Crop Classification Based on a Novel Feature Filtering and Enhancement Method

Vegetation indices, such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from remote sensing images, are widely used for crop classification. However, vegetation index profiles for different crops with a similar phenology lead to difficulties in discerning these crops both spectrally and temporally. This paper proposes a feature filtering and enhancement (FFE) method to map soybean and maize, two major crops widely cultivated during the summer season in Northeastern China. Different vegetation indices are first calculated and the probability density functions (PDFs) of these indices for the target classes are established based on the hypothesis of normal distribution; the vegetation index images are then filtered using the PDFs to obtain enhanced index images where the pixel values of the target classes are „enhanced„. Subsequently, the minimum Gini index of each enhanced index image is computed, generating at the same time the weight for every index. A composite enhanced feature image is produced by summing all indices with their weights. Finally, a classification is made from the composite enhanced feature image by thresholding, which is derived automatically based on the samples. The efficiency of the proposed FFE method is compared with the maximum likelihood classification (MLC), support vector machine (SVM), and random forest (RF) in a mapping operation to determine the soybean and maize distribution in a county in Northeastern China. The classification accuracies resulting from this comparison show that the FFE method outperforms MLC, and its accuracies are similar to those of SVM and RF, with an overall accuracy of 0.902 and a kappa coefficient of 0.846. This indicates that the FFE method is an appropriate method for crop classification to distinguish crops with a similar phenology. Our research also shows that when the sample size reaches a certain level (e.g., 2000), the mean and standard deviation of the sample are very close to the actual values, which leads to high classification accuracy. In a case where the condition of normal distribution is not fulfilled, the PDF of the vegetation index can be created by a lookup table. Furthermore, as the method is rather simple and explicit, and convenient in terms of computing, it can be used as the backbone for automatic crop mapping operations

Protective Effect of <i>Lycium ruthenicum</i> Polyphenols on Oxidative Stress against Acrylamide Induced Liver Injury in Rats

Acrylamide (ACR) is formed during tobacco and carbohydrate-rich food heating and is widely applied in many industries, with a range of toxic effects. The antioxidant properties of Lycium ruthenicum polyphenols (LRP) have been established before. This study aimed to research the protective effect of LRP against ACR-induced liver injury in SD rats. Rats were divided into six groups: Control, ACR (40 mg/kg/day, i.g.), LRP (50, 100, and 200 mg/kg/day, i.g.) plus ACR, and LRP groups. After 19 days, we evaluated oxidative status and mitochondrial functions in the rat’s liver. The results showed that glutathione (GSH) and superoxide dismutase (SOD) levels increased after LRP pretreatment. In contrast, each intervention group reduced reactive oxygen species (ROS) and malondialdehyde (MDA) levels compared to the ACR group. Meanwhile, alanine aminotransferase (ALT), aspartate aminotransferase (AST), liver mitochondrial ATPase activity, mRNA expression of mitochondrial complex I, III, and expression of nuclear factor-erythroid 2-related factor 2 (Nrf2) and its downstream proteins were all increased. This study suggested that LRP could reduce ACR-induced liver injury through potent antioxidant activity. LRP is recommended as oxidative stress reliever against hepatotoxicity

Data_Sheet_1_Effect of fruit intake on functional constipation: A systematic review and meta-analysis of randomized and crossover studies.zip

Functional constipation (FC) is commonly treated with fruits whose efficacy remains unclear. We conducted a meta-analysis of fruit intervention for FC and provided evidence-based recommendations. We searched seven databases from inception to July 2022. All randomized and crossover studies on the effectiveness of fruits on FC were included. We conducted sensitivity and subgroup analysis. A total of 11 studies were included in this review. Four trials showed that kiwifruits have significantly increased stool frequency (MD = 0.26, 95% CI (0.22, 0.30), P 2 = 0%) than palm date or orange juice in the fixed-effect meta-analysis. Three high-quality studies suggested that kiwifruits have a better effect than ficus carica paste on the symptom of the FC assessed by the Bristol stool scale in the fixed-effect meta-analysis [MD = 0.39, 95% CI (0.11, 0.66), P 2 = 27%]. Besides, five trials showed that fruits can increase the amount of Lactobacillus acidophilus [MD = 0.82, 95% CI (0.25, 1.39), P 2 = 52%], analyzed with the random-effect model. Subgroup meta-analysis based on the types of fruits suggested that fruits including pome fruit, citrus fruit, and berries have increased the effect of Bifidobacterium t more than the stone fruits in the random effect meta-analysis [MD = 0.51, 95% CI (0.23, 0.79), P 2 = 84%]. Totally, fruit intake may have potential symptom alleviation on the FC as evidence shows that they can affect stool consistency, stool frequency, and gut microbiota. Further large-scale studies are needed to