Plant disease forecasting plays an important role in modern crop protection by enabling early disease prediction and supporting optimized management decisions. With the rapid development of digital agriculture, artificial intelligence, and environmental monitoring technologies, forecasting systems have evolved from traditional empirical and mechanistic models to machine learning, deep learning, multi-source data fusion, and hybrid forecasting frameworks. Unlike previous reviews that mainly focused on specific model types, decision support systems, or disease recognition technologies, this review provides a comprehensive synthesis of different forecasting approaches and their practical applications. The strengths and limitations of various models are comparatively analyzed in terms of predictive performance, interpretability, fungicide reduction potential, and practical applicability. In addition, recent advances in climate-driven disease forecasting, precision disease management, and intelligent decision support systems are discussed. Finally, current challenges and future directions, including AI-mechanistic model integration, multi-disease forecasting, IoT and remote sensing data fusion, and climate-adaptive forecasting systems, are highlighted to support the development of sustainable and intelligent crop protection strategies.

Micro Tom tomatoes were cultivated in a plant factory to investigate the effect of discontinuous blue light intervention on the coloration process of tomato fruit grown under red light. Five treatments were set up, namely R (continuous pure red light), R6h/RB2h (pure red light for 6 h and then blue light intervention for 2 h, the same below), R4h/RB4h, R2h/RB6h, RB (simultaneous irradiation of red and blue light). Based on fruit color spectral parameters, the influence of discontinuous blue light on fruit pigmentation was evaluated by measuring pigment contents, key enzyme activities, and the expression levels of related genes. The results showed that: (1) At color break stage (47 days after anthesis, DAA), an increase in the a* (20.7 %) and Red/Green (29.8 %) was observed in tomato fruits exposed to R6h/RB2h compared to R. In contrast, L*, Hue and MCARI were decreased by 9.4 %, 15.3 % and 74.6 % respectively, indicating that the tomato fruits were redder under R6h/RB2h. (2) At color break stage, the contents of lycopene, α-carotene, lutein and zeaxanthin in fruits subjected to R6h/RB2h were increased by 3.1 %, 11.1 %, 3.9 % and 8.8 %, respectively, relative to R, resulting in orange-red tomato fruits. (3) Compared to R, the activities of phytoene synthase, phytoene desaturase, ζ-carotene desaturase, lycopene β-cyclase and lycopene ε-cyclase in fruits under R6h/RB2h were increased by 6.2 %, 9.8 %, 18.5 %, 2.6 % and 1.9 % at color break stage, respectively. Furthermore, the expression of key carotenoid biosynthetic genes GGPS, PSY, PDS, ZDS, LCY-B and LCY-E were upregulated under R6h/RB2h. (4) Correlation analysis revealed that Mg and Ca were positively correlated with PSY, GGPS, PDS, ZDS, and lycopene, suggesting that discontinuous blue light intervention might regulate fruit coloration by affecting the absorption of mineral elements in tomatoes. In summary, in terms of tomato fruit coloration, short-term discontinuous blue light intervention (R6h/RB2h) could maximize the positive effects of single red light and mixed red-blue light on tomato fruit coloration under the same photoperiod. This study provided an effective light supply strategy for regulating the light environment of tomato coloration in industrial production.

