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Fast, accurate, and non-destructive large-scale detection of sweet cherry ripeness is the key to determining the optimal harvesting period and accurate grading by ripeness. Due to the complexity and variability of the orchard environment and the multi-scale, obscured, and even overlapping fruit, there are still problems of low detection accuracy even using the mainstream algorithm YOLOX in the absence of a large amount of tagging data. In this paper, we proposed an improved YOLOX target detection algorithm to quickly and accurately detect sweet cherry ripeness categories in complex environments. Firstly, we took a total of 2400 high-resolution images of immature, semi-ripe, and ripe sweet cherries in an orchard in Hanyuan County, Sichuan Province, including complex environments such as sunny days, cloudy days, branch and leaf shading, fruit overlapping, distant views, and similar colors of green fruits and leaves, and formed a dataset dedicated to sweet cherry ripeness detection by manually labeling 36068 samples, named SweetCherry. On this basis, an improved YOLOX target detection algorithm YOLOX-EIoU-CBAM was proposed, which embedded the Convolutional Block Attention Module (CBAM) between the backbone and neck of the YOLOX model to improve the model's attention to different channels, spaces capability, and replaced the original bounding box loss function of the YOLOX model with Efficient IoU (EIoU) loss to make the regression of the prediction box more accurate. Finally, we validated the feasibility and reliability of the YOLOX-EIoU-CBAM network on the SweetCherry dataset. The experimental results showed that the method in this paper significantly outperforms the traditional Faster R-CNN and SSD300 algorithms in terms of mean Average Precision (mAP), recall, model size, and single-image inference time. Compared with the YOLOX model, the mAP of this method is improved by 4.12%, recall is improved by 4.6%, F-score is improved by 2.34%, while model size and single-image inference time remain basically comparable. The method in this paper can cope well with complex backgrounds such as fruit overlap, branch and leaf occlusion, and can provide a data base and technical reference for other similar target detection problems. [ABSTRACT FROM AUTHOR]

Ecological restoration can improve soil fertility and have a significant impact on the soil nitrogen cycle. Nitrogen (N) is an essential nutrient element for plant growth and development, and also an important factor limiting soil productivity. As an important part of soil nitrogen, the composition and proportion of soil organic nitrogen components can directly or indirectly affect the difficulty of soil organic nitrogen mineralization and nitrogen availability, and then affect soil fertility. However, the current studies on soil nitrogen under ecological restoration mainly focus on nitrogen accumulation and nitrogen mineralization, while there are relatively few studies on changes in soil organic nitrogen components, especially in alpine regions. Therefore, in this study, three restoration pattern of mixed forage (MG), single shrub (SA) and shrub combination (SG) that have been restored continuously for 15 years in northwest Sichuan, China, were taken as the research object, and natural sandy land (CK) without manual intervention was taken as the control. Through field sampling and laboratory analysis, the characteristics of the soil nitrogen content and its proportion to soil total nitrogen (TN) under ecological restoration in alpine sandy land in northwest Sichuan, China, were investigated, and the correlation between the nitrogen content and soil physicochemical properties was analyzed. The results showed that the three ecological restoration patterns significantly increased the contents of acylated ammonium nitrogen (AMMN), acid-lyzed amino sugar nitrogen (ASN), acid-lyzed amino acid nitrogen (AAN), acid-lyzed unknown nitrogen (HUN), acid-lyzed total nitrogen (AHN) and non-acid-lyzed nitrogen (NHN) in soil, and the change trend was consistent with that of soil TN. Ecological restoration improved soil nitrogen mineralization and storage capacity by increasing the proportion of AAN, HUN and NHN to soil TN, and the effect was most obvious in the MG pattern 20–40 cm and SG pattern 40–60 cm soil layers. In general, except ASN, the soil nitrogen content was positively correlated with the soil TN, soil water content (SWC) and soil organic carbon (SOC), and negatively correlated with the soil bulk density (BD) and pH. The results of this study will help us to understand the supply capacity of soil nitrogen under ecological restoration and provide a scientific basis for the selection of an ecological restoration mode and the improvement of the restoration effect and efficiency in alpine sandy land. [ABSTRACT FROM AUTHOR]

Water management and soil conditions affect the bioavailability of cadmium (Cd) and inorganic arsenic (As) in the soil, and hence, their accumulation in rice grains. A field experiment was conducted to investigate the effects of two water management regimes (flooding and dry–wet alternation) on Cd and inorganic As uptake and transport in rice under different soil conditions (paddy soil developed from gray-brown alluvium, K1; paddy soil developed from weathered shale and slate, K2) in the Sichuan Basin, Western China. The results indicated that compared to the wet–dry rotation, long-term flooding led to a substantial decrease of 49.3~55.8% in soil-available Cd content (p < 0.05), accompanied by a significant increase of 16.0~74.2% in As(Ⅲ) content (p < 0.05), causing no significant difference in As(V) content at the K1 site (p > 0.05). However, differences in soil-available Cd and inorganic As content under different water management treatments were both insignificant at the K2 site (p > 0.05). Long-term flooding treatment at the K1 site resulted in a remarkable reduction of 90.2% in Cd content in rice husks and 92.2% in brown rice (p < 0.05), along with a significant increase of 263.6% and 153.3%, respectively, in As(Ⅲ) content; no significant differences in As(V) content were observed at the K2 site (p > 0.05). In conclusion, the effect of water management on rice Cd and inorganic As varied under different soil conditions, with the change in rice Cd and inorganic As in paddy soil developed from gray-brown alluvium being significantly greater than that in paddy soil developed from weathered shale and slate. [ABSTRACT FROM AUTHOR]

Purple soil is a type of global soil that is referred to by various names in different countries, which makes it difficult to understand, utilize, and ameliorate purple soil internationally. Soil Taxonomy (ST) and the World Reference Base for Soil Resources (WRB) are the most widely used soil classification systems in the world. The aim of this study was to clarify the classification of purple soil in ST and the WRB and to establish a reference between different classification systems of purple soil. Therefore, based on the current principles and methods of the ST and WRB systems, 18 typical purple soil profiles in the eastern Sichuan Basin were identified, retrieved, and classified. Then, the soil units of the WRB were compared with those of ST and the Chinese Soil Taxonomy (CST). The results revealed that the 18 typical purple soil profiles could be classified into three soil orders, four soil group orders, and seven soil subgroups in ST and four reference soil groups (RSGs) in the WRB; each profile had its own unique principal and supplementary qualifier combinations within the soil units. It was found that when compared with the ST system, the WRB and CST systems had stronger abilities to distinguish purple soil. In addition, the WRB system was able to more comprehensively consider soil characteristics such as soil layer thickness, ferric horizon, soil color, texture mutations, and carbonate through qualifiers. However, the CST system added diagnostic characteristics, such as the lithologic characteristics of purplish sandstones and shales and the ferric properties and alic properties at the soil group and subgroup levels, which enhanced the differentiation ability of the purple soil at the subgroup level. [ABSTRACT FROM AUTHOR]

Due to frequent human disturbance and the influence of crop growth and development, the migration of soil organic carbon (SOC) in sloping farmland is considerably different to those in other ecosystems. The impacts of maize over its entire growth period on the SOC loss in sloping farmland on purple soils under different erosion stages were investigated, in 2016. This was undertaken using rainfall simulation tests on 15° slopes with a rainfall intensity of 1.5 mm·min−1, in Sichuan Province, China. In this study, erosion development, fluctuating increasing trends in the surface runoff yield, interflow runoff yield, sediment yield, and dissolved organic carbon (DOC) migration flux were observed. Opposite trends were observed in the DOC mass concentration, total soil organic carbon (TOC) content of the sediment, the SOC content of sediment particle state, the DOC content of the sediment, and the SOC enrichment ratio. The DOC migration flux in the surface runoff and in interflow of the rill erosion stage was 1.39–2.84, 3.22–7.78 times significantly higher than that of the sheet erosion stage at each maize growth stage, respectively (p < 0.05). The average DOC mass concentration in the surface runoff, the total DOC content of the sediment, and the SOC enrichment ratio in the sheet erosion stage increased by 100.58–146.44%, 44.44–126.15%, 141.32–191.26%, respectively, compared with the rill erosion stage. Under the experimental conditions, we found that DOC loss mainly occurred at the seedling and mature stages for maize. We also found that maize growth could promote the production of soil interflow, leading to intense soil loss occurring at the subsurface. Compared with DOC mass concentrations in the surface runoff, there was an increase of 4.90–28.29% in the soil interflow, indicating that soil interflow plays a more important role in DOC loss. The growth of maize could impact formation of surface runoff and interflow, reducing the loss of SOC caused by soil erosion. This study helps to understand the carbon loss process in agricultural production in purple soil areas. [ABSTRACT FROM AUTHOR]

Estimating the available potassium (AK) in soil can help improve field management and crop production. Fourier-transform infrared (FTIR) spectroscopy is one of the most promising techniques for the fast and real-time analysis of soil AK content. However, the successful estimation of soil AK content by FTIR depends on the proper selection of appropriate spectral dimensionality reduction techniques. To magnify the subtle spectral signals concerning AK content and improve the understanding of the characteristic FTIR wavelengths of AK content, a total of 145 soil samples were collected in an agricultural site located in the southwest part of Sichuan, China, and three typical spectral dimensionality reduction methods—the successive projections algorithm (SPA), simulated annealing algorithm (SA) and competitive adaptive reweighted sampling (CARS)—were adopted to select the appropriate spectral variable. Then, partial least squares regression (PLSR) was utilized to establish AK inversion models by incorporating the optimal set of spectral variables extracted by different dimensionality reduction algorithms. The accuracy of each inversion model was tested based on the coefficient of determination (R2), root mean square error (RMSE) and mean absolute value error (MAE), and the contribution of the inversion model variables was explored. The results show that: (1) The application of spectral dimensionality reduction is a useful technique for isolating specific components of multicomponent spectra, and as such is a powerful tool to improve and expand the predicted potential of the spectroscopy of soil AK content. Compared with the SA and CARS algorithms, the SPA was more suitable for soil AK content inversion. (2) The inversion model results showed that the characteristic wavelengths were mainly around 777 nm, 1315 nm, 1375 nm, 1635 nm, 1730 nm and 3568–3990 nm. (3) Comparing the performances of different inversion models, the SPA–PLSR model (R2= 0.49, RMSE = 22.80, MAE = 16.82) was superior to the SA–PLSR and CARS–PLSR models, which has certain guiding significance for the rapid detection of soil AK content. [ABSTRACT FROM AUTHOR]

This study evaluated the potential distribution of the potato tuber moth. This species severely impacts global potato production, especially in China and India, which have the world's largest potato production. We developed two indices considering host plant availability and production in addition to climatic suitability, which was simulated using the CLIMEX model. Thus, three different indices were used to project potential distribution of the potato tuber moth under a climate change scenario: (1) climatic suitability (ecoclimatic index (EI)) (EIM), (2) climatic suitability combined with host plant availability (EIN1), and (3) climatic suitability combined with host plant production (EIN2). Under the current climate, EIM was high in southern India and central to southern China, while EIN1 and EIN2 were approximately 38% and 20% lower than EIM, respectively. Under the Special Report on Emissions Scenario A1B, the potato tuber moth would probably not occur in India, but its distribution could be extended to the north, reaching N47°. The areas with the highest climatic suitability by potato tuber moth based on three indices were Sichuan and Karnataka in response to climate change. These areas require adequate pest control, such as prevention of spread through transport of potato seed or by using cold storage facilities. [ABSTRACT FROM AUTHOR]