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Legume inclusion into cropping systems has been proposed to maintain high crop yields while offering multiple environmental benefits. However, the effect of legumes as pre-crop on subsequent wheat yield and soil has not been well explored. Thus, a 7-year field experiment was used to determine the interactive effects of mineral fertilization and legumes (peanut, mung bean, soybean, adzuki bean) inclusion on wheat productivity and soil quality. Our results showed that legume inclusion led to a higher wheat yield advantage (52% on average) than maize–wheat rotation under no fertilization but the advantage decreased to 26% with fertilization. All legume–wheat rotation systems supported stable wheat production, where a stronger effect was observed after peanut than after maize. Meanwhile, the wheat yield under legume–wheat systems was more resistant (i.e., less variability in the yield after ceasing fertilization) and more resilient (i.e., recovering more quickly after fertilizer re-application) relative to maize-wheat. Furthermore, soil ecosystem multifunctionality increased by 0.8 times in the topsoil while maintaining soil organic carbon stocks, even with low C and N inputs under legume–wheat. Interestingly, we also observed a positive correlation between wheat yield and soil ecosystem multifunctionality. In conclusion, legume inclusion as a sustainable practice can optimize crop yields by enhancing soil multifunctionality while maintaining soil organic carbon stocks, particularly for integration into low-yielding agroecosystems. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have driven the recent advances in fire detection. However, existing methods require large-scale labeled samples to train data-hungry networks, which are difficult to collect and even more laborious to label. This paper applies unsupervised domain adaptation (UDA) to transfer knowledge from a labeled public fire dataset to another unlabeled one in practical application scenarios for the first time. Then, a transfer learning benchmark dataset called Fire-DA is built from public datasets for fire recognition. Next, the Deep Subdomain Adaptation Network (DSAN) and the Dynamic Adversarial Adaptation Network (DAAN) are experimented on Fire-DA to provide a benchmark result for future transfer learning research in fire recognition. Finally, two transfer tasks are built from Fire-DA to two public forest fire datasets, the aerial forest fire dataset FLAME and the large-scale fire dataset FD-dataset containing forest fire scenarios. Compared with traditional handcrafted feature-based methods and supervised CNNs, DSAN reaches 82.5% performance of the optimal supervised CNN on the testing set of FLAME. In addition, DSAN achieves 95.8% and 83.5% recognition accuracy on the testing set and challenging testing set of FD-dataset, which outperform the optimal supervised CNN by 0.5% and 2.6%, respectively. The experimental results demonstrate that DSAN achieves an impressive performance on FLAME and a new state of the art on FD-dataset without accessing their labels during training, a fundamental step toward unsupervised forest fire recognition for industrial applications. [ABSTRACT FROM AUTHOR]

It is important to reduce the danger of collecting flame image data sets by compositing flame images by computer. In this paper, a Global-Local mask Generative Adversarial Network (FGL-GAN) is proposed to address the current status of low quality composite flame images. First, FGL-GAN adopts a hierarchical Global-Local generator structure, to locally render high-quality flame halo and reflection, while also maintaining a consistent global style. Second, FGL-GAN incorporates the fire mask as part of the input of the generation module, which improves the rendering quality of flame halo and reflection. A new data augmentation technique for flame image compositing is used in the network training process to reconstruct the background and reduce the influence of distractors on the network. Finally, FGL-GAN introduces the idea of contrastive learning to speed up network fitting and reduce blurriness in composite images. Comparative experiments show that the images composited by FGL-GAN have achieved better performance in qualitative and quantitative evaluation than mainstream GAN. Ablation study shows the effectiveness of the hierarchical Global-Local generator structure, fire mask, data augmentation, and MONCE loss of FGL-GAN. Therefore, a large number of new flame images can be composited by FGL-GAN, which can provide extensive test data for fire detection equipment, based on deep learning algorithms. [ABSTRACT FROM AUTHOR]