Efficient regulation of supplementary lighting in greenhouse tomato production remains a challenge owing to the lack of quantitative decision-making methods and the strong coupling among environmental factors. To address this issue, this study proposes a yield-oriented LED supplementary lighting decision framework for greenhouse tomato production, which directly links the target yield to dynamic lighting regulation under natural light variability. Within this framework, the required annual light integral (ALI) corresponding to a target yield is first determined from the yield-light response relationship via data-driven estimation and marginal effect analysis. This ALI is then allocated to the daily light integral (DLI) at the tomato growth week scale based on dynamic light allocation (Dx), which simultaneously accounts for the stage-specific characteristics of tomato growth (Wy,x) and the phenological variations in natural light availability (Px). In the validation experiment, a target yield of 22 kg center dot m-2 center dot year-1 for cherry tomatoes was set according to actual production. The framework determined the required ALI of 6.92 kmol center dot m-2 center dot year-1 (toplighting) and 3.74 kmol center dot m-2 center dot year-1 (interlighting), which was then allocated to the weekly DLI ranging from 0 to 32 mol center dot m-2 center dot day-1 throughout the growing period. The results demonstrated that compared with the empirically derived supplementary lighting strategy, the strategy based on the proposed decision-making framework increased the yield target achievement rate of cherry tomatoes by 17.3%, enhanced light use efficiency (LUE) by 28.6%, and improved electrical energy use efficiency (EUE) by 40.7%. In addition, the framework incorporates uncertainty handling through error propagation and sensitivity analysis, ensuring stable decision performance under varying environmental conditions. Overall, this study provides a practical and robust decision-support approach for yield-oriented and energy-efficient supplementary lighting regulation in greenhouse production systems.

Supplemental lighting is essential for overcoming low-light stress and enabling overwintering tomato production in greenhouses. This study investigated the effects of LED supplemental lighting with different spectral qualities in the upper and lower canopy on the fruit weight and quality of tomatoes. Six treatments were established: upper-red/lower-blue (RUBL), full red (R), full blue (B), upper-blue/lower-red (BURL), red-blue mixture (RB), and a non-lit control (CK). The results demonstrated that: (1) All supplemental lighting treatments increased tomato fruit weight. During the early overwintering stage (October-December), the highest fruit weight was observed under the RB treatment, representing an increase of 22.62-24.02% compared to CK at the same truss positions. The light gain coefficient (LGC) under RB treatment reached up to 4.41 times that of other treatments. During the later phase (January-February), the BURL treatment achieved the highest LGC, reaching 1.28 to 5.30 times that of other treatments, and it increased the fruit weight by 48.2-72.88% compared to CK. (2) Regarding fruit quality, R and BURL promoted lycopene accumulation the most, followed by RB treatment. Additionally, lycopene was found positively correlated with key color parameters (a, a*/b*, CCI, and C). (3) Compared to CK, all supplemental lighting treatments increased the soluble sugar content in tomato fruits (ranging 5.36 similar to 95.35%), with the highest sugar-acid ratios typically observed under R or BURL treatments. The RB treatment yielded the highest VC levels during the later overwintering stage, exceeding the control by 29.97-39.65%. In summary, for overwintering greenhouse tomato production, application of the RB treatment during the early phase (October to December) and transition to the BURL treatment in the late phase (January to February) could be considered. This phased strategy may help achieve synergistic improvements in yield, fruit coloration, and quality.

As an important link of power engineering, substation low-carbon evaluation research is an inherent requirement for realizing the strategic goal of dual-carbon, and also an important support for promoting the energy revolution. Based on the life cycle evaluation (LCA) theory, this paper summarizes the research progress of low-carbon evaluation in substation, and discusses the evaluation index construction and evaluation method. Firstly, the whole life cycle of substation is decomposed in stages, relevant planning index data is collected, and the low-carbon evaluation index system of substation is screened and constructed through qualitative and quantitative analysis. Then, the appropriate evaluation index model is selected, the weights are determined by combining subjective and objective methods to get substation scores, and the low-carbon situation of the substation is divided into zero carbon, near-zero carbon, low carbon and other levels, and the low-carbon evaluation model of the substation is determined. Provide support for the scientific construction of subsequent substations and power engineering in China, and help the substation project to further reduce carbon.

