation treatments—four conventional deficit irrigation (I-125, I-100, I-75, I-50) and four partial root drying (PRD-125, PRD-100, PRD75, PRD-50)—were considered in the study. ET values were determined between 47.69–60.78 mm in the first year and 80.11–101.37 mm in the second year. Total and marketable yield values, WUE and IWUE values, were significantly affected by the irrigation treatments. As a result of the research, the highest total and marketable yields were found in I-125, PRD-125, I-100, and PRD-100 treatments. It was important that WUE and IWUE values reached their highest levels in full irrigation and 25% more irrigation treatments as well as in deficit irrigation treatments. In conditions where irrigation water is scarce and expensive, I-75 and PRD-75 applications are also recommended. While an increase in cabbage head height and diameter was observed with increasing irrigation water level, SSC and L values increased at deficit irrigations. According to the correlation coefficients, a positive relationship was determined between marketable yield and head and stem diameter, head height, WUE, and ET for marketable yield. In addition, it was predicted that I-50 and PRD-50 treatments may also be advantageous if the "kc" plant coefficient cover percentage was increased. [ABSTRACT FROM AUTHOR]

When monitoring crop growth using top-down images of the plant canopies, leaves in agricultural fields appear very dense and significantly overlap each other. Moreover, the image can be affected by external conditions such as background environment and light intensity, impacting the effectiveness of image segmentation. To address the challenge of segmenting dense and overlapping plant leaves under natural lighting conditions, this study employed a Bilayer Convolutional Network (BCNet) method for accurate leaf segmentation across various lighting environments. The major contributions of this study are as follows: (1) Utilized Fully Convolutional Object Detection (FCOS) for plant leaf detection, incorporating ResNet-50 with the Convolutional Block Attention Module (CBAM) and Feature Pyramid Network (FPN) to enhance Region of Interest (RoI) feature extraction from canopy top-view images. (2) Extracted the sub-region of the RoI based on the position of the detection box, using this region as input for the BCNet, ensuring precise segmentation. (3) Utilized instance segmentation of canopy top-view images using BCNet, improving segmentation accuracy. (4) Applied the Varifocal Loss Function to improve the classification loss function in FCOS, leading to better performance metrics. The experimental results on cucumber canopy top-view images captured in glass greenhouse and plastic greenhouse environments show that our method is highly effective. For cucumber leaves at different growth stages and under various lighting conditions, the Precision, Recall and Average Precision (AP) metrics for object recognition are 97%, 94% and 96.57%, respectively. For instance segmentation, the Precision, Recall and Average Precision (AP) metrics are 87%, 83% and 84.71%, respectively. Our algorithm outperforms commonly used deep learning algorithms such as Faster R-CNN, Mask R-CNN, YOLOv4 and PANet, showcasing its superior capability in complex agricultural settings. The results of this study demonstrate the potential of our method for accurate recognition and segmentation of highly overlapping leaves in diverse agricultural environments, significantly contributing to the application of deep learning algorithms in smart agriculture. [ABSTRACT FROM AUTHOR]

Agricultural sensors can greatly contribute to future technologies and systemic innovation in smart agriculture. However, the types and precision of sensors are limited to monitoring the agricultural environment with complex and diverse objects. The large and redundant monitoring data has also resulted in the low reliability of information perception. In this study, an improved radial basis function neural network (RBFNN) and Chernobyl disaster optimizer (ICDO) multi-sensor data fusion was proposed to improve the accuracy and reliability of single-sensor measurement. Firstly, an improved Chernobyl catastrophe optimization was performed on the neural network model. The good-point set theory was introduced to improve the initial population quality of the CDO, particularly for accuracy and speed. The adaptive Laplacian crossover operator was added to enhance the search performance. The better adaptive behavior was achieved in the high convergence speed. And then, the individual learning and differential evolution strategy were used to redefine the location update equation, in order to balance the local and global exploration. Secondly, the RBF neural network model was optimized by ICDO, in order to improve the stability of the model. Finally, the nonlinear mapping of the RBF neural network model was used to realize the multi-sensor data fusion with high accuracy. Three experiments were conducted to verify the improved model. The first one was to verify the ICDO. A large improvement was obtained in the solution accuracy and optimization stability, compared with particle swarm optimization (PSO), gray wolf optimization (GWO), firefly algorithm (FA), dung beetle optimizer (DBO), and subtraction average-based optimizer (SABO). The second one was to evaluate the quality of the atmospheric environment. Specifically, the atmospheric data was collected outside the South Subtropical Botanical Garden in Mazhang District, Zhanjiang City, Guangdong Province, China, from September 1, 2022, to September 30, 2023. The goodness of fit reached 0.999 for the prediction of atmospheric environmental quality, the mean square error was as low as 0.348, and the mean absolute percentage error was reduced to 0.729%. The third one was to classify the greenhouse environment. The data was collected in the greenhouses of the South Asian Tropical Botanical Garden. The accuracy rate of greenhouse environment classification was 99.21% with a precision rate of 99.91%. The data fusion was suitable for both indoor and outdoor environments, indicating better adaptability and high accuracy. This finding can also provide solid technical support to agricultural sensor data fusion in the field of precision agriculture. [ABSTRACT FROM AUTHOR]

Ground roughness can significantly impact the battery performance in greenhouse environments. In this study, battery energy management was integrated with path planning to address this challenge. A systematic investigation was also implemented to explore the effects of ground roughness on the battery life and utilization efficiency of greenhouse vehicle platforms. A graded pre-scoring model was constructed using prior knowledge. Additionally, the Manhattan distance between the vehicle's current position and the target point was incorporated into the reinforcement learning reward function, thus linking travel distance with battery life to optimize both battery utilization efficiency and life during path planning. An Adaptive Multi- step Q-learning algorithm (AMQL) with adaptive step sizes and an Adaptive ε-greedy Q-learning algorithm (AEQL) with an adaptive exploration rate was proposed to enhance the performance of the Q-learning algorithm. The traditional Q-learning algorithms were associated with some issues, such as long iteration times, low convergence efficiency, susceptibility to local optima, and excessive path turns. The AMQL algorithm was used to adjust the step size, according to the forward reward assessment, if the reward at the current position increased corresponding to the previous reward, the step size increased. The step size gradually decreased to prevent suboptimal path optimization, as the current position approached the endpoint. The AEQL algorithm was used to adaptively adjust the exploration rate ε using the difference between adjacent reward values—ε increased when the adjacent reward value increased, and ε decreased when the reward value decreased. Although AMQL improved the convergence efficiency and iteration speed, the variations in the step size caused significant fluctuations in rewards, resulting in lower algorithm stability. Additionally, there was no outstanding impact of multi-step length on the convergence efficiency and iteration speed. Furthermore, the AEQL enhanced the exploration efficiency and algorithm stability through dynamic adjustments. But its fluctuating rise during the initial training phase also increased the training time. Therefore, the AMQL and AEQL algorithms were combined to develop an Adaptive Multi-step and ε-greedy Q-learning algorithm (AMEQL), in order to ensure faster and more optimal global path selection during path planning. In a simulated environment, the models were first used to simulate a realistic greenhouse tomato scenario. Then, an Inertial Measurement Unit (IMU) was used to record the changes in the aisle roughness in real time. This data was then incorporated into the simulation model. Finally, 300 rounds of simulation experiments were carried out to test the traditional Q-learning, AMQL, and AMEQL algorithm for path planning in the single-row (30 m×20 m), double-row (50 m×50 m), and triple-row (70 m×50 m) environments. Simulation results show that the AMEQL algorithm reduced the average training time by 44.10%, the average number of iterations required for convergence by 11.06%, the number of path turns by 63.13%, and the post-convergence average fluctuation by 15.62%, compared with the traditional Q-learning. Due to its higher convergence speed in 400 iterations, the AMEQL algorithm averaged 14 fluctuations per 100 iterations after reaching the maximum reward, while the AMQL algorithm averaged 15 fluctuations. This algorithm can provide a theoretical reference for the autonomous path planning of greenhouse platforms. [ABSTRACT FROM AUTHOR]

