Search Results (136)

The health of crops is a major challenge for productivity growth in agriculture, with plant diseases playing a key role in limiting crop yield. Identifying and understanding these diseases is crucial to preventing their spread. In particular, greenhouse pepper leaves are susceptible to diseases such as mildew, mites, caterpillars, aphids, and blight, which leave distinctive marks that can be used for disease classification. The study proposes a seven-class classifier for the rapid and accurate diagnosis of pepper diseases, with a primary focus on pre-processing techniques to enhance colour differentiation between green and yellow shades, thereby facilitating easier classification among the classes. A novel algorithm is introduced to improve image vibrancy, contrast, and colour properties. The diagnosis is performed using a modified VGG16Net model, which includes three additional layers for fine-tuning. After initialising on the ImageNet dataset, some layers are frozen to prevent redundant learning. The classification is additionally accelerated by introducing flattened, dense, and dropout layers. The proposed model is tested on a private dataset collected specifically for this study. Notably, this work is the first to focus on diagnosing aphid and caterpillar diseases in peppers. The model achieves an average accuracy of 92.00%, showing promising potential for seven-class deep learning-based disease diagnostics. Misclassifications in the aphid class are primarily due to the limited number of samples available. Full article

Greenhouses are applied to mitigate the deleterious effects of inclement weather, which facilitates the optimal growth and development of the crops. South China has a climate characterized by high temperature and high humidity, and the temperature and relative humidity inside a Venlo greenhouse are higher than those in the atmosphere. In this paper, the numerical model of the flow distribution of a Venlo greenhouse in South China was established using the CFD method, which mainly applied the DO model, the k-e turbulence model, and the porous medium model. The porous resistance characteristics of tomatoes were obtained through experimental research. The inertial resistances of tomato plants in the x, y, and z directions were 80,000,000, 18,000,000, and 120,000,000, respectively; the viscous resistances of tomato plants in the x, y, and z directions were 0.43, 0.60, and 0.63, respectively. The porosity of tomato plants was 0.996. The average difference between the temperature of the established numerical model and the experimental temperature was less than 0.11 °C, and the average relative error was 2.72%. This research also studied the effects of five management and structure parameters on the velocity and temperature distribution in a greenhouse. The optimal inlet velocity is 1.32 m/s, with the COF of velocity and temperature being 9.23% and 1.18%, respectively. The optimal skylight opening is 1.76 m, with the COF of velocity and temperature being 10.68% and 0.88%, respectively. The optimal side window opening is 0.67 m, with the COF of velocity and temperature being 9.25% and 2.10%, respectively. The optimal side window height is 1.18 m, with the COF of velocity and temperature being 9.50% and 1.33%, respectively. The optimal planting interval is 1.40 m, with the COF of velocity and temperature being 15.29% and 0.20%, respectively. The results provide a reference for the design and management of Venlo greenhouses in South China. Full article

The DNDC-Rice model effectively simulates yield and greenhouse gas emissions within a paddy system, while its performance under upland conditions remains unclear. Using data from a long-term cover crop experiment (fallow [FA] vs. rye [RY]) in a soybean field, this study validated the DNDC-Rice model’s performance in simulating soil dynamics, crop growth, and C-N cycling processes in upland systems through various indicators, including soil temperature, water-filled pore space (WFPS), soybean biomass and yield, CO₂ and N₂O fluxes, and soil organic carbon (SOC). Based on simulated results, the underestimation of cumulative N₂O flux (25.6% in FA and 5.1% in RY) was attributed to both underestimated WFPS and the algorithm’s limitations in simulating N₂O emission pulses. Overestimated soybean growth increased respiration, leading to the overestimation of CO₂ flux. Although the model captured trends in SOC stock, the simulated annual values differed from observations (−9.9% to +10.1%), potentially due to sampling errors. These findings indicate that the DNDC-Rice model requires improvements in its N cycling algorithm and crop growth sub-models to improve predictions for upland systems. This study provides validation evidence for applying DNDC-Rice to upland systems and offers direction for improving model simulation in paddy-upland rotation systems, thereby enhancing its applicability in such contexts. Full article

