Huanglongbing (HLB), or citrus greening disease, has complex and variable symptoms, making its diagnosis almost entirely reliant on subjective experience, which results in a low diagnosis efficiency. To overcome this problem, we constructed and validated a deep learning (DL)-based method for detecting citrus HLB using YOLOv5l from digital images. Three models (Yolov5l-HLB1, Yolov5l-HLB2, and Yolov5l-HLB3) were developed using images of healthy and symptomatic citrus leaves acquired under a range of imaging conditions. The micro F1-scores of the Yolov5l-HLB2 model (85.19%) recognising five HLB symptoms (blotchy mottling, “red-nose” fruits, zinc-deficiency, vein yellowing, and uniform yellowing) in the images were higher than those of the other two models. The generalisation performance of Yolov5l-HLB2 was tested using test set images acquired under two photographic conditions (conditions B and C) that were different from that of the model training set condition (condition A). The results suggested that this model performed well at recognising the five HLB symptom images acquired under both conditions B and C, and yielded a micro F1-score of 84.64% and 85.84%, respectively. In addition, the detection performance of the Yolov5l-HLB2 model was better for experienced users than for inexperienced users. The PCR-positive rate of Candidatus Liberibacter asiaticus (CLas) detection (the causative pathogen for HLB) in the samples with five HLB symptoms as classified using the Yolov5l-HLB2 model was also compared with manual classification by experts. This indicated that the model can be employed as a preliminary screening tool before the collection of field samples for subsequent PCR testing. We also developed the ‘HLBdetector’ app using the Yolov5l-HLB2 model, which allows farmers to complete HLB detection in seconds with only a mobile phone terminal and without expert guidance. Overall, we successfully constructed a reliable automatic HLB identification model and developed the user-friendly ‘HLBdetector’ app, facilitating the prevention and timely control of HLB transmission in citrus orchards.

The vicious invasive alien plantAmaranthus palmeriposes a serious threat to ecological security and food security due to its strong adaptability, competitiveness, and herbicide resistance. Predicting its potential habitats under current and future climate change is critical for monitoring and early warning. In this study, we used two sets of climate data, namely, WorldClim1.4 and RCPs (the historical climate data of WorldClim version 1.4 and future climate data of RCPs), WorldClim2.1 and SSPs (the historical climate data of WorldClim version 2.1 and future climate data of SSPs), to analyze the dominant environmental variables affecting the habitat suitability and predict the potential distribution ofA. palmerito climate change in China based on the MaxEnt model. The results show that (i) Temperature has a greater impact on the distribution ofA. palmeri. The relative contributions of temperature‐related variables count to 70% or more, and the annual mean temperature (bio1) reached more than 40%. (ii) At present, the potentially suitable area is widely distributed in the central‐east and parts of southwest China, and the high suitable area is focused on the North China Plain. The potential suitable area predicted by WorldClim1.4 and WorldClim2.1 both accounts for about 31% of China's total land area. (iii) Future climate change will expand the suitable habitats to high latitudes and altitudes. The overall suitable area maximum increased to 44.93% under SSPs and 38.91% under RCPs. We conclude that climate change would increase the risk ofA. palmeriexpanding to high latitudes and altitudes, the results have practical implications for the effective long‐term management in response to the global warming ofA. palmeri.

Global climate change has significantly affected terrestrial carbon sinks. Net ecosystem production (NEP) plays a critical role in the global carbon cycle. However, interannual variability (IAV) of the NEP and its regional contributions and climate attributions are not well-understood on a global scale. This study used a diagnostic model driven by remote sensing leaf area index (LAI) to investigate the NEP IAV and analyze regional and climate contributions on a global scale from 1982 to 2016. We found large NEP IAV during the study period, with the NEP detrended anomaly ranging from −2.3 Pg C in 1998 to 1.6 Pg C in 2013 at a global scale. Furthermore, 63.7% and 34.1% of the areas showed positive and negative contributions to NEP IAVs globally, respectively. Evergreen broadleaf forest (EBF) contributed the most (31.1%) to NEP IAV, followed by cropland (21.7%) and grassland (20.8%). Temperature played the most critical roles in the global NEP IAV, with a contribution of 45.5%. However, the partial correlation between NEP and temperature was negative, and the correlation with precipitation was positive in most areas of the globe, indicating that global warming is not conducive to the global carbon sink, but abundant rainfall is important for the global carbon cycle. This study suggests that, to increase the global carbon sink, we should pay more attention to tropical forests (EBFs) and highlight the importance of water availability.

Anxi Tieguanyin belongs to the oolong tea category and is one of the top ten most famous teas in China. In this study, hyperspectral imaging (HSI) technology was combined with chemometric methods to achieve the rapid determination of free amino acid and tea polyphenol contents in Tieguanyin tea. Here, the spectral data of Tieguanyin tea samples of four quality grades were obtained via visible near-infrared hyperspectroscopy in the range of 400–1000 nm, and the free amino acid and tea polyphenol contents of the samples were detected. First derivative (1D), normalization (Nor), and Savitzky–Golay (SG) smoothing were utilized to preprocess the original spectrum. The characteristic wavelengths were extracted via principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and the successive projection algorithm (SPA). The contents of free amino acid and tea polyphenol in Tieguanyin tea were predicted by the back propagation (BP) neural network, partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM). The results revealed that the free amino acid content of the clear-flavoured Tieguanyin was greater than that of the strong-flavoured type, that the tea polyphenol content of the strong-flavoured Tieguanyin was greater than that of the clear-flavoured type, and that the content of the first-grade product was greater than that of the second-grade product. The 1D preprocessing improved the resolution and sensitivity of the spectra. When using CARS, the number of wavelengths for free amino acids and tea polyphenols was reduced to 50 and 70, respectively. The combination of 1D and CARS is conducive to improving the accuracy of late modelling. The 1D-CARS-RF model had the highest accuracy in predicting the free amino acid (R_P² = 0.940, RMSEP = 0.032, and RPD = 4.446) and tea polyphenol contents (R_P² = 0.938, RMSEP = 0.334, and RPD = 4.474). The use of hyperspectral imaging combined with multiple algorithms can be used to achieve the fast and non-destructive prediction of free amino acid and tea polyphenol contents in Tieguanyin tea.

Optimizing soil carbon content is essential for mitigating climate change. Understanding the soil organic carbon (SOC) contents and their relationship with plantation age is important for enhancing SOC in tea plantations. However, there is still a lack of studies in quantifying the SOC–age curve of the whole life cycle in tea plantations. Thus, in this study, we collected 140 soil samples aged 3 to 60 years in the four representative regions (Anxi, Datian, Qingliu, and Fuzhou) in Fujian Province to quantify the SOC contents and their relationship with plantation age. We found that the average SOC was 14.6 ± 6.1 g/kg in the four sampling regions. Nitrogen (N) emerged as having a highly significant positive correlation with SOC (R² = 0.9). We also found a significant negative correlation between SOC and mean annual temperature (MAT) (R² = 0.6), and a significant positive correlation with mean annual precipitation (MAP) (R² = 0.6). The SOC increased with plantation age at 3–20 years old and peaked at 16–20 years old. After 35 years, the SOC decreased gradually with the aging of the plantation. The results indicated that tea plantations could be renewed after the age of 35. These results showed that optimizing age structure is important in enhancing SOC in tea plantations and is meaningful in achieving carbon neutrality.