Weed control in container-grown ornamentals can be improved by careful herbicide selection. Four studies were conducted in greenhouses at Oregon State University to improve the understanding of how differences in the mode of action and formulation of herbicides can affect bryophyte control efficacy and crop safety. Granular (G) formulations of pendimethalin and indaziflam were compared with sprayable liquid (L) formulations of pendimethalin, indaziflam, and dimethenamid-p as well as with a nontreated control on four perennial container-grown ornamentals. Indaziflam in the L formulation performed better than that in the G formulations for controlling hairy bittercress up to 20 weeks after the initial treatment. Dimethenamid-p was more effective than indaziflam for liverwort and moss control. Pendimethalin in the G formulation less effectively controlled hairy bittercress than the L formulation did, but it performed better against moss during a second study. The L formulation of indaziflam injured Japanese pachysandra and boxwood and reduced root and shoot growth by 10% to 29%. Dimethenamid-p provided excellent control of the weed species tested and was safe for the crops, indicating its potential use as an alternative to hand-weeding in greenhouses. [ABSTRACT FROM AUTHOR]

In response to the growing demand for environmentally sustainable agricultural practices that enhance production efficiency, this study aimed to determine the optimal application rates of bovine rumen content (CBR) and chicken litter (CCL) as organic composts. Conducted in a controlled greenhouse environment, the research assessed the effects of various doses of these composts on soil chemical and microbiological properties and on the growth and productivity of yellow biquinho pepper (Capsicum chinense Jacq.). The results of this study confirm that organic composts improve soil health and increase the growth and productivity of yellow biquinho pepper. However, while CBR consistently enhances plant growth across a range of doses, CCL shows a dose-dependent response, with the highest fruit production achieved at an application rate of 73.92 Mg ha−1. Although this dose of CCL is beneficial for increasing yield, it also marks a threshold beyond which further increases may lead to negative effects such as nutrient saturation or salt accumulation, potentially resulting in soil toxicity. The study supports the use of CBR and CCL as effective soil conditioners and emphasizes the need to identify the optimal compost dose to efficiently harness the benefits of organic composts in sustainable agricultural practices. [ABSTRACT FROM AUTHOR]

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Greenhouses have taken on a fundamental role in agriculture. The Internet of Things (IoT) is a key concept used in greenhouse-based precision agriculture (PA) to enhance vegetable quality and quantity while improving resource efficiency. Integrating wireless sensor networks (WSNs) into greenhouses to monitor environmental parameters represents a critical first step in developing a complete IoT solution. For further optimization of the results, including actuator nodes to control the microclimate is necessary. The greenhouse must also be remotely monitored and controlled via an internet-based platform. This paper proposes an IoT-based architecture as a decision support system for farmers. A web platform has been developed to acquire data from custom-developed wireless sensor nodes and send commands to custom-developed wireless actuator nodes in a greenhouse environment. The wireless sensor and actuator nodes (WSANs) utilize LoRaWAN, one of the most prominent Low-Power Wide-Area Network (LPWAN) technologies, known for its long data transmission range. A real-time end-to-end deployment of a remotely managed WSAN was conducted. The power consumption of the wireless sensor nodes and the recharge efficiency of installed solar panels were analyzed under worst-case scenarios with continuously active nodes and minimal intervals between data transmissions. Datasets were acquired from multiple sensor nodes over a month, demonstrating the system's functionality and feasibility. [ABSTRACT FROM AUTHOR]

Timely detection and control of airborne disease is important to improve productivity. This study proposed a novel approach that utilizes micro polarization image features and a backpropagation neural network (BPNN) to classify and identify airborne disease spores in a greenhouse setting. Firstly, disease spores were collected in the greenhouse, and their surface morphological parameters were analyzed. Subsequently, the micropolarization imaging system for disease spores was established, and the micropolarization images of airborne disease spores from greenhouse crops were collected. Then the micropolarization images of airborne disease spores were processed, and the image features of polarization degree and polarization angle of disease spores were extracted. Finally, a disease spore classification model based on the BPNN was ultimately developed. The results showed that the texture position of the surface of the three disease spores was inconsistent, and the texture also showed an irregular shape. Texture information was present on the longitudinal and transverse axes, with the longitudinal axis exhibiting more uneven texture information. The polarization-degree images of the three disease spores exhibit variations in their representation within the entirety of the beam information. The disease spore polarization angle image exhibited the maximum levels of contrast and entropy when the Gabor filter's direction was set to n/15. The recognition accuracy of cucumber downy mildew spores, tomato gray mildew spores, and cucumber powdery mildew spores were 75.00%, 83.33%, and 96.67%, respectively. The average recognition accuracy of disease spores was 86.67% based on BPNN and micropolarization image features. This study can provide a novel method for the detection of plant disease spores in the greenhouse. [ABSTRACT FROM AUTHOR]

Electrostatic spraying is considered an effective means to improve the efficacy of pesticide application and reduce pesticide consumption. However, the effectiveness of electrostatic spraying needs further validation in greenhouse environments, especially in long-range air-assisted spraying scenarios. A waist-shaped charging device has been improved to obtain a maximum charge-to-mass ratio of 4.4 mC/kg at an applied voltage of 6 kV in a laboratory setting, representing an increase of approximately 84.9% compared to a commercial circular charging electrode with a fan-shaped nozzle. A comparative air-assisted spray test between electrostatic deactivation (EDAS) and electrostatic activation (EAAS) was conducted on greenhouse tomato crops using a single hanging track autonomous sprayer equipped with a pair of waist-shaped charging devices. The results showed that EAAS yielded an overall average coverage of 28.4%, representing a significant 10.9% improvement over the 25.6% coverage achieved with EDAS. The overall coefficient of variation (CV) for EDAS and EAAS was 62.0% and 48.0%, respectively. Within these, the CV for the average coverage of the sample set reflecting axial distribution uniformity was 33.4% and 31.4%, respectively. Conversely, the CV for the average coverage of the sample group reflecting radial distribution uniformity was 33.7% and 17.9%, respectively. The results indicate that the waist-shaped charging device possesses remarkable charging capabilities, presenting favorable application prospects for long-range air-assisted spraying in greenhouses. The electrostatic application has a positive effect on enhancing the average coverage and improving the overall distribution uniformity. Notably, it significantly improves the radial distribution uniformity of the air-assisted spray at long range, albeit with limited improvement in the axial distribution uniformity. [ABSTRACT FROM AUTHOR]

