Yiting Cai

For many countries, the reclamation of coastal saline–alkali land is of great significance for ensuring food security. However, systematic research on the effects of leaching irrigation and biochar application on the “soil–crop” system in coastal areas remains scarce. This study investigated the effects of three irrigation quotas (8, 16, and 24 mm per application, applied once every 10 days) and four biochar rates (0, 3, 5, 7 t·ha⁻¹, from composite biomass) on soil salinity, available nutrients, and yield and quality of sunflower in coastal saline–alkali soils of Jiangsu, China. The experiment used a completely randomized block design with three replications. The results showed that soil salinity under all treatments exhibited a consistent decline–rebound–decline pattern over the growing season, with the lowest values occurring around 55 days after sowing and peaking around 95 days. The combined application of water and biochar enhanced salt reduction, with the highest irrigation and biochar levels achieving the greatest desalination effect. Biochar application increased soil available phosphorus and potassium contents, with average increases of 19.6% and 13.3% under the highest biochar rate compared with the control, while available nitrogen showed no clear response to any treatment. Crop yield responded nonlinearly to water and biochar inputs: under low irrigation, yield increased continuously with biochar addition, whereas under medium or high irrigation, yield plateaued at the moderate biochar rate (5 t·ha⁻¹), with no further gains from additional biochar or water. Regarding seed quality, linoleic acid content generally increased with higher water and biochar levels, oleic acid decreased, and crude fat remained unaffected. Based on a balanced consideration of yield, quality, salt reduction, and water conservation, the combination of 16 mm irrigation per application and 5 t·ha⁻¹ biochar is recommended as the optimal amelioration strategy for coastal saline–alkali land.

Early and accurate identification of tomato leaf diseases constitutes a key safeguard for mitigating economic losses in tomato production. Conventional tomato leaf disease detection methodologies are constrained by inherent limitations, such as low operational efficiency, inadequate detection precision, and limited adaptability to environmental fluctuations. In contrast, the integration of deep learning techniques has yielded improvements in this research domain. Consequently, the development of deep learning-based approaches for the rapid and precise detection of tomato leaf diseases holds considerable theoretical significance and practical application value. To improve the detection accuracy of tomato leaf diseases, this study proposes a transfer learning-based DenseNet disease recognition model named DenseNet-BiFPN-ECA Fusion Network. The bidirectional feature pyramid network (BiFPN) is introduced at the terminal of DenseNet121 to achieve multi-scale feature fusion, while the efficient channel attention (ECA) mechanism is applied to enhance the discriminative capacity of fused features. Classification is ultimately completed via a global average pooling layer and a fully connected layer. The experimental results demonstrate that the improved model achieves an accuracy of 90.63% on the small-sample tomato leaf dataset collected from complex greenhouse environments, representing an improvement of 20.32 percentage points over the original DenseNet121 model. On the large-scale open-source Plant Village dataset, the model attains an accuracy of 98.47%, significantly outperforming the baseline models. Furthermore, a comparative analysis shows that the highest accuracy achieved by DenseNet, ResNet101, and VGG16 models on the same dataset is only 83.59% (within ±0.5%). This result validates the effectiveness of DenseNet-BiFPN-ECA Fusion Network in disease recognition tasks. The model provides a reliable technical reference for the intelligent diagnosis of tomato leaf diseases.