IntroductionGreenhouse tomato cultivation cycles recognition is often impeded by the long-tailed challenge, arising from significant differences in cycle lengths affecting data distribution. This imbalance hinders accurate recognition, particularly for rare stages, limiting intelligent management in precision agriculture.MethodsThis study proposes a lightweight framework integrating a novel multi-expert grouping strategy with knowledge distillation. The dataset is divided into three groups (Head, Balanced, Tail) based on sample quantity. Separate expert models are trained on each group. Knowledge distillation then transfers the expertise of these models to a lightweight student model (MSC-MobileViT). MSC-MobileViT enhances the MobileViT foundation by incorporating a multi-scale convolution module to improve feature extraction across different scales, capturing both local details and global structure.ResultsExperimental results demonstrate superior performance. The framework achieves an overall accuracy of 95.99%, precision of 91.03%, recall of 93.57%, and F1-score of 92.02%, outperforming state-of-the-art models (ResNet50, MobileNetV3, MobileViT variants). Crucially, it excels in handling tail classes, improving accuracy from 79.27% (baseline) to 93.83% for rare stages like "Substrate Soaking" and "Early Production". The maximum performance gap across categories is minimized to only 3.49 percentage points. The student model achieves this high performance while maintaining an extremely low parameter count (0.95M).DiscussionThe proposed framework effectively addresses the long-tailed recognition challenge in greenhouse tomato cultivation cycles. The multi-expert grouping strategy optimizes learning for different data distributions, while knowledge distillation enables high performance within a lightweight model suitable for edge deployment. The integration of multi-scale convolution significantly enhances feature extraction in complex agricultural scenes. This research provides a new paradigm for long-tail recognition in agriculture and demonstrates the viability of deploying efficient, high-accuracy intelligent systems in real-world greenhouse environments.

The online dynamic collection of irrigation and plant physiological information is crucial for the precise irrigation management of nutrient solutions and efficient crop cultivation in vegetable soilless substrate cultivation facilities. In this study, an intelligent weighing system was installed in a tomato substrate cultivation greenhouse. The monitored values from the intelligent weighing system’s pressure-type module were used to calculate irrigation start–stop times, frequency, volume, drainage volume, drainage rate, evapotranspiration, evapotranspiration rate, and stomatal conductance. In contrast, the monitored values of the suspension-type weighing module were used to calculate the amount of weight change in the plants, which supported the dynamic and quantitative characterization of substrate cultivation irrigation and crop growth based on an intelligent weighing system. The results showed that the monitoring curves of pressure and flow sensors based on the pressure-type module could accurately identify the irrigation start time and number of irrigations and calculate the irrigation volume, drainage volume, and drainage rate. The calculated irrigation amount was closely aligned with that determined by an integrated-water–fertilizer automatic control system (R2 = 0.923; mean absolute error (MAE) = 0.105 mL; root-mean-square error (RMSE) = 0.132 mL). Furthermore, transpiration rate and leaf stomatal conductance were obtained through inversion, and the R2, MAE, and RMSE of the extinction coefficient correction model were 0.820, 0.014 mol·m−2·s−1, and 0.017 mol·m−2·s−1, respectively. Compared to traditional estimation methods, the MAE and RMSE decreased by 12.5% and 15.0%, respectively. The measured values of fruit picking and leaf stripping linearly fitted with the calculated values of the suspended weighing module, and R2, MAE, and RMSE were 0.958, 0.145 g, and 0.143 g, respectively. This indicated that data collection based on the suspension-type weighing module could allow for a dynamic analysis of plant weight changes and fruit yield. In summary, the intelligent weighing system could accurately analyze irrigation information and crop growth physiological indicators under the practical application conditions of facility vegetable substrate cultivation, providing technical support for the precise management of nutrient solutions.