As the world progresses towards achieving carbon neutrality, there is an urgent need to explore renewable energy sources, such as solar and geothermal energy, for greenhouse heating. Integrating these energy sources not only reduces greenhouse gas emissions but also ensures reliable and efficient heating, which is essential for maintaining optimal growing conditions in greenhouses. This study specifically focuses on the challenges associated with ground source heat pump (GSHP) systems, which have gained widespread attention for their stability when operating under low ambient temperatures. However, a well-documented issue with GSHP systems is the thermal imbalance in the soil, where prolonged use causes the ground temperature to drop, thereby reducing the system's efficiency over time. To address this issue, this study explores the operational characteristics of coupling seasonal solar thermal energy storage (SSTES) and solar thermal energy direct heating (STEDH) with the GSHP system. These methods aim to mitigate thermal imbalance and enhance the overall performance of the heating system. An experimental platform was constructed in northern China to facilitate this investigation, consisting of a 112 m² Venlo-style glass greenhouse heated by a GSHP system integrated with solar energy. Over two heating seasons, the study conducted a series of experiments to compare the performance of the GSHP system with and without the integration of SSTES and STEDH. During the first heating season, which spanned from January 1st to March 15th, the GSHP system operated independently without SSTES. The results showed significant diurnal fluctuations in the greenhouse heat load, leading to intermittent operation of the GSHP system. This caused the soil temperature in the boreholes of the GSHP working wells to exhibit daily cyclical fluctuations, with a local temperature drop of up to 2.5 ℃ during heat extraction. The rate of soil temperature recovery was approximately 1.8 times faster than the rate of temperature decline, indicating a good daily thermal balance self-recovery capability. By the end of the heating season, a total of 16,934 kWh of heat had been extracted, with an annual heating coefficient of performance of 2.79. The soil temperature in the working wells dropped by 2.40 to 2.97 ℃ compared to the initial soil temperature, while the soil temperature in the monitoring wells within the well field decreased by 0.60 to 1.00 ℃. This temperature drop indicates that the soil thermal imbalance persisted, and even by early June, the soil had not recovered to its initial temperature, highlighting the severity of the thermal imbalance issue. In the subsequent non-heating season, from June 10th to November 7th, SSTES experiment was conducted. A total of 10,173 kWh of heat was stored in the soil, accounting for 60.1% of the total heat released during the previous heating season. The soil temperature at the monitoring points increased by approximately 0.2 ℃, demonstrating the effectiveness of SSTES in mitigating thermal imbalance. In the second heating season, from November 8th to March 15th of the following year, STEDH was coupled with the GSHP system. The results showed a 14.3% increase in the annual heating coefficient of performance, raising it to 3.19. At the same time, the rate of soil temperature decrease was slowed, indicating a reduction in thermal imbalance. In conclusion, coupling SSTES and STEDH with GSHP systems effectively addresses the issue of thermal imbalance and significantly enhances heating efficiency. Additionally, to further improve the overall efficiency of the system, it was recommended that the load-side return water temperature for the heat pump unit be maintained at 30 ℃, with the upper and lower temperature limits for SSTES set at 40 ℃ and 30 ℃, respectively. These findings provide valuable data and case studies for optimizing the design, operation, and control of greenhouse heating systems in engineering applications. This study contributes to the broader goal of sustainable energy use and carbon neutrality in the agricultural sector. [ABSTRACT FROM AUTHOR]

Zoophytophagous predators can be beneficial for controlling crop pests in greenhouses. Yet, they can also cause significant economic damage. More zoophagous and effective predator lines can be developed by selectively breeding highly zoophagous individuals. Hence, artificial selection based on the degree of zoophagy in zoophytophagous predators can improve their efficiency as biocontrol agents while reducing the risk of crop damage. However, artificial selection on zoophagy could cause changes in other behavioral or life history traits due to genetic correlation or pleiotropy. These changes can affect the ecological conditions in which biological control agents work. We created highly and lowly zoophagous lines of Dicyphus hesperus Knight (Hemiptera: Miridae) using artificial selection. We tested genetic correlations between zoophagy and food patch exploitation equity in four generations of artificial selection. The results revealed that females were more zoophagous than males. The broad sense heritability (H2) of zoophagy was 0.38 in females and 0.29 in males. Artificial selection on zoophagy led to decreased food patch exploitation equity, yet the traits were not genetically correlated. Our results suggest that artificial selection can be used to develop lines of D. hesperus that enhance the benefits of biological control and modify ecological factors such as prey density and distribution. [ABSTRACT FROM AUTHOR]

Ocean temperature, redox state, circulation, and nutrient levels regulate the marine nitrogen (N) cycle, yet their specific impacts during greenhouse intervals remain poorly understood. Here, we examined the Smithian–Spathian hyperthermal event (∼250.5 Ma) during the Early Triassic greenhouse using stable N isotopes (δ15N) from sedimentary records in the Nanpanjiang Basin and Northern Yangtze Basin of South China. The δ15N profiles in both basins reveal consistent trends that correspond to fluctuations in temperature across the Smithian–Spathian transition. The Smithian hyperthermal interval exhibited low δ15N values (mostly <+2‰), indicating N deficiency and enhanced biological N 2 fixation. Bacterial blooms and the release of the potent greenhouse gas N 2 O, enhanced by high temperatures, may have triggered positive feedback mechanisms that sustained the warming and contributed to the late Smithian extinction. During subsequent cooling across the Smithian–Spathian transition, δ15N increased to a range of +3‰ to +7‰, likely reflecting signals of partial denitrification based on reconstruction of the NO 3 − inventory associated with oceanic cooling and oxygenation. The prevailing increases in sedimentary TOC/TN ratios signify heightened deamination (removal of amino groups) and N recycling across the Smithian–Spathian boundary. This transition is likely attributed to cooling-driven amelioration of seawater stratification and anoxia from the Smithian to the Spathian, which resulted in increased nitrate availability in the photic zone. Eukaryotic algae thrived while prokaryotes declined during the Spathian, evidenced by elevated δ15N and Δ13C carb-org (the difference between carbonate and organic carbon isotopes). The proliferation of eukaryotic algae had a positive impact on environmental conditions and facilitated biotic recovery due to more efficient burial of organic particles. A notable latitudinal gradient in the N cycle response was observed, with low-latitude regions showing a more pronounced response to cooling compared to mid-latitude areas. This significant gradient may suggest that the rapid recovery of nutrient cycles, despite relatively small decline in high temperature, was a key factor in the amelioration of climate and recovery of life in low latitudes. These findings highlight that the marine N cycle is highly sensitive to temperature changes, particularly in low-latitude regions, and that changes in N cycle under high-temperature conditions may be a critical life-limiting factor. [ABSTRACT FROM AUTHOR]

