Rational irrigation and nitrogen management strategies are crucial for wheat growth. However, the optimal amount of water and nitrogen for the newly developed drip irrigated spring wheat system (TR6S, one drip tube service for six rows of wheat, with a row spacing of 10 cm and an inter-block space of 25 cm, saves drip tubes and obtains higher profits) in dry and semi-arid areas remains unclear. Therefore, a field experiment was conducted with four nitrogen levels (300, 270, 240, and 0 kg ha⁻¹ referred N300, N270, N240, and N0) and four irrigation levels (4500, 4200, 3900, and 3600 m³ ha⁻¹ referred I4500, I4200, I3900, and I3600) during the 2021–2022 and 2022–2023 spring wheat seasons to analyze the effects of irrigation (I) and nitrogen (N) levels on grain yield, water-nitrogen use efficiency, profit, biomass accumulation, and nitrogen nutrient absorption status under TR6S. Compared with the traditional irrigation and nitrogen management strategy (N300–I4500, as control), lesser irrigation and nitrogen supply (I<3979 m³ ha⁻¹ and NI>3979 m³ ha⁻¹ and 300 kg ha⁻¹ >N>273 kg ha⁻¹) improved grain yield, WUE, AEN, and profit. The increase in grain yield was mainly related to the rise in 1000-grain weight and kernels per spike. Although the moderate reduction in irrigation lowered soil moisture status, the dry matter pre-stored in the vegetative organs before anthesis that gets redistributed into grains during grain filling was improved. Moreover, the moderate reduction in nitrogen supply resulted in a more reasonable nitrogen nutrition index (NNI) of wheat plant, which improved flag leaf area and chlorophyll relative content (SPAD) at the anthesis stage. This also played a positive role in biomass accumulation and redistributed, yield structure optimization. Considering comprehensively yield, WUE, AEN and profit, combination of 285 kg ha⁻¹ N and 4170 m³ ha⁻¹ I was optimal irrigation and nitrogen application pattern for TR6S. This strategy can be applied to other arid and semi-arid regions.

The occurrence of cucumber downy mildew in solar greenhouses directly affects the yield and quality of cucumber. Chemical control methods may cause excessive pesticide residues, endanger food quality and safety, pollute the ecological environment, etc. Therefore, it is very important to predict the disease before its occurrence. To provide farmers with better and effective guidance for the prevention and control work, minimize the loss of disease damage, this article took cucumber ‘Lyujingling No. 2′ as the experimental material and acquired greenhouse environmental factors data by wireless sensors, including Temp (Temperature), RH (Relative Humidity), ST (Soil Temperature) and SR (Solar Radiation). LSTM (Long Short-Term Memory) neural network structure was constructed based on Keras deep learning framework to develop a prediction model with time-series environmental factors. Combined with the occurrence of downy mildew from manual investigation and statistics, through debugging the parameters, this article developed an occurrence prediction model for cucumber downy mildew and compared it with KNN (K-Nearest Neighbors Classification) and ANN (Artificial Neural Network). In the prediction model, the forecasted results of the four environmental factors were consistent with the true value distributions, and R² (R-Squared) were all above 0.95. Among them, the ST variable predicted the best results, e.g., R² = 0.9982, RMSE (Root Mean Square Error) = 0.08 °C, and MAE (Mean Absolute Error) = 0.05 °C. In the disease occurrence prediction model, the training accuracy was 95.99%, the Loss value was 0.0159, the disease occurrence prediction Accuracy was 90%, Precision was 94%, Recall was 89%, F₁-score was 91%, the AUC (Area Under Curve) value was 90.15%, and Kappa coefficient was 0.80. It also had obvious advantages over other different models. In summary, the model had a high classification accuracy and performance, and it can provide a reference for the occurrence prediction of cucumber downy mildew in actual production.

Guizhou province is the largest area of hot pepper cultivation and processing in China. However, diseases and pests are major bottlenecks for the sustainable development of the industry. This study proposes a solution that combines intelligent monitoring equipment, a prediction model and decision support system for hot peppers, including a demonstration of the solution in Guizhou province. We scouted hot pepper diseases and pests in Zunyi city, and deployed weather stations and automatic pathogens and pest monitoring equipment. A prediction model was developed to forecast powdery mildew and anthracnose based on long short-term memory, with accuracy of 0.74 and 0.68, respectively. Using big data analysis and an app for pest outbreaks as the front desks, we developed an epidemic monitoring and warning system for hot peppers in Guizhou. The results could effectively serve grass-roots managers, increase productivity, reduce production costs and overall have a high demonstration effect. This concept could be extended to other crops to accelerate the process of agricultural modernization in China.

Timely and accurate judgment of the nitrogen nutritional status of crops is the key to develop an optimal nitrogen application strategy. However, the evaluation criteria of nitrogen nutrition and nitrogen application strategies at each growth stage of wheat are not clear for the new type of drip-irrigated spring wheat system, TR6S (where one drip tube serves six rows of wheat, with a row spacing (RS) of 10 cm, inter-block space (IBS) of 25 cm and the lateral spacing (LS) of 80 cm, which achieved a lower drip-tube input and higher profit compared with the traditional planting system in Xinjiang). Therefore, we studied the recommendation mechanism of nitrogen fertilizer in different growth stages of wheat based on the critical SPAD values of leaves under TR6S. We set four nitrogen treatments (N1 (300 kg ha⁻¹), N2 (270 kg ha⁻¹), N3 (240 kg ha⁻¹) and N4 (0 kg ha⁻¹)) during two spring wheat growth seasons. The results revealed that the correlation coefficient (r²) between SPAD (soil plant analysis development) value and plant nitrogen content in the middle of first top leaf (L1-M) of wheat was higher than that in other leaf types and leaf positions under TR6S. A quadratic function relationship existed between a SPAD value of L1-M and grain yield. The critical SPAD values at the jointing, booting, anthesis, early milk, and late milk stages were 37.34, 39.40, 42.25, 45.57, and 35.91, respectively. In addition, through the establishment of the nitrogen application recommendation model for various wheat growth stages based on the critical SPAD value, the recommended optimal nitrogen application rates at jointing, booting, anthesis, early milk, and late milk stages were observed to be 69.4, 80.0, 90.8, 44.0, and 6.0 kg ha⁻¹, respectively. This recommended nitrogen application strategy exhibited a better parallel relationship with the nitrogen nutrition index (NNI) of each growth period than the conventional nitrogen application strategy. Therefore, it was more in line with the actual absorption and utilization of nitrogen in wheat of TR6S. In conclusion, the SPAD values of L1-M could be relatively more accurate to evaluate the nitrogen nutrition status of wheat. Compared to traditional nitrogen application strategy, reducing and delaying nitrogen application, recommended based on the leaf SPAD model, was more suitable for nitrogen utilization under TR6S. The results can be applied in other arid and semiarid regions.