The summer cultivation of lettuce in greenhouses frequently encounters heat stress challenges. In hydroponic systems, cooling the nutrient solution to reduce root zone temperature is an effective strategy to alleviate heat stress. To address the issue of temperature control instability in hydroponic nutrient solutions under high-temperature conditions, this study developed a nutrient solution temperature control system based on an adaptive DBO-fuzzy PID controller. Firstly, the system integrates high-precision sensor networks and air-source heat pump units, forming the hardware foundation. Simultaneously, a fuzzy PID controller optimized by the Dung Beetle Optimizer (DBO) algorithm was designed for this system, enabling real-time adjustment of quantization and scaling factors in the fuzzy controller. Simulation results showed that the DBO-Fuzzy PID achieved a settling time of 35.23 s, overshoot of 2.18%, and steady-state error of 0.009 °C. The DBO-Fuzzy PID controller exhibited faster and more stable disturbance rejection compared to traditional PID and fuzzy PID control, demonstrating enhanced stability and robustness. System performance tests in the summer greenhouse demonstrated that with a setpoint of 22 °C, the DBO-Fuzzy PID optimized nutrient solution temperature control system maintained an average temperature of 21.98 °C, closer to the target value and exhibiting better adaptability to high-temperature environments compared to traditional PID control. Cultivation experiments confirmed the system’s effectiveness in mitigating heat stress and maintaining optimal nutrient solution temperature for lettuce growth. The results can provide a theoretical basis and practical reference for precise and stable temperature control in hydroponic nutrient solutions.

Intercropping and phosphorus application are effective ways to increase crop yield and improve soil quality. However, the effects of intercropping and phosphorus application on rhizosphere soil properties, root morphology, and microbial characteristics are still unclear. This study focuses on the effects of intercropping and phosphorus fertilizer application (180 kg P2O5 ha−1) on the physicochemical properties, enzyme activity, root morphology, and microbial characteristics of rhizosphere soil in a maize–peanut intercropping field planted for 14 years. The results showed that compared with monoculture, intercropping increased the carbon and nutrient contents. Phosphorus fertilizer application further increased the rhizosphere soil nutrient contents. Compared with monoculture, intercropping increased the urease and saccharase by 14.00 and 7.16% in rhizosphere soil, and phosphorus application increased the urease, alkaline phosphatase, and saccharase in rhizosphere soil by 13.38%, 9.75%, and 24.20% compared with no phosphorus application. Compared with monoculture, intercropping increased the root length, root surface area, root volume, and root tip number by 19.17%, 21.57%, 20.74%, and 28.54%, and phosphorus fertilizer application further increased the root length, root surface area, and root volume by 44.66%, 40.20%, and 41.70%. Compared with monoculture, intercropping increased the Chao index and Shannon index of rhizosphere soil bacteria and fungi by 4.29% and 1.63%, and 27.25% and 7.68%. Intercropping and phosphorus application increased the number of edges and modularity of the network of bacterial and fungal communities. To sum up, the intercropping of maize and peanut improved the nutrient contents and enzyme activity of rhizosphere soil, promoted the growth of the root system, and improved the diversity and connectivity of rhizosphere microbial communities, and the application of phosphate fertilizer further optimized the rhizosphere soil microecological environment. The research results provide a theoretical basis for maintaining the stability and sustainable development of the micro-ecosystem in a maize–peanut intercropping field.

To improve the reform effect of the mental health education model in higher vocational colleges, this paper combines big data technology to analyze the mental health education model of higher vocational colleges.It applies the echo state network to higher vocational colleges' mental health education model.This paper focuses on "theme teaching + classroom assessment" to determine the teaching content and emphasizes diversified assessment methods.Moreover, using psychological sitcoms as the carrier, this paper relies on the intelligent learning platform to organically integrate online and offline teaching.In addition, this paper constructs a teaching mode for understanding, mastering, and assessing students' ability to apply basic knowledge of mental health.The research shows that the mental health education model of higher vocational colleges based on the echo network model proposed in this paper can effectively change the mental health education model of higher vocational colleges.