One of the key issues confronting modern greenhouses is the need to supply the necessary energy in an environmentally friendly manner to facilitate heating and cooling processes within greenhouses. Solar radiation entering the greenhouse during the day can sometimes be more than the energy demand of the greenhouse. In contrast, there are cases where the greenhouse must dissipate a significant amount of heat, absorbed over a long period, either naturally or forcibly, during the cooling process. Moreover, the system's efficiency could be enhanced if there is a mechanism capable of capturing heat expelled during greenhouse cooling and redistributing it on demand. Employing thermal energy storage is critical for maintaining stable temperatures, assuring energy efficiency, encouraging sustainability, and enabling year-round production. This technique ensures a safe environment for crops and eliminates temperature fluctuations inside the greenhouse. Nocturnal thermal energy storage, storing thermal energy during the daytime for later use at night, is essential to managing a contemporary greenhouse because it promotes consistent crop growth, sustainability, and profitability, particularly in areas with severe winters and significant day-to-night temperature variations. This work reviews various types of thermal energy storage systems employed in previous works focusing on greenhouse applications by researchers and categorizes them based on efficient factors. [ABSTRACT FROM AUTHOR]

In recent years, an increase in the frequency of low-sunlight conditions due to climate change has resulted in a decline in the yield and quality of crops for greenhouse farmers, leading to significant challenges in maintaining optimal plant growth. The crop growth model can be used to predict changes in cucumber yield in response to variations in sunlight, which can help efficiently address sunlight shortages. The objective of this study was to improve and validate the model for predicting cucumber yield under different light environment conditions, including shading and supplemental lighting. The model comprises three steps: LAI prediction, daily assimilate yield prediction, and fruit yield prediction, each of which involves modifying the coefficients applied to suit the cucumber cultivar and environment condition. The improved model demonstrated a high degree of accuracy in predicting cucumber yields in the control and low-sunlight treatments (10, 20, and 30% shading), with a coefficient of determination (R2) > 0.98. When supplemental lighting was incorporated into the control and shading treatments, the accuracy of the improved model in predicting cucumber yield was also high, with a coefficient of determination (R2) > 0.99. The model also accurately predicted the decrease in cucumber fruit yield under low-sunlight conditions (shading treatments) and the increase in yield due to supplemental lighting. The findings of this study indicate that the improved cucumber yield prediction model can be applied to assess the efficacy of yield reduction in low-sunlight conditions and the potential for yield enhancement through supplemental lighting. [ABSTRACT FROM AUTHOR]

Many plant species form symbiotic associations with nitrogen-fixing bacteria. Through this symbiosis, plants allocate photosynthate belowground to the bacteria in exchange for nitrogen fixed from the atmosphere. This symbiosis forms an important link between carbon and nitrogen cycles in many ecosystems. However, the economics of this relationship under soil nitrogen availability gradients is not well understood, as plant investment toward symbiotic nitrogen fixation tends to decrease with increasing soil nitrogen availability. Here, we used a manipulation experiment to examine how costs of nitrogen acquisition vary under a factorial combination of soil nitrogen availability and inoculation with Bradyrhizobium japonicum in Glycine max L. (Merr.). We found that inoculation decreased belowground biomass carbon costs to acquire nitrogen and increased total leaf area and total biomass, but these patterns were only observed under low fertilization and were the result of increased plant nitrogen uptake and no change in belowground carbon allocation. These results suggest that symbioses with nitrogen-fixing bacteria reduce carbon costs of nitrogen acquisition by increasing plant nitrogen uptake, but only when soil nitrogen is low, allowing individuals to increase nitrogen allocation to structures that support aboveground growth. This pattern may help explain the prevalence of plants capable of forming these associations in less fertile soils and provides useful insight into understanding the role of nutrient acquisition strategy on plant nitrogen uptake across nitrogen availability gradients. [ABSTRACT FROM AUTHOR]

Greenhouses (GHs) are important elements of agricultural production and help to ensure food security aligning with United Nations Sustainable Development Goals (SDGs). However, there are still environmental concerns due to excessive use of plastics. Therefore, it is important to understand the past and future trends on spatial distribution of GH areas, whereby use of remote sensing data provides rapid and valuable information. The present study aimed to determine GH area changes in an agricultural hotspot, Serik, Türkiye, using 2008 and 2022 Landsat imageries and machine learning, and to predict future patterns (2036 and 2050) via the Markov–FLUS model. Performances of random forest (RF), k-nearest neighborhood (KNN), and k-dimensional trees k-nearest neighborhood (KD-KNN) algorithms were compared for GH discrimination. Accordingly, the RF algorithm gave the highest accuracies of over 90%. GH areas were found to increase by 73% between 2008 and 2022. The majority of new areas were converted from agricultural lands. Markov-based predictions showed that GHs are likely to increase by 43% and 54% before 2036 and 2050, respectively, whereby reliable simulations were generated with the FLUS model. This study is believed to serve as a baseline for future research by providing the first attempt at the visualization of future GH conditions in the Turkish Mediterranean region. [ABSTRACT FROM AUTHOR]

Soil-based emissions from land conversions are often overlooked in climate planning. The objectives of this study were to use quantitative data on soil-based greenhouse gas (GHG) emissions for the state of Georgia (GA) (USA) to examine context-specific (temporal, biophysical, economic, and social) climate planning and legal options to deal with these emissions. Currently, 30% of the land in GA has experienced anthropogenic land degradation (LD) primarily due to agriculture (64%). All seven soil orders were subject to various degrees of anthropogenic LD. Increases in overall LD between 2001 and 2021 indicate a lack of land degradation neutrality (LDN) in GA. Besides agricultural LD, there was also LD caused by increased development through urbanization, with 15,197.1 km2 developed, causing midpoint losses of 1.2 × 1011 kg of total soil carbon (TSC) with a corresponding midpoint social cost from carbon dioxide (CO2) emissions (SC-CO2) of USD $20.4B (where B = billion = 109, $ = U.S. dollars (USD)). Most developments occurred in the Metro Atlanta and Coastal Economic Development Regions, which indicates reverse climate change adaptation (RCCA). Soil consumption from developments is an important issue because it limits future soil or forest carbon (C) sequestration potential in these areas. Soil-based emissions should be included in GA's carbon footprint. Understanding the geospatial and temporal context of land conversion decisions, as well as the social and economic costs, could be used to create incentives for land management that limit soil-based GHG emissions in a local context with implications for relevant United Nations (UN) initiatives. [ABSTRACT FROM AUTHOR]

Abstract Background Tomato (Solanum lycopersicum L.) can overcome productivity challenges caused by plant-parasitic nematodes, especially Meloidogyne incognita. M. incognita is the most harmful polyphagous endoparasitic nematode of various cultivated crops globally and causes huge yield losses. The use of traditional chemical nematicides poses environmental and health risks, which is why safe alternatives are being explored. This study aimed to find alternatives to peat as a potting medium and evaluate their effectiveness in enhancing plant resilience against nematodes. Specifically, the study assessed the efficacy of four biocontrol agents (Trichoderma asperellum, Beauveria bassiana, Fusarium proliferatum and Bacillus mojavensis) in peat-based and compost-based potting media under open-field and greenhouse conditions. Result The research analyzes their impact on plant growth performance and their capacity to mitigate the severity of galling, the primary symptom caused by M. incognita in tomato cultivation. The results showed insignificant difference between peat and compost-based media in open-field (F(1,101) = 0.001, p = 0.97) and greenhouse (F(1,90) = 2.53, p = 0.12) experiments. However, biocontrol agents differed significantly in effectiveness (open field: F(4,101) = 5.85, p

Abstract Greenhouses need to supply CO2 to crops while simultaneously emitting CO2. To effectively harness the dual functionality of greenhouses as a carbon source and carbon consumer, this work incorporates carbon capture and emissions trading into a multi‐energy greenhouse (MEG), which is equipped with various power and heat sources such as photovoltaic (PV) panels and a combined heat and power (CHP) unit and proposes that the captured CO2 should be used to feed crops on‐site. A low‐carbon economic operation method is proposed for the coordinated environment‐energy‐carbon management of the MEG, and it considers various factors, including the power purchase/carbon supply costs, carbon emissions trading income, temperature/humidity/light intensity and CO2 concentration requirements for crops, and operational constraints of various energy/environmental regulation equipment. The proposed method is validated using a tomato MEG. The results highlight the significant economic and environmental benefits of introducing carbon capture, emissions trading, and utilisation into MEGs.