The chlorophyll index (CHI) is a crucial indicator for assessing the photosynthetic capacity and nutritional status of crops. However, traditional methods for measuring CHI, such as chemical extraction and handheld instruments, fall short in meeting the requirements for efficient, non-destructive, and continuous monitoring at the canopy level. This study aimed to explore the feasibility of predicting rice canopy CHI using nighttime multi-source spectral data combined with machine learning models. In this study, ground truth CHI values were obtained using a SPAD-502 chlorophyll meter. Canopy spectral data were acquired under nighttime conditions using a high-throughput phenotyping platform (HTTP) equipped with active light sources in a greenhouse environment. Three types of sensors—multispectral (MS), visible light (RGB), and chlorophyll fluorescence (ChlF)—were employed to collect data across different growth stages of rice, ranging from tillering to maturity. PCA and LASSO regression were applied for dimensionality reduction and feature selection of multi-source spectral variables. Subsequently, CHI prediction models were developed using four machine learning algorithms: support vector regression (SVR), random forest (RF), back-propagation neural network (BPNN), and k-nearest neighbors (KNNs). The predictive performance of individual sensors (MS, RGB, and ChlF) and sensor fusion strategies was evaluated across multiple growth stages. The results demonstrated that sensor fusion models consistently outperformed single-sensor approaches. Notably, during tillering (TI), maturity (MT), and the full growth period (GP), fused models achieved high accuracy (R² > 0.90, RMSE < 2.0). The fusion strategy also showed substantial advantages over single-sensor models during the jointing–heading (JH) and grain-filling (GF) stages. Among the individual sensor types, MS data achieved relatively high accuracy at certain stages, while models based on RGB and ChlF features exhibited weaker performance and lower prediction stability. Overall, the highest prediction accuracy was achieved during the full growth period (GP) using fused spectral data, with an R² of 0.96 and an RMSE of 1.99. This study provides a valuable reference for developing CHI prediction models based on nighttime multi-source spectral data. Full article

The increasing concentration of greenhouse gases is amplifying the global risk of drought on crop productivity. This study sought to investigate the effects of drought on the growth of maize (Zea mays L.) seedlings. A total of 78 maize hybrids were employed in this study to replicate drought conditions through the potting method. The maize seedlings were subjected to a 10-day period of water breakage following a standard watering cycle until they reached the third leaf collar (V3) stage. Parameters including plant height, stem diameter, chlorophyll content, and root number were assessed. The eight phenotypic traits include the fresh and dry weights of both the aboveground and underground parts. Three machine learning methods—random forest (RF), K-nearest neighbor (KNN), and extreme gradient boosting (XGBoost)—were employed to systematically analyze the relevant traits of maize seedlings’ drought tolerance and to assess their predictive performance in this regard. The findings indicated that plant height, aboveground weight, and chlorophyll content constituted the primary indices for phenotyping maize seedlings under drought conditions. The XGBoost model demonstrated optimal performance in the classification (AUC = 0.993) and regression (R² = 0.863) tasks, establishing itself as the most effective prediction model. This study provides a foundation for the feasibility and reliability of screening drought-tolerant maize varieties and refining precision breeding strategies. Full article

This study evaluates various strains of soil bacterial for use in the development of new biopreparations. Mesophilic spore-forming bacteria were isolated from cultivated soil and analysed for their enzymatic activity, ability to decompose crop residues, and antagonistic properties towards selected phytopathogens. Notably, this is the first cytotoxicity assessment of soil bacterial metabolites on Spodoptera frugiperda Sf-9 (fall armyworm). Bacillus subtilis, Bacillus licheniformis, Bacillus velezensis, Paenibacillus amylolyticus, and Prestia megaterium demonstrated the highest hydrolytic potential for the degradation of post-harvest residues from maize, winter barley, and triticale. They exhibited antimicrobial activity against at least three of the tested phytopathogens and demonstrated the ability to solubilize phosphorus. Metabolites of B. licheniformis (IC50 = 8.3 mg/mL) and B. subtilis (IC50 = 144.9 mg/mL) were the most cytotoxic against Sf-9. We recommend the use of the tested strains in industrial practice as biocontrol agents, plant growth biostimulants, crop residue decomposition stimulants, and bioinsecticides. Future studies should focus on assessing the efficacy of using these strains under conditions simulating the target use, such as plant microcosms and greenhouses and the impact of these strains on the abundance and biodiversity of native soil microbiota. This research can serve as a model procedure for screening other strains of bacteria for agricultural purposes. Full article