In the plant factories using stereoscopic cultivation systems, the cultivation plate transport equipment is an essential component of production. However, there are problems, such as high labor intensity, low levels of automation, and poor versatility of existing solutions, that can affect the efficiency of cultivation plate transport processes. To address these issues, this study designed a cultivation plate transport system that can automatically input and output cultivation plates, and can flexibly adjust its structure to accommodate different cultivation frame heights. We elucidated the working principles of the transport system and carried out structural design and parameter calculation for the lift cart, input actuator, and output actuator. In the input process, we used dynamic simulation technology to obtain an optimum propulsion speed of 0.3 m·s−1. In the output process, we used finite element numerical simulation technology to verify that the deformation of the cultivation plate and the maximum stress suffered by it could meet the operational requirements. Finally, operation and performance experiments showed that, under the condition of satisfying the allowable amount of positioning error in the horizontal and vertical directions, the horizontal operation speed was 0.2 m·s−1, the maximum positioning error was 2.87 mm, the vertical operation speed was 0.3 m·s−1, and the maximum positioning error was 1.34 mm. Accordingly, the success rate of the transport system was 92.5–96.0%, and the operational efficiency was 176–317 plates/h. These results proved that the transport system could meet the operational requirements and provide feasible solutions for the automation of plant factory transport equipment.

To address the structural concerns of a 12.0 m-span landing assembled single-tube frame (LASF) for Chinese solar greenhouses subjected to snow loads, the internal forces and deformations of LASF and its reinforced counterpart (RLASF) were numerically simulated to determine the ultimate bearing capacities (Lu) and the failure loads (Lf). During the simulations, steel tubes were modeled as beam188 elements and cables as link180 elements. The frame constraints and the connections were assumed to be fixed supports and rigid, respectively. The loads were determined according to the Chinese standard (GB51183-2016). Simulations revealed that the LASF and RLASF primarily withstand bending moments and are prone to strength failures under snow loads. Both exhibited lower Lu and Lf under non-uniform snow loads than under uniform snow loads. The results also indicated that crop loads could deteriorate the structural safety of the LASF and RLASF. Lu and Lf were found to be proportional to the section modulus of the tubes. The effects of wind loads and initial geometry imperfections on Lf of the LASF and RLASF can be neglected. Furthermore, the RLASF exhibited higher Lf compared to the LASF. Steel usage of the RLASF could be further reduced by replacing circular tubes with rectangular tubes, making the RLASF a feasible option for constructing Chinese solar greenhouses.

In order to explore the role of red and blue light in the coloring process of tomato fruit, the micro tomato was planted with rock wool in an artificial light plant factory and irradiated with different lighting modes such as pure red light, combined red and blue light and alternating red/blue light. The reflection spectrum and hue index of tomato fruits at different development stages were analyzed to study the effects of red and blue irradiation modes on the spectral characteristics and coloring of tomato fruit. The results showed that: (1) The alternating irradiation mode of R,IIRB21, (pure red light/combined red and blue light, 6 h/2 h) was the most beneficial to the accumulation of the red pigments (e. g. p-carotene and lycopene) and the decomposition of chlorophyll, finally led to the earlier color conversion of tomato fruit; The effects of R (pure red light) was second only to R6h/RB21,; In contrast, the mixed irradiation mode of RB (combined red and blue light) was not conducive to the accumulation of red pigments and fruit coloring process of tomato fruit. (2) There are both synergistic enhancement effects and signal crosstalk weakening effects between red and blue light in promoting tomato fruit coloring. The alternated irradiation of R and RB may maximize the positive effects of single red light and mixed red and blue light in tomato fruit coloring. (3) The dynamics of pigment content reflected by spectral parameters such as Red/Green, MCARI, PRI of tomato fruit in the color conversion period displayed consistent with the color of tomato fruit reflected by Hue value, the reflection spectral characteristics of tomato fruit in this period were highly unified with peel coloring. The reflection spectrum of tomato fruit in the color conversion period can better reflect the degree and process of tomato fruit coloring.

