The governance of rural living environments is one of the important tasks in the implementation of a rural revitalization strategy. At present, the illegal behaviors of random construction and random storage in public spaces have seriously affected the effectiveness of the governance of rural living environments. The current supervision on such problems mainly relies on manual inspection. Due to the large number and wide distribution of rural areas to be inspected, this method is limited by obvious disadvantages, such as low detection efficiency, long-time spending, and huge consumption of human resources, so it is difficult to meet the requirements of efficient and accurate inspection. In response to the difficulties encountered, a low-altitude remote sensing inspection method on rural living environments was proposed based on a modified YOLOv5s-ViT (YOLOv5s-Vision Transformer) in this paper. First, the BottleNeck structure was modified to enhance the multi-scale feature capture capability of the model. Then, the SimAM attention mechanism module was embedded to intensify the model’s attention to key features without increasing the number of parameters. Finally, the Vision Transformer component was incorporated to improve the model’s ability to perceive global features in the image. The testing results of the established model showed that, compared with the original YOLOv5 network, the Precision, Recall, and mAP of the modified YOLOv5s-ViT model improved by 2.2%, 11.5%, and 6.5%, respectively; the total number of parameters was reduced by 68.4%; and the computation volume was reduced by 83.3%. Relative to other mainstream detection models, YOLOv5s-ViT achieved a good balance between detection performance and model complexity. This study provides new ideas for improving the digital capability of the governance of rural living environments.

The knowledge of crop water requirements is critical for agricultural water conservation, especially for accurate irrigation decision making in the greenhouse. Investigating the water demand pattern of the tomato in the solar greenhouse environment and constructing an appropriate irrigation decision-making model are urgently needed to improve irrigation water use efficiency. We designed four irrigation-level treatments: 100% ET₀ (T1), 85% ET₀ (T2), 70% ET₀ (T3), and 55% ET₀ (T4), and conducted a two-vegetation-season tomato planting trial under drip irrigation conditions in a solar greenhouse. The Pearson’s correlation coefficient method analyzed the intrinsic linkage and influence between soil–crop–environment and tomatoes’ water demand patterns. Indicators suitable for irrigation decision making in greenhouse tomatoes were selected, and regression functions were constructed for environmental and crop physiological parameters by combining path analysis and multiple regression methods. Finally, a fusion irrigation decision-making model was constructed by introducing a distance function in the Dempster–Shafer (D–S) theory primary probability assignment (BPA) synthesis algorithm and combining it with a triangular affiliation function. The results showed that: (1) the soil coefficient of variation was shallow > middle > deep, and tomatoes absorbed water mainly in the 0–60 cm soil layer; (2) the crop stem flow rate, net photosynthetic rate, and transpiration rate were positively correlated with irrigation water and had the highest correlation with net radiation, relative humidity, and relative humidity, with correlation coefficients of 0.9441, 0.9441, and 0.7679, respectively; (3) the constructed decision model had a significantly lower value of uncertainty than other methods, while the highest decision value could reach over 0.99, which achieved the best decision accuracy compared to other algorithms.

The production efficiency and quality of tomatoes is affected by the mode of irrigation and the nitrogen forms. This study explored the impacts of different irrigation regimes, nitrogen forms, and their coupled effects on tomato production. The various irrigation regimes were set at 50%FC~90%FC (W1), 60%FC~90%FC (W2), 70%FC~90%FC (W3), and 80%FC~90%FC (W4) Furthermore, the control (CK) group followed a conventional drip irrigation regime in the local area. Nitrogen forms in this study comprised urea-based fertilizer (urea N 32%, F1), nitrate-based fertilizer (calcium ammonium nitrate N 15%, F2), and ammonium-based fertilizer (ammonium sulfate N 21%, F3). Combining these two factors yielded 15 treatment groups. The experiment was conducted in a solar greenhouse, and the soil type was sandy loam soil. The research focused on observing the yield, quality, and water–fertilizer use efficiency of tomatoes under these 15 treatment groups. The results demonstrate that irrigation had a more significant impact on the yield and nutrient accumulation rate compared to the nitrogen forms. To comprehensively evaluate the yield, quality, and water–fertilizer use efficiency of tomatoes, a combination evaluation method was employed. W3F2 produced the highest yield, CKF2 achieved the highest comprehensive quality score, and W2F2 had the highest comprehensive water and fertilizer use efficiency score. Using the fuzzy Borda model, the evaluation information of the three dimensions was combined. W3F2 ranked first, suggesting the adoption of an irrigation control regime of 70%FC to 90%FC, along with the application of nitrate-based nitrogen fertilizer during the fruit set to the harvest stage. It presented the best performance of tomato yield, quality, and water–fertilizer use efficiency across multiple dimensions.

Greenhouse tomatoes (Solanum lycopersicum) are a high-value cash crop globally; however, inefficient water and nitrogen management in greenhouse systems often leads to resource waste, environmental risks, and reduced yield/quality, thereby posing challenges for sustainable cultivation. Herein, we aimed to optimize water and fertilizer management for spring greenhouse tomatoes by investigating how different irrigation lower limits, combined with reduced nitrogen and organic fertilizer, affect photosynthetic performance, yield, fruit quality, and water–nitrogen use efficiency. A three-factor experiment was conducted in 2024. Treatments included two irrigation levels (L) [75–90% field capacity (FC; L1) and 85–90% FC (L2), where FC refers to the soil field capacity]; two basal organic fertilizer application rates (O) [high (O1: 400 kg decomposed sheep manure) and low (O2: 280 kg decomposed sheep manure)]; and three topdressing nitrate-nitrogen levels (N) [conventional (N1: 352.14 kg·hm ⁻ ²), 20% reduced (N2: 281.72 kg·hm ⁻ ²), and 40% reduced (N3: 211.29 kg·hm ⁻ ²)]. Data were analyzed using three-way ANOVA, principal component analysis (for photosynthetic and quality traits), and entropy-weighted TOPSIS (comprehensive evaluation). The first-level indicators in the system included photosynthetic indicators (F1), yield indicators (F2), quality indicators (F3), water use indicators (F4), partial factor productivity of nitrogen (F5), and nitrogen–phosphorus–potassium accumulation (F6). The results revealed that the treatments L2, N3 and O2 can increase the average yield of tomatoes, and the combination of L2 (85%–90% FC), O2 (low organic fertilizer), and N2 (20% reduced nitrogen) not only displayed the highest levels of vitamin C (21.46 mg/100g) and soluble solids content (5.62%) in fruits but also exhibited the highest total nitrogen content in fruits, stems, and leaves across all treatments, surpassing the lowest-performing treatment by 62.83%. Consequently, this treatment yielded the highest comprehensive performance.

Greenhouse tomatoes (Solanum lycopersicum) are a high-value cash crop globally; however, inefficient water and nitrogen management in greenhouse systems often leads to resource waste, environmental risks, and reduced yield/quality, thereby posing challenges for sustainable cultivation. Herein, we aimed to optimize water and fertilizer management for spring greenhouse tomatoes by investigating how different irrigation lower limits, combined with reduced nitrogen and organic fertilizer, affect photosynthetic performance, yield, fruit quality, and water–nitrogen use efficiency. A three-factor experiment was conducted in 2024. Treatments included two irrigation levels (L) [75–90% field capacity (FC; L1) and 85–90% FC (L2), where FC refers to the soil field capacity]; two basal organic fertilizer application rates (O) [high (O1: 400 kg decomposed sheep manure) and low (O2: 280 kg decomposed sheep manure)]; and three topdressing nitrate-nitrogen levels (N) [conventional (N1: 352.14 kg·hm ⁻ ²), 20% reduced (N2: 281.72 kg·hm ⁻ ²), and 40% reduced (N3: 211.29 kg·hm ⁻ ²)]. Data were analyzed using three-way ANOVA, principal component analysis (for photosynthetic and quality traits), and entropy-weighted TOPSIS (comprehensive evaluation). The first-level indicators in the system included photosynthetic indicators (F1), yield indicators (F2), quality indicators (F3), water use indicators (F4), partial factor productivity of nitrogen (F5), and nitrogen–phosphorus–potassium accumulation (F6). The results revealed that the treatments L2, N3 and O2 can increase the average yield of tomatoes, and the combination of L2 (85%–90% FC), O2 (low organic fertilizer), and N2 (20% reduced nitrogen) not only displayed the highest levels of vitamin C (21.46 mg/100g) and soluble solids content (5.62%) in fruits but also exhibited the highest total nitrogen content in fruits, stems, and leaves across all treatments, surpassing the lowest-performing treatment by 62.83%. Consequently, this treatment yielded the highest comprehensive performance.

Greenhouse tomatoes (Solanum lycopersicum) are a high-value cash crop globally; however, inefficient water and nitrogen management in greenhouse systems often leads to resource waste, environmental risks, and reduced yield/quality, thereby posing challenges for sustainable cultivation. Herein, we aimed to optimize water and fertilizer management for spring greenhouse tomatoes by investigating how different irrigation lower limits, combined with reduced nitrogen and organic fertilizer, affect photosynthetic performance, yield, fruit quality, and water–nitrogen use efficiency. A three-factor experiment was conducted in 2024. Treatments included two irrigation levels (L) [75–90% field capacity (FC; L1) and 85–90% FC (L2), where FC refers to the soil field capacity]; two basal organic fertilizer application rates (O) [high (O1: 400 kg decomposed sheep manure) and low (O2: 280 kg decomposed sheep manure)]; and three topdressing nitrate-nitrogen levels (N) [conventional (N1: 352.14 kg·hm ⁻ ²), 20% reduced (N2: 281.72 kg·hm ⁻ ²), and 40% reduced (N3: 211.29 kg·hm ⁻ ²)]. Data were analyzed using three-way ANOVA, principal component analysis (for photosynthetic and quality traits), and entropy-weighted TOPSIS (comprehensive evaluation). The first-level indicators in the system included photosynthetic indicators (F1), yield indicators (F2), quality indicators (F3), water use indicators (F4), partial factor productivity of nitrogen (F5), and nitrogen–phosphorus–potassium accumulation (F6). The results revealed that the treatments L2, N3 and O2 can increase the average yield of tomatoes, and the combination of L2 (85%–90% FC), O2 (low organic fertilizer), and N2 (20% reduced nitrogen) not only displayed the highest levels of vitamin C (21.46 mg/100g) and soluble solids content (5.62%) in fruits but also exhibited the highest total nitrogen content in fruits, stems, and leaves across all treatments, surpassing the lowest-performing treatment by 62.83%. Consequently, this treatment yielded the highest comprehensive performance.