Agricultural greenhouse gas emissions continue to rise year after year, contributing significantly to global warming—an escalating crisis that demands urgent attention. In order to address this issue, it is crucial to investigate the relationship between greenhouse gas emissions from farmland and crop yield and quality through comprehensive regulation of the soil micro-environment by inputting water, fertilizer, gas, and heat. Therefore, we conducted field experiments in 2024 to examine the effects of different water, fertilizer, gas, and heat conditions on the yield, quality, greenhouse gas emissions, net global warming potential (NGWP), and greenhouse gas emission intensity (GHGI) of processing tomatoes in Xinjiang, China. This study established two irrigation water temperatures (T0: the local irrigation water temperature, approximately 10–15 °C; and T1: warming irrigation, 20–25 °C), two humic acid application rates (H0: 0% and H1: 0.5%, % as a percentage of total fertilizer application), and three aeration methods (A0: no aeration, A1: Venturi aerated, and A2: micro–nano aerated) during the growth period. The results showed that the number of fruits per hectare (NP), vitamin C (VC) content, titratable acidity and lycopene content were all significantly increased with increasing temperature, application of 0.5% humic acid, and aeration. Warming has little effect on GHGI, while humic acid application and aeration have significant and extremely significant effects on GHGI. The GHGI of humic acid treatment was 7.70% lower than that of H0, and the GHGI of micro–nano aeration and Venturi aeration treatment was 18.95% and 6.85% lower than that of A0, respectively. We employed a comprehensive evaluation model that focused on overall differences to assess yield, quality, economic benefits, and environmental impact (GHGI, global warming potential). The optimal strategy identified comprised 20–25 °C irrigation, micro–nano aeration, and 0.5% humic acid, which collectively achieved the highest scores in yield, quality, and emission reduction. This study establishes a theoretical and technical foundation for the sustainable and efficient production of tomatoes in the arid regions of Northern Xinjiang. Full article

China faces the dual challenges of mitigating greenhouse gas emissions and ensuring food security. Given that crop cultivation constitutes a major source of agricultural greenhouse gas emissions, analyzing the emission reduction impact of China’s high-standard farmland construction (HSFC) policy, a crucial food security initiative, holds significant importance. This study calculates greenhouse gas emissions from crop cultivation (CGHGE) from a life cycle assessment (LCA) perspective and evaluates the agricultural new-quality productivity level across 31 regions in China from 2005 to 2022. Subsequently, this study utilizes the continuous difference-in-differences (DID) model to examine the impact of the HSFC policy on CGHGE per unit area. Furthermore, the mediating role of agricultural new-quality productivity in the relationship between HSFC policies and CGHGE per unit area was examined. The results show that HSFC can significantly mitigate the growth of CGHGE per unit area, with an average annual reduction of 62.88%. The regional heterogeneity analysis indicates that HSFC exerts statistically significant negative effects on CGHGE per unit area across both western and eastern China. Furthermore, heterogeneity tests demonstrate that HSFC’s emission reduction effects are particularly pronounced in major grain-producing regions. HSFC contributes to emission reductions by enhancing agricultural new-quality productive forces, which subsequently lead to lower CGHGE. The findings of this study suggest that governments should implement differentiated and targeted policies for HSFC, with particular emphasis on the crucial role of new-quality agricultural productivity in reducing CGHGE. Full article