Determination of relative root-zone water depletion (RRWD) thresholds to trigger irrigation is crucial to create optimal irrigation schedules targeting maximum yield and/or water productivity with limited water supply for a crop. In this study, a numerical procedure to determine RRWD thresholds was developed through coupling AquaCrop software with genetic-simplex algorithms. Using a two-year field lysimetric experiment for winter wheat conducted in the North China Plain (NCP), AquaCrop adequately simulated canopy cover, final aboveground biomass, grain yield, seasonal evapotranspiration, and soil water storage, with the normalized root mean squared error (NRMSE) smaller than 15 % and determination coefficient (R2) larger than 0.84. The global optimum range of RRWD thresholds was preliminarily determined using the genetic algorithm, and subsequently final RRWD thresholds were optimized by fine tuning using the simplex algorithm. The RRWD threshold combinations (composed of the RRWD thresholds to trigger different sequential irrigation events) for varying number of irrigation events (i.e.1–4) were optimized based on 39 years of historical meteorological data, and the effects of climate change on the optimal crop yield (Ya, opt), water productivity (WPopt), and the combinations of optimized RRWD threshold (RRWDopt) were investigated. The results indicated that both Ya, opt and WPopt generally increased with time showing a tendency of gradually elevated annual CO2 concentration and seasonal average effective temperature. Irrespective of the number of irrigation events during the winter wheat growing season, the differences of RRWDopt for different combinations of irrigation sequence and event in the same kind of hydrological year were relatively small, with a coefficient of variation consistently less than 23 % and a mean of 8 %. When combinations of mean RRWDopt were applied into AquaCrop to trigger irrigation for winter wheat in various hydrological years, the simulated yield (Ya, sim) and water productivity (WPsim) under 1–4 irrigation events were found to be comparable to their respective optimums (Ya, opt and WPopt), with all the values of Ya, sim (WPsim) falling in the range of 92 %Ya, opt (90 %WPopt). Therefore, the mean RRWDopt should be helpful to formulate rational irrigation management strategies of winter wheat under changing climatic conditions in the NCP.

IntroductionDue to the shortage of land and water resource, optimization of systems for production in commercial greenhouses is essential for sustainable vegetable supply. The performance of lettuce productivity and the economic benefit in greenhouses using a soil-based system (SBS) and a hydroponic production system (HPS) were compared in this study. MethodsExperiments were conducted in two identical greenhouses over two growth cycles (G1 and G2). Three treatments of irrigation volumes (S1, S2, and S3) were evaluated for SBS while three treatments of nutrient solution concentration (H1, H2, and H3) were evaluated for HPS; the optimal levels from each system were then compared. Results and discussionHPS was more sensitive to the effects of environmental temperature than SBS because of higher soil buffer capacity. Compared with SBS, higher yield (more than 134%) and higher water productivity (more than 50%) were observed in HPS. We detected significant increases in ascorbic acid by 28.31% and 16.67% and in soluble sugar by 57.84% and 32.23% during G1 and G2, respectively, compared with SBS. However, nitrate accumulated in HPS-grown lettuce. When the nutrient solution was replaced with fresh water 3 days before harvest, the excess nitrate content of harvested lettuce in HPS was removed. The initial investment and total operating cost in HPS were 21.76 times and 47.09% higher than those in SBS, respectively. Consideration of agronomic, quality, and economic indicators showed an overall optimal performance of the H2 treatment. These findings indicated that, in spite of its higher initial investment and requirement of advanced technology and management, HPS was more profitable than SBS for commercial lettuce production.

To investigate the effects of extended light/dark (L/D) cycle period (relative to the diurnal L/D cycle) on lettuce and explore potential advantages of abnormal L/D cycles, butter leaf lettuce were grown in a plant factory with artificial light (PFAL) and exposed to mixed red (R) and blue (B) LED light with different L/D cycles that were respectively 16 h light/8 h dark (L16/D8, as control), L24/D12, L48/D24, L96/D48 and L120/D60. The results showed that, all the abnormal L/D cycles increased shoot dry weight (DW) of lettuce (by 34–83%) compared with the control, and lettuce DW increased with the L/D cycle period prolonged. The contents of soluble sugar and crude fiber in lettuce showed an overall upward trend with the length of L/D cycle extended, and the highest vitamin C content as well as low nitrate content were both detected in lettuce treated with L120/D60. The light use efficiency (LUE) and electric use efficiency (EUE) of lettuce reached the maximum (respectively 5.37% and 1.76%) under L120/D60 treatment and so were DW, Assimilation rate (A), RC/CS, ABS/CS, TRo/CS and DIo/CS, indicating that longer L/D cycle period was beneficial for the assimilation efficiency and dry matter accumulation in lettuce leaves. The highest shoot fresh weight (FW) and nitrate content detected in lettuce subjected to L24/D12 may be related to the vigorous growth of root, specific L/D cycle seemed to strengthen root growth and water absorption of lettuce. The openness level of RC in PSII (Ψo), ETo/CS, and PIabs were all the highest in lettuce treated with L24/D12, implying that slightly extending the L/D cycle period might promote the energy flowing to the final electron transfer chain. In general, irradiation modes with extended L/D cycle period had the potential to improve energy use efficiency and biomass of lettuce in PFAL. No obvious stress or injury was detected in lettuce subjected to prolonged L/D cycles in terms of plant growth and production. From the perspective of shoot FW, the optimal treatment in this study was L24/D12, while L120/D60 was the recommended treatment as regards of the energy use efficiency and nutritional quality.

Knowledge regarding uptake of water and nutrients as a function of their status in the soil is critical for smart fertigation management. Of particular interest is the uptake of water and potassium (K), each as a function of root zone salinity. The objective of this study was to quantify the response of tomato water uptake (transpiration) and K uptake to varied levels of K availability combined with salinity. Two independent lysimetric experiments were conducted and used to calibrate and validate models for water and K uptake under varied soil salinity. Tomato water and K uptake were determined by water and nutrient balance using the measured soil water content and K concentration in soil and drainage solution. Tomato water uptake was affected by root zone soil K and salinity. Salinity was the dominant factor driving uptake when irrigation solution had NaCl concentration of over 3 g L–1. Potassium uptake of tomato decreased with decreasing soil K content and increasing soil salinity. The linear relationship between tomato water uptake and K uptake rate was not influenced by soil salinity, indicating that the inhibition of K uptake was probably due to passive uptake of K with the flux of water from soil to roots decreased due to salinity. Tomato water and K uptake were simulated considering the effect of soil solution K concentration under simultaneous K and salinity stresses. Simulated daily average water and K uptake rates agreed well with measured values, with root mean squared error, normalized root mean squared error, and index of agreement of 144 cm3 d–1, 20.13% and 0.99 for average daily water uptake; and 24.43 mg d–1, 29.78% and 0.98 for K average daily uptake rate, respectively. These findings can be used to predict crop water and K requirements under combined salinity and K status conditions, which should contribute to efficient and sustainable fertigation scheduling.

随着农业4.0的到来,物联网、机器视觉、机器制造、自动化技术等新技术在设施农业领域的应用已经成为近几年的研究热点,农业生产也迎来装备化、无人化的发展阶段.针对我国设施生菜生产装备应用还处于发展阶段,总体生产装备应用不广泛的现象,阐述我国设施生菜生产及其机械化管理的必要性,全面梳理从播种、种苗培育、幼苗定植、田间管理到收获运输5个环节作业装备的应用现状,提出未来设施生菜生产装备在各环节智能控制算法、流水线作业、无人化管理、融合大数据平台等方向的发展趋势,以期为我国设施生莱生产过程智能化、无人化发展提供建议.

Lettuce (Lactuca sativa) was cultured in a completely enclosed plant factory and irradiated with an intelligent LED light system with accurate regulation of spectral space-time distribution. Absorption and content of eight mineral elements such as K, P, Ca, Mg, Fe, Mn, Zn and Cu in Lactuca sativa exposed to alternating red and blue light spectrum with different alternating intervals were studied by ICP-AES technology. The results showed that; (1) Compared with concurrent red and blue light spectrum, all the alternating spectrum treatments increased shoot biomass of lettuce, the increasing range of fresh weight and dry weight was respectively 18.6%similar to 67.4% and 5.1%similar to 88.0%. All the alternating spectrum treatments significantly increased the accumulation of Mg, Fe and Zn in the lettuce plant (p<0.05); all the alternating spectrum treatments decreased the content of Ca in lettuce to different extents. (2) Fe content in lettuce subjected to R/B(5 m) was significantly higher than any other treatment, increasing by 38.87%similar to 85.37% compared with the other treatments. Alternating red and blue light spectrum with high alternating frequency seemed to stimulate Fe uptake by lettuce plants. (3) Alternating red and blue light spectrum enhanced the use efficiency of light photons and electricity by lettuce. Compared with RB, all alternating treatments significantly improved the light and electric use efficiency of leaf lettuce by about 34.3%similar to 87.5% and 34.6%similar to 87.9%; Among them, the light and electric use efficiency of leaf lettuce were both the largest under the alternating treatment with an interval of 4 h, which were 6.13% and 2.01% respectively. Except for the alternating treatments with the intervals of 5 m and 10 m, no significant difference existed in the light and electric use efficiency of plants among the alternating spectrum treatments. (4) The absorption of K and Mg by lettuce showed antagonism under the alternating intervals of 10m, 15 m, 60 m and 4 h. (5) The contents of P, Ca, Fe and Mn in lettuce treated with R/B(30 m) showed the lowest level among treatments, and the contents of P and Ca were significantly lower than the control.

Sucrose metabolism and carbohydrate accumulation were evaluated in butterhead lettuce cultured under alternating red light (R) and blue light (B) irradiance. The R/B treatments were respectively R/B(5 m) (i.e. R 5 min/B 5 min), R/B(10 m), R/B(15 m), R/B(30 m) and R/B(60 m). Meanwhile, concurrent R and B (RB) as well as monochromatic R (R) and monochromatic B (B) were conducted. Compared with concurrent RB, all the alternating R/B treatments increased the fresh weight (FW), dry weight (DW) and the content of pigment and soluble sugar in lettuce on the basis of the same daily photon amount and electric energy consumption. Lettuce exposed to R/B(30 m) required the least number of photons (1.82 mu mol) and electricity (1.04 MJ) per gram DW produced among all the treatments, thus R/B(30 m) was the recommended lighting mode in terms of the use efficiency of photons and electricity. Relatively higher soluble sugar content and the total sweetness index (TSI), as well as lower crude fiber content were detected in lettuce subjected to R/B(60 m), which was regarded as optimal irradiation mode in terms of lettuce taste. The content of sucrose, glucose and fructose, and the activities of sucrose phosphate synthase (SPS) and sucrose synthase (SS) in the synthesis direction, as well as the gene expression level of SPS in lettuce were all enhanced by the alternating irradiation of R/B (15 m), R/B (30 m) and R/B (60 m) in comparison to the concurrent RB treatment. Moreover, higher enzyme activities in the synthetic direction of sucrose were found to be accompanied with higher soluble sugar content in lettuce subjected to R/B (30 m), R/B (60 m) and the solely R, indicating that the enzyme activities in sucrose synthetic direction seemed more related to the soluble sugar accumulation in lettuce. The results also showed that SS in lettuce leaves mainly acted on sucrose decomposition rather than synthesis. The hexose (glucose and fructose) content of lettuce subjected to R/B(60 m) basically kept the highest level among all the treatments through the whole processing period, which might be due to that lettuce exposed to R/B (60 m) owned the highest gene expression level of SS and invertase, as well as the highest enzyme activities of SS (cleavage) and invertase among all the treatments. Solely B inhibited the gene expression of sucrose metabolism-related enzymes in lettuce, and the photon use efficiency to R by lettuce was higher than to B from the angle of biomass and soluble sugar accumulation. A low intensity of B (1/9 of the R level) alternating with R improved the photon and electric use efficiency of lettuce in this study.