Yellow sticky traps (YSTs) are a standard tool for insect monitoring in greenhouses. These traps have been further developed by using them in combination with green light‐emitting diodes (LEDs) to increase their attractiveness towards pest insects such as aphids and whiteflies. However, also natural enemies, such as the whitefly parasitoid Encarsia formosa Gahan (Hymenoptera: Aphelinidae), are attracted to these traps. This may cause problems with biological control of the pest or may be used for indirect monitoring purposes. Therefore, we compared the attractiveness of YSTs and green (521 nm) LED traps towards E. formosa under practical growing conditions in tomato, Solanum lycopersicum L. (Solanaceae) and cucumber, Cucumis sativus L. (Cucurbitaceae), crops in the greenhouse. The aim of the study was to investigate the compatibility of LED traps with this parasitoid frequently used against the greenhouse whitefly, Trialeurodes vaporariorum Westwood (Hemiptera: Aleyrodidae). The results show LED traps catching less E. formosa than standard YSTs. Moreover, LED traps also showed compatibility with other beneficial insects. The results are discussed in the context of the parasitoid's behaviour towards various green light spectra and in the context of pest and beneficial insect monitoring using different trap types. Our study will help implementing green LED traps in future insect monitoring programmes and developing new pest control strategies without affecting natural enemies. [ABSTRACT FROM AUTHOR]

Abstract Saffron is the world's most expensive and legendary crop that is widely used in cuisine, drugs, and cosmetics. Therefore, the demand for saffron is increasing globally day by day. Despite its massive demand the cultivation of saffron has dramatically decreased and grown in only a few countries. Saffron is an environment-sensitive crop that is affected by various factors including rapid change in climate, light intensity, pH level, soil moisture, salinity level, and inappropriate cultivation techniques. It is not possible to control many of these environmental factors in traditional farming. Although, many innovative technologies like Artificial Intelligence and Internet of Things (IoT) have been used to enhance the growth of saffron still, there is a dire need for a system that can overcome primary issues related to saffron growth. In this research, we have proposed an IoT-based system for the greenhouse to control the numerous agronomical variables such as corm size, temperature, humidity, pH level, soil moisture, salinity, and water availability. The proposed architecture monitors and controls environmental factors automatically and sends real-time data from the greenhouse to the microcontroller. The sensed values of various agronomical variables are compared with threshold values and saved at cloud for sending to the farm owner for efficient management. The experiment results reveal that the proposed system is capable to maximize saffron production in the greenhouse by controlling environmental factors as per crop needs.

The importance of Model Predictive Control (MPC) has significant applications in the agricultural industry, more specifically for greenhouse’s control tasks. However, the complexity of the greenhouse and its limited prior knowledge prevent an exact mathematical description of the system. Subspace methods provide a promising solution to this issue through their capacity to identify the system’s comportment using the fit between model output and observed data. In this paper, we introduce an application of Constrained Model Predictive Control (CMPC) for a greenhouse temperature and relative humidity. For this purpose, two Multi Input Single Output (MISO) systems, using Numerical Subspace State Space System Identification (N4SID) algorithm, are firstly suggested to identify the temperature and the relative humidity comportment to heating and ventilation actions. In this sense, linear state space models were adopted in order to evaluate the robustness of the control strategy. Once the system is identified, the MPC technique is applied for the temperature and the humidity regulation. Simulation results show that the regulation of the temperature and the relative humidity under constraints was guaranteed, both parameters respect the ranges 15 °C ≤ Tint ≤ 30 °C and 50 % ≤ Hint ≤ 70 % respectively. On the other hand, the control signals uf and uh applied to the fan and the heater, respect the hard constraints notion, the control signals for the fan and the heater did not exceed 0 ≤ uf ≤ 4.3 Volts and 0 ≤ uh ≤ 5 Volts, respectively, which proves the effectiveness of the MPC and the tracking tasks. Moreover, we show that with the proposed technique, using a new optimization toolbox, the computational complexity has been significantly reduced. The greenhouse in question is devoted to Schefflera Arboricola cultivation.

To alleviate social problems in agriculture such as aging and labor force shortages, automatic growth monitoring based on image measurement has been introduced to tomato cultivation in greenhouses. The overlap of leaves and fruits makes precise observations challenging. In this study, we applied context recognition to tomato growth monitoring by using a Bayesian network. The proposed method combines image recognition using convolutional networks and context recognition using Bayesian networks. It enables not only the recognition of individual tomatoes but also the evaluation of tomato plants. An accurate number of tomatoes and the condition of the stocks can be estimated based on the number of ripe and unripened tomatoes in addition to their density information. The verification experiments clarified that a more accurate number of tomatoes could be estimated than with simple tomato detection, and the stock states could also be evaluated correctly. Compared to conventional methods, the method used in this study has improved tomato decision accuracy by 23%.

Mechanically transmissible and stable viruses such as tobamoviruses, which include Tobamovirus fructirugosum (syn. tomato brown rugose fruit virus (ToBRFV), will continue to pose major challenges for farmers. Consequently, holistic hygiene concepts are being implemented to prevent the introduction and spread of these viruses. The decontamination of tires and castors was previously a weak point in many industrial hygiene concepts. For this reason, the ProfilGate clean-off zone was tested in combination with the disinfectant MENNO Florades for the decontamination of ToBRFV-contaminated tires. In total, 478 tire segments were sampled to evaluate the contamination of ToBRFV and the following decontamination of the tires. This treatment reliably removed high (4.5 µg/cm2), medium (0.45 µg/cm2), and low concentrations (0.045 µg/cm2) of ToBRFV from the tires, as shown by a bioassay. The reduction in necrotic local lesions on susceptible indicator plants N. tabacum cv. Xanthi NN was between 91.9 and 97.6%. The reduction in ToBRFV contamination largely depended on the length of the rollover distance, i.e., the number of tire rotations. For transport trolleys with polyamide and rubber tires, depletions of 97.4 and 97.6%, respectively, was determined after 16 rotations. For transport wagons with tires twice the size and polyurethane tread, the depletion was still at least 91% after eight wheel turns. Even in the case of gross soiling of the tires, the mean reduction from the different tread materials was 80.9 to 98.9%. Subsequent analysis of the clean-off zone revealed that ToBRFV did not accumulate, even when the contaminated tires were driven over several times, but was safely inactivated completely in the disinfectant solution. This provides growers with an effective tool for preventing the introduction and spread of ToBRFV.