Silicon application shows potential for enhancing crop resistance to pests while improving productivity. This study evaluated silicon’s effects on agronomic traits and chemical composition of biomass sorghum (Sorghum bicolor) under aphid infestation (Melanaphis sorghi (Theobald, 1904) (Hemiptera: Aphididae)). Greenhouse-grown sorghum (hybrid BRS716) was treated with silicic acid (0, 2, 4, or 6 metric tons per hectare), applied at sowing and the five-leaf stage. Aphid-infested plants were monitored weekly for damage, alongside growth measurements (height, stem diameter, leaf retention). Post-harvest, fresh, and dry biomass were analyzed via near-infrared spectroscopy and chemical assays. Data were assessed using ANOVA and regression models. Results demonstrated that silicon reduced aphid infestation and damage at 6 metric tons per hectare. Silicon also increased cellulose content and improved phosphorus and calcium uptake, though nitrogen and potassium levels decreased. These findings suggest that silicon supplementation can strengthen sorghum’s natural defenses, enhance biomass production, and modify nutrient profiles. This approach offers a sustainable strategy to mitigate aphid damage while maintaining crop yield and quality, with potential applications in integrated pest management systems. Full article

Under the global climate governance framework, the Paris Agreement and the China–U.S. Glasgow Joint Declaration established a non-negotiable target of limiting 21st-century temperature rise to 1.5 °C. To date, over 130 nations have pledged carbon neutrality by mid-century, with agricultural activities contributing 25% of global greenhouse gas (GHG) emissions. The spatiotemporal dynamics of these emissions critically determine the operational efficacy of carbon peaking and neutrality strategies. While China’s Nationally Determined Contributions (NDCs) commit to achieving carbon peaking by 2030, a policy gap persists regarding differentiated implementation pathways at the county level. Addressing this challenge, this study selects the Three Gorges Reservoir (TGRA)—a region characterized by monocultural cropping systems and intensive fertilizer dependency—as a representative case. Guided by IPCC emission accounting protocols, we systematically evaluate spatiotemporal distribution patterns of agricultural CH₄ and N₂O emissions across 18 county-level units from 2006 to 2020. The investigation advances through two sequential phases: Mechanistic drivers analysis: employing the STIRPAT model, we quantify bidirectional effects (positive/negative) of critical determinants—including agricultural mechanization intensity and grain productivity—on CH₄/N₂O emission fluxes. Pathway scenario prediction: We construct three developmental scenarios (low-carbon transition, business-as-usual, and high-resource dependency) integrated with regional planning parameters. This framework enables the identification of optimal peaking chronologies for each county and proposes gradient peaking strategies through spatial zoning, thereby resolving fragmented carbon governance in agrarian counties. Methodologically, we establish a multi-scenario simulation architecture incorporating socioeconomic growth thresholds and agroecological constraints. The derived decision-support system provides empirically grounded solutions for aligning subnational climate actions with global mitigation targets. Full article

Chlorophyll is a key substance in plant photosynthesis, and its content detection methods are of great significance in the field of agricultural AI. These methods provide important technical support for crop growth monitoring, pest and disease identification, and yield prediction, playing a crucial role in improving agricultural productivity and the level of intelligence in farming. This paper aims to explore an efficient and low-cost non-destructive method for detecting chlorophyll content (SPAD) and investigate the feasibility of smartphone image analysis technology in predicting chlorophyll content in greenhouse tomatoes. This study uses greenhouse tomato leaves as the experimental object and analyzes the correlation between chlorophyll content and image color features. First, leaf images are captured using a smartphone, and 42 color features based on the red, green, and blue (R, G, B) color channels are constructed to assess their correlation with chlorophyll content. The experiment selects eight color features most sensitive to chlorophyll content, including B, (2G − R − B)/(2G + R + B), GLA, RGBVI, g, g − b, ExG, and CIVE. Based on this, this study constructs and evaluates the predictive performance of multiple models, including multiple linear regression (MLR), ridge regression (RR), support vector regression (SVR), random forest (RF), and the Stacking ensemble learning model. The experimental results indicate that the Stacking ensemble learning model performs the best in terms of prediction accuracy and stability (R² = 0.8359, RMSE = 0.8748). The study confirms the feasibility of using smartphone image analysis for estimating chlorophyll content, providing a convenient, cost-effective, and efficient technological approach for crop health monitoring and precision agriculture management. This method helps agricultural workers to monitor crop growth in real-time and optimize management decisions. Full article

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. Full article

Soil pore water electrical conductivity (EC), as a comprehensive indicator of soil nutrient status, is closely linked to crop growth and development. Accurate prediction of pore water EC is therefore essential for informed and scientific crop management. This study focuses on a greenhouse rose cultivation site in Jiangchuan District, Yuxi City, Yunnan Province, China. Leveraging multi-parameter sensors deployed within the facility, we collected continuous soil data (temperature, moisture, EC, and pore water EC) and meteorological data (air temperature, humidity, and vapor pressure deficit) from January to December of 2024. We propose a hybrid prediction model—PSO–CNN–LSTM–BOA–XGBoost (PCLBX)—that integrates a particle swarm optimization (PSO)-enhanced convolutional LSTM (CNN–LSTM) with a Bayesian optimization algorithm-tuned XGBoost (BOA–XGBoost). The model utilizes highly correlated environmental variables to forecast soil pore water EC. The experimental results demonstrate that the PCLBX model achieves a mean square error (MSE) of 0.0016, a mean absolute error (MAE) of 0.0288, and a coefficient of determination (R²) of 0.9778. Compared to the CNN–LSTM model, MSE and MAE are reduced by 0.0001 and 0.0014, respectively, with an R² increase of 0.0015. Against the BOA–XGBoost model, PCLBX yields a reduction of 0.0006 in MSE and 0.0061 in MAE, alongside a 0.0077 improvement in R². Furthermore, relative to an equal-weight ensemble of CNN–LSTM and BOA–XGBoost, the PCLBX model shows improved performance, with MSE and MAE decreased by 0.0001 and 0.0005, respectively, and R² increased by 0.0007. These results underscore the superior predictive capability of the PCLBX model over individual and ensemble baselines. By enhancing the accuracy and robustness of soil pore water EC prediction, this model contributes to a deeper understanding of soil physicochemical dynamics and offers a scalable tool for intelligent perception and forecasting. Importantly, it provides agricultural researchers and greenhouse managers with a deployable and generalizable framework for digital, precise, and intelligent management of soil water and nutrients in protected horticulture systems. Full article

In crop growth, irrigation has to be adjusted according to developmental stages. Smart agriculture requires the accurate prediction of growth status and timely intervention to improve the quality of agricultural products, but this task faces significant challenges due to variable environmental factors. To address this issue, this study proposes a data-driven irrigation method to enhance crop yield. Our approach harvests extensive datasets to train and optimize an integrated deep-learning architecture combining Informer, Long Short-Term Memory (LSTM) networks, and Exponential Weighted Moving Average (EWMA) models. Controlled greenhouse experiments validated the reliability and practicality of the proposed prediction and intervention strategy. The results showed that the model accurately issued irrigation warnings 3–5 days in advance. Compared to traditional fixed irrigation, the model significantly reduced irrigation frequency while maintaining the same or even better growth conditions. In terms of plant quantity, the experimental group increased by 410.0%, while the control group grew by 50.0%. Additionally, the experimental group’s average plant height was 21.8% higher than that of the control group. These results demonstrate the efficacy of the proposed irrigation prediction method in enhancing crop growth and yield, providing a novel strategy for future agricultural planning and management. Full article

Greenhouse technologies provide controlled environmental conditions for crop growth, often incorporating automation to enhance productivity. Energy management, which involves monitoring, controlling, and conserving energy, is particularly crucial in northern climates, where greenhouses are among the most energy-intensive sectors of agriculture. This paper presents a comprehensive review of state-of-the-art greenhouse technologies from an energy management perspective, exploring their role in enhancing efficiency and sustainability. It examines the energy management framework, key technological advancements, benefits, challenges, and available solutions in the market. Furthermore, it discusses principles and methods of energy optimization, best practices for sustainable greenhouse operations, and emerging trends in smart grids, renewable integration, and automation. Unlike previous studies primarily focusing on agricultural and control perspectives, this review highlights new insights into integrating greenhouse energy management with smart grid participation, leveraging model predictive control (MPC) for energy optimization, multi-agent reinforcement learning (DRL) for adaptive control, and digital twin technology for real-time system modeling. By bridging greenhouse energy management with transactive energy platforms, this paper underscores the importance of intelligent, data-driven decision-making in enhancing efficiency, sustainability, and system resilience while minimizing environmental impact. Full article