Agronomy

Rapid and accurate identification of crop leaf diseases is essential for informed agricultural decision-making. However, achieving reliable classification remains challenging under conditions such as extreme lighting, complex color variations, and intricate structural backgrounds, particularly when early-stage symptoms are subtle and easily masked by surrounding tissues. To address these challenges, this study proposes a novel network architecture, SREM-Net, which incorporates stylistic and multiscale feature extraction strategies. Specifically, the model introduces the style recalibration MBconv (SRMB) to mitigate feature dilution caused by the coexistence of lesions and complex backgrounds. In addition, the EMF dynamically adjusts the receptive field, enabling the model to capture lesion distributions across the entire leaf while simultaneously emphasizing morphological details, edges, and fine-scale features. To improve interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to generate visual explanations of the detected diseases. On our self-constructed, weather-augmented MCCD dataset, the experimental results demonstrate that SREM-Net outperforms state-of-the-art networks such as LWMobileViT, MobileNetV3-CA, and LWDN, achieving F1-score improvements of 2.13%, 1.21%, and 1.18%, respectively.

Grasshoppers and their allies (Orthoptera) are numerous and diverse insects globally, while being significant components of biodiversity and nutrient cycling. They are variously responsive to environmental change but are paradoxical, as some species are major pests while others are threatened or even extinct. Most orthopteran species are somewhere in between, with their assemblage composition shifting in response to changing conditions and according to the response traits of the constituent species. With global concern over the impact of conventional agriculture on biodiversity, there is currently a strong transition to agroecology. The agroecological approach is two-fold: to set aside land and to better manage the overall landscape. Both approaches aim to boost the numbers and diversity of most orthopterans, while reducing the impact of the pest species using biologically based pesticides instead of chemical pesticides as part of an integrated pest management program. Set-aside land is present at various spatial scales for conservation action, involving a diversity of practical approaches. Management depends on understanding orthopteran responses to change, and harnessing the positive responses using, for example, improved grazing, fire management, and vegetation diversification for maximizing habitat heterogeneity. These initiatives also recognize the additional interactive effect of climate change and extreme weather events. Importantly, improvement of orthopteran abundance and diversity is an integral component of overall biodiversity conservation. New technologies, both aerial and genomic, are advancing the field of orthopteran biology and ecology greatly. We review advances being made in the field that hold the most promise and suggest ways forward based on three themes: appreciating orthopteran value, recognizing the adverse drivers of orthopteran abundance and diversity, and better design and management of agroecosystems.

Cottonseed is an important resource for edible oil and protein. Here, we evaluated cottonseed oil, protein, and gossypol contents using traditional chemical methods and near-infrared reflectance spectroscopy (NIRS) in diverse upland cotton (n = 456) and sea island cotton (n = 205) germplasm. In upland cotton, oil content averaged 21.23 ± 3.98% (12.74–43.56%), protein averaged 23.63 ± 4.63% (15.53–49.79%), and gossypol averaged 1.47 ± 0.21 mg/g (0.06–2.07). Correlation analysis showed a significant negative association between oil and protein (r = −0.125, p < 0.01; confirmed by NIRS: r = −0.171, p < 0.01), a significant negative association between protein and gossypol (r = −0.375, p < 0.01), and a significant positive association between oil and gossypol (r = 0.409, p < 0.01). In sea island cotton, oil, protein, and gossypol contents averaged 24.82 ± 6.15% (14.64–41.43%), 25.75 ± 2.04% (18.84–39.00%), and 1.60 ± 0.15 mg/g (1.22–2.08), respectively. The oil–protein association was strongly negative by NIRS (r = −0.744, p < 0.01), whereas correlations with gossypol were weak and not significant by the traditional method. After screening and evaluation, high oil and protein varieties were identified in upland cotton (n = 15) and sea island cotton (n = 6). Fourteen extreme-oil upland materials were further used to examine flowering-date effects on oil accumulation and physiological indicators, showing rapid oil accumulation and a flowering-date-dependent maximum. Finally, qRT-PCR analysis of lipid-metabolism-related candidate genes showed that seven genes were expressed significantly higher in high-oil than in low-oil materials (p < 0.05), peaking at the late stage of oil accumulation. GhDGAT1 and GhDGAT2 showed positive regulatory effects on oil accumulation, whereas GhFAD3 and GhKCR2 showed negative regulatory effects. Collectively, these findings provide compositional benchmarks, clarify trait relationships, and identify candidate genes useful for breeding cotton cultivars with improved seed oil/protein traits.

Water availability critically affects basil (Ocimum basilicum L.) growth and physiological performance, making the early and precise monitoring of water-deficit responses essential for precision irrigation. However, conventional visual or biochemical methods are destructive and unsuitable for real-time assessment. This study presents a multimodal optical biosensing and 3D convolutional neural network (3D-CNN) fusion framework for phenotyping physiological responses of basil under water-deficit stress. RGB, depth, and chlorophyll fluorescence (CF) imaging were integrated to capture complementary morphological and photosynthetic information. Through the fusion of 130 optical parameter layers, the 3D-CNN model learned spatial and temporal–spectral features associated with resistance and recovery dynamics, achieving 96.9% classification accuracy—outperforming both 2D-CNN and traditional machine-learning classifiers. Feature-space visualization using t-SNE confirmed that the learned latent representations reflected biologically meaningful stress–recovery trajectories rather than superficial visual differences. This multimodal fusion framework provides a scalable and interpretable approach for the real-time, non-destructive monitoring of crop water stress, establishing a foundation for adaptive irrigation control and intelligent environmental management in precision agriculture.