Saffron is the world's most expensive and legendary crop that is widely used in cuisine, drugs, and cosmetics. Therefore, the demand for saffron is increasing globally day by day. Despite its massive demand the cultivation of saffron has dramatically decreased and grown in only a few countries. Saffron is an environment-sensitive crop that is affected by various factors including rapid change in climate, light intensity, pH level, soil moisture, salinity level, and inappropriate cultivation techniques. It is not possible to control many of these environmental factors in traditional farming. Although, many innovative technologies like Artificial Intelligence and Internet of Things (IoT) have been used to enhance the growth of saffron still, there is a dire need for a system that can overcome primary issues related to saffron growth. In this research, we have proposed an IoT-based system for the greenhouse to control the numerous agronomical variables such as corm size, temperature, humidity, pH level, soil moisture, salinity, and water availability. The proposed architecture monitors and controls environmental factors automatically and sends real-time data from the greenhouse to the microcontroller. The sensed values of various agronomical variables are compared with threshold values and saved at cloud for sending to the farm owner for efficient management. The experiment results reveal that the proposed system is capable to maximize saffron production in the greenhouse by controlling environmental factors as per crop needs. [ABSTRACT FROM AUTHOR]

An accurate prediction model can be required for the greenhouse air temperature, such as Model Predictive Control. Long short-term memory neural networks (LSTM) have been widely used to predict time series data, such as air temperature. However, the prediction accuracy of LSTM can be reduced for the long time series data, due to data forgetting. In this study, the LSTM model was combined with the attention mechanism to construct the LSTM-AT model. The query vector, key vector, and value vector were calculated, according to attention mechanism and output states of LSTM’s hidden layer. The similarity between the query and key vector was calculated to obtain the similarity score. Softmax function was used to obtain attention distribution for the normalization processing. The larger the attention value was, the higher the relevance of input information to the task objective was. The dot product operation was carried out with the normalized weight and value vector to obtain the output of the attention mechanism. The local information integration and data dimension transformation were carried out through the full connection layer. Finally, the output data was obtained in the output layer of the LSTM-AT model. The weights were assigned to the output states of LSTM’s hidden layer, according to the degree of importance. The forgetting of long time series data was effectively solved to improve the prediction accuracy of indoor air temperature. The prediction performances were verified and compared on the LSTM-AT, LSTM, recurrent neural network (RNN), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM) model in the different prediction horizons (12, 24 and 48 h). The results showed that the prediction accuracy of the five models shared a decreasing trend with the increase in prediction time. The maximum and minimum RMSE for the LSTM-AT model were 1.34 ℃ and 0.59 ℃, respectively. The maximum and minimum RMSE for the rest four models were 3.37 ℃ and 0.66 ℃, respectively. The maximum and minimum MAPE for the LSTM-AT model were 8.14% and 2.48%, respectively. The maximum and minimum MAPE for the rest four models were 38.7% and 2.90%, respectively. Therefore, the prediction accuracy of the LSTM-AT model was higher than that of rest four models. The average RMSE for LSTM-AT, LSTM, GRU, RNN, and BiLSTM were 0.89, 1.42, 1.89, 2.10, and 1.51 ℃, respectively. The average MAPE for LSTM-AT, LSTM, GRU, RNN, and BiLSTM were 4.26%, 8.96%, 13.57%, 17.70%, and 10.67%, respectively. The sort data of the prediction model was ranked in descending order of the LSTM-AT, LSTM, BiLSTM, GRU, and RNN. The prediction performances of the LSTM-AT and LSTM model were compared under different weather conditions (sunny, cloudy, and rainy), in order to further explore the universality of the LSTM-AT model. The minimum and maximum RMSE for LSTMAT were 0.26℃ and 0.70 ℃, respectively. The minimum and maximum RMSE for LSTM were 0.68 ℃ and 1.57 ℃, respectively. The minimum and maximum MAPE for LSTM-AT were 1.61% and 10.51%, respectively. The minimum and maximum MAPE for LSTM were 4.27% and 25.07%, respectively. The prediction accuracy of the LSTM-AT model was higher than LSTM in all weather conditions. The LSTM-AT model has a higher prediction accuracy to accurately predict the indoor air temperature. [ABSTRACT FROM AUTHOR]

Mechanically transmissible and stable viruses such as tobamoviruses, which include Tobamovirus fructirugosum (syn. tomato brown rugose fruit virus (ToBRFV), will continue to pose major challenges for farmers. Consequently, holistic hygiene concepts are being implemented to prevent the introduction and spread of these viruses. The decontamination of tires and castors was previously a weak point in many industrial hygiene concepts. For this reason, the ProfilGate clean-off zone was tested in combination with the disinfectant MENNO Florades for the decontamination of ToBRFV-contaminated tires. In total, 478 tire segments were sampled to evaluate the contamination of ToBRFV and the following decontamination of the tires. This treatment reliably removed high (4.5 µg/cm2), medium (0.45 µg/cm2), and low concentrations (0.045 µg/cm2) of ToBRFV from the tires, as shown by a bioassay. The reduction in necrotic local lesions on susceptible indicator plants N. tabacum cv. Xanthi NN was between 91.9 and 97.6%. The reduction in ToBRFV contamination largely depended on the length of the rollover distance, i.e., the number of tire rotations. For transport trolleys with polyamide and rubber tires, depletions of 97.4 and 97.6%, respectively, was determined after 16 rotations. For transport wagons with tires twice the size and polyurethane tread, the depletion was still at least 91% after eight wheel turns. Even in the case of gross soiling of the tires, the mean reduction from the different tread materials was 80.9 to 98.9%. Subsequent analysis of the clean-off zone revealed that ToBRFV did not accumulate, even when the contaminated tires were driven over several times, but was safely inactivated completely in the disinfectant solution. This provides growers with an effective tool for preventing the introduction and spread of ToBRFV. [ABSTRACT FROM AUTHOR]

To alleviate social problems in agriculture such as aging and labor force shortages, automatic growth monitoring based on image measurement has been introduced to tomato cultivation in greenhouses. The overlap of leaves and fruits makes precise observations challenging. In this study, we applied context recognition to tomato growth monitoring by using a Bayesian network. The proposed method combines image recognition using convolutional networks and context recognition using Bayesian networks. It enables not only the recognition of individual tomatoes but also the evaluation of tomato plants. An accurate number of tomatoes and the condition of the stocks can be estimated based on the number of ripe and unripened tomatoes in addition to their density information. The verification experiments clarified that a more accurate number of tomatoes could be estimated than with simple tomato detection, and the stock states could also be evaluated correctly. Compared to conventional methods, the method used in this study has improved tomato decision accuracy by 23%. [ABSTRACT FROM AUTHOR]

Blue sticky traps contribute substantially to monitoring the western flower thrips, Frankliniella occidentalis Pergande (Thysanoptera: Thripidae), in greenhouses. Although sticky traps can detect the initial presence of thrips reliably, an estimation of the actual thrips density in the crop by counting number of thrips on the traps is often not accurate. To overcome this issue, we compared blue sticky traps and newly developed sticky LED-enlightened traps in combination with the commercial thrips kairomone Lurem-TR under commercial growing conditions. Therefore, an experiment was conducted in cucumber, Cucumis sativus L. (Cucurbitaceae), crop stands in greenhouse cabins investigating the correlation between thrips caught on (LED) traps and the thrips density in the crop for an accurate and reliable thrips monitoring. Additionally, experiments aiming to understand underlying mechanisms of thrips orientation towards traps in different scenarios were conducted under controlled conditions. Results show that thrips catches on sticky LED enlightened coloured traps correlated strongly positive with number of thrips in the crop, especially at low thrips population densities. Adding Lurem to this trap type further improved accuracy of the correlation in the greenhouse cabin experiment. Moreover, LED traps with and without Lurem were more attractive towards thrips in small follow-up experiments compared to standard blue sticky traps. The results are discussed in the context of general orientation of thrips and its behaviour towards visual and olfactory cues when considering different scenarios. Our study shows the successful integration of blue LEDs into an existing trapping system and underlines the advantages compared with standard sticky plates. In conclusion, sticky LED enlightened coloured traps have a potential as an improved thrips monitoring device that might improve pest management decisions. [ABSTRACT FROM AUTHOR]

This work explores a new application of titanium nitride nanoparticles (TiN NPs) as efficient photothermal materials in enhancing the greenhouse effect. We demonstrate that a simple greenhouse using TiN NPs-embedded black paint boasts several advantages in solar drying technology, which are indicated by the drying of red chilli. In particular, the greenhouse using TiN NPs significantly improves the drying efficiency, which reduces the mass of red chilli by approximately four times and results in dried chilli with a moisture content of 10% within two days. In addition, by conducting long experiments in various environments, we found that the relative humidity can have a predominant role over the temperature in the solar drying of red chilli and observed that the re-adsorption of moisture can take place during the drying process, which prolongs the drying time and reduces the quality of the dried products. [ABSTRACT FROM AUTHOR]

Salinity, a significant abiotic stressor, imperils vegetable growth, yield, and quality. Moreover, elevated salinity levels, driven by climate change, jeopardize vegetable nutritional quality. In particular, protected cultivation systems, responsible for 60% of the global vegetable industry's economic value, encounter notable challenges in managing salinity due to water runoff and drainage mechanism limitations. Therefore, it is crucial to understand the intricate mechanisms that control salinity and use this knowledge to improve plant tolerance to these conditions. In this study, we explore strategies to effectively mitigate the detrimental impacts of salinity on vegetable crops cultivated within protected environments. Additionally, we investigate the benefits of controlled moderate salinity adjustments in protected cultivation to enhance their nutritional content. Moderate salinity or nutrient solution increases typically raise total soluble solids, sugar, vitamin C, phenols, lycopene, and antioxidants in most fruit vegetables. Though generally applicable to leafy vegetables, exceptions like lettuce and wild rocket may show inconsistencies, potentially reducing some quality traits. Interdisciplinary approaches are essential to developing sustainable solutions for managing salinity in protected cultivation systems, thereby ensuring the resilience of vegetable production in the face of changing environmental conditions. Given the impracticality of desalinating all areas, future research should also investigate synergies between moderate salinity stress, cultivars used, and environmental factors from physiological and molecular perspectives to enhance vegetable nutritional quality. [ABSTRACT FROM AUTHOR]

To mitigate the challenges posed by the confined spatial environment of greenhouses and various obstacles that frequently cause non-line-of-sight (NLOS) communication issues in ultra-wideband (UWB) localization systems, leading to localization difficulties and low accuracy, we propose a real-time greenhouse localization system that recognizes UWB ranging values prior to correction. First, the initial ranging value is obtained through double-sided two-way ranging (DS-TWR). Subsequently, a communication state identifier is designed based on the residual distribution of ranging values across two UWB communication modes. A correction model is then established by analyzing the causes of ranging value deviations. Finally, the NLOS localization deviation is corrected using an improved unscented Kalman filter (IUKF) algorithm. Experimental results in the greenhouse environment demonstrate that the proposed algorithm enhances positioning accuracy by 68% compared to the uncorrected localization method, offering a valuable reference for localization services in greenhouse settings. [ABSTRACT FROM AUTHOR]

Yellow sticky traps (YSTs) are common tools for monitoring the greenhouse whitefly (GWF), Trialeurodes vaporariorum Westwood (Hemiptera: Aleyrodidae), which can cause significant yield reduction in different greenhouse crops such as cucumber and tomato. In recent years, sticky traps equipped with green light-emitting diodes (LEDs) have also been (successfully) tested for catching GWFs. However, no study has observed GWF population dynamics at low population densities using such LED traps for early pest detection in crop stands. Therefore, a greenhouse experiment was conducted aiming to investigate the correlation between GWF populations on tomato crops (Solanum lycopersicum L. (Solanaceae)) and the numbers caught on yellow sticky traps and green LED traps, respectively. A small number of whiteflies was released into two pest-free greenhouse cabins, and populations on plants and traps were monitored for the duration of two months. The results show that the GWFs caught on LED traps correlate significantly positive with the population density on the tomato crops. Such a correlation was not found for standard YSTs. Moreover, the results indicate the possibility of early pest detection using LED traps. The findings are discussed in the context of the whiteflies' ecology and population dynamics in greenhouses. [ABSTRACT FROM AUTHOR]

Analysis of studies on the direction of enrichment of dendroflora of artificial plantations showed the promising potential of Pinus pallasiana (L.) for agroforestry in the southern regions of Russia. The aim is to develop a technology for obtaining planting material with a closed root system taking into account the ecological and biological characteristics of coniferous plants. The accelerated technology of obtaining seedlings of P. pallasiana with a closed root system was developed. The location of research in closed ground conditions (greenhouse, area 252 m²) at the Nizhnevolzhskaya station on the selection of woody species -- branch of the Federal Research Center of Agroecology of the Russian Academy of Sciences (Russia, Volgograd region, Kamyshin; coordinates 50.078957, 45.370560). The research period was from December 2020 to September 2022. Substrate composition, optimal timing and frequency of plant feeding with complex fertilizers, including chlorine-free fertilizers, were recommended. Accelerated terms of cultivation and placement of pine plants for adaptation on the hardening site give a positive effect, which is associated with the economical consumption of seeds, and the safety of seedlings at all stages of cultivation. A technique is proposed that allows the root to avoid deformation, which, under conditions of soil moisture deficit, allows to increase the rooting of the plant. [ABSTRACT FROM AUTHOR]

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Tropical urban greenhouses are prone to heat trapping due to higher daily temperatures compared to temperate climates, and a high energy cost is required to maintain optimal growth conditions. Lanthanide-based nanoparticles are known for their capability of performing upconversion, a process where lower energy photons are absorbed and converted to a single photon of higher energy. These upconversion nanoparticles (UCNPs) possess the innate ability to convert infrared energy to visible light, thereby facilitating efficient heat removal while simultaneously providing upconverted visible light. Consequently, the incorporation of such material into greenhouse façades presents an attractive zero-energy alternative to commonly used greenhouse cooling technologies. In this study, NaYF4:Yb3+/Er3+ UCNPs were synthesized and coated onto a simulated greenhouse enclosure with Pak Choi plants to observe the effect of such a film coating on actual crop growth. A reduction of the interior temperature, increased sunlight diffusion, and improved crop yield were demonstrated in this work, showing promising results for future urban greenhouse façade applications. [ABSTRACT FROM AUTHOR]

The real-time detection of fruits and plants is a crucial aspect of digital agriculture, enhancing farming efficiency and productivity. This study addresses the challenge of embedding a real-time strawberry detection system in a small mobile robot operating within a greenhouse environment. The embedded system is based on the YOLO architecture running in a single GPU card, with the Open Neural Network Exchange (ONNX) representation being employed to accelerate the detection process. The experiments conducted in this study demonstrate that the proposed model achieves a mean average precision (mAP) of over 97%, processing eight frames per second for 512 × 512 pixel images. These results affirm the utility of the proposed approach in detecting strawberry plants in order to optimize the spraying process and avoid inflicting any harm on the plants. The goal of this research is to highlight the potential of integrating advanced detection algorithms into small-scale robotics, providing a viable solution for enhancing precision agriculture practices. [ABSTRACT FROM AUTHOR]

Introduction: The Vision Transformer (ViT) model, which leverages selfsupervised learning, has shown exceptional performance in natural image segmentation, suggesting its extensive potential in visual tasks. However, its effectiveness diminishes in remote sensing due to the varying perspectives of remote sensing images and unique optical properties of features like the translucency of greenhouses. Additionally, the high cost of training Visual Foundation Models (VFMs) from scratch for specific scenes limits their deployment. Methods: This study investigates the feasibility of rapidly deploying VFMs on new tasks by using embedding vectors generated by VFMs as prior knowledge to enhance traditional segmentation models' performance. We implemented this approach to improve the accuracy and robustness of segmentation with the same number of trainable parameters. Comparative experiments were conducted to evaluate the efficiency and effectiveness of this method, especially in the context of greenhouse detection and management. Results: Our findings indicate that the use of embedding vectors facilitates rapid convergence and significantly boosts segmentation accuracy and robustness. Notably, our method achieves or exceeds the performance of traditional segmentation models using only about 40% of the annotated samples. This reduction in the reliance on manual annotation has significant implications for remote sensing applications. Discussion: The application of VFMs in remote sensing tasks, particularly for greenhouse detection and management, demonstrated enhanced segmentation accuracy and reduced dependence on annotated samples. This method adapts more swiftly to different lighting conditions, enabling more precise monitoring of agricultural resources. Our study underscores the potential of VFMs in remote sensing tasks and opens new avenues for the expansive application of these models in diverse downstream tasks. [ABSTRACT FROM AUTHOR]

Introduction: Aquaponics provide multiple benefits due to the simultaneous yield of vegetables and fish, however they are characterized by increased greenhouse gas emissions owing to intensive production system. The most appropriate method for quantifying the environmental effects of these systems is Life Cycle Assessment with which the identification of hotspots and the suggestion of improved production plans can be achieved. The purpose of the present study was to evaluate the environmental impact of a pilot high-tech aquaponic system utilized for the simultaneous production of baby lettuce and rocket as well as rainbow trout, in indicators such as Global Warming Potential. Materials and methods: To achieve this goal, data on inputs and outputs were collected from 12 case studies that were implemented, combining different fertilizer treatments, substrate choices, plant species cultivated and water source provision. Life Cycle Assessment was performed using SimaPro v.9.4.0.2 software. Results: The results showcase that the optimal case studies include the cultivation of baby lettuce and rocket in perlite substrate using wastewater from fish and partial use of synthetic fertilizers. Indicatively, Global Warming Potential of these cases was calculated at 21.18 and 40.59 kg CO2-eq/kg of vegetable respectively. The parameter with the greatest impact on most of the environmental indicators was electricity consumption for the operation of the oxygen supply pump for the fish tanks, while greenhouse infrastructure had the greatest impact in Abiotic Depletion and Human Toxicity impact categories. In an alternative production scenario tested where renewable energy sources were used, system impacts were reduced by up to 50% for Global Warming Potential and 86% for Eutrophication impact. The results of this study aspire to constitute a significant milestone in environmental impact assessments of aquaponic production systems and the adoption of more sustainable farming practices. [ABSTRACT FROM AUTHOR]

In order to solve the immaturity of decision-making methods in the regulation of winter heating in greenhouses, this study proposed a solution to the problem of greenhouse winter heating regulation using a dynamic programming algorithm. A mathematical model that included indoor environmental state variables, optimization decision variables, and outdoor random variables was established. The temperature is kept close to the expected value and the energy consumption is low. The model predicts the control solution by considering the cost function within the next 10 steps. The two-stage planning method was used to optimize the state of each moment step by step. The temperature control strategy model was obtained by training the relationship between indoor temperature, outdoor temperature, and heating time after optimization using a regression algorithm. Based on a typical Internet of Things (IoT) structure, the greenhouse control system was designed to regulate the optimal control according to the feedback of the current environment. Through testing and verification, the optimized control method could stabilize the temperature near the target value. Compared to the threshold control (threshold interval of 2.0°C) under similar weather conditions, the optimized control method reduced the temperature fluctuation range by 0.9°C and saved 7.83 kW·h of electricity, which is about 14.56% of the total experimental electricity consumption. This shows that the dynamic programming method is feasible for environmental regulation in actual greenhouse production, and further research can be expanded in terms of decision variables and policy models to achieve a more comprehensive, scientific, and precise regulation. [ABSTRACT FROM AUTHOR]

Tomato fruit is a source of many important nutrients. It is difficult to store it for a long time because it contains a high percentage of moisture. The moisture content could be reduced in different ways to restrict the high growth of fungi. This study mainly aims to manufacture a simple and easy-to-use solar dryer for drying tomato fruits with solar cell-based fans. This method can be adapted to dry a wide range of Vegetables and fruits. The measured factors in this study are solar radiation, ambient temperature, relative humidity, and drying time. Solar drying is an affordable method to soothe the negative impact of post harvest losses on cultivators in Iraq. A greenhouse dryer (1 × 0.5 × 0.5 m) was constructed using glass of (τ = 0.9 for 0.4 μm < λ < 0.7 μm and τ = 0.01 for λ > 0.7 μm). Two fans are used to force an airstream with an average velocity of 0.025 m/s at the tray section. A selected quantity of tomato was washed and ground, making 1 kg of puree to check the effectiveness of the dryer. An experiment conducted on 10-11 March 2023 showed that 14 drying hours are needed to bring the paste to an acceptable quality of 0.25 brix. The efficiency of the greenhouse has not exceeded 25% on average, accompanied by an average drying rate of 60 g/hr. It was found that converting the perishable tomato crop into paste is profitable for cultivators in Iraqi conditions. Using a solar dryer is particularly profitable for local farmers by reducing crop losses, as per 1 m2 land area, a production of 6 kg of tomatoes is expected with losses of about 1 kg, and a profit of 6 thousand IQD and losses of 1 thousand IQD. A land area of 1 m2 with a dryer produces 3.6 kg of tomatoes converted into 1.008 kg tomato paste producing 15 thousand IQD without losses. [ABSTRACT FROM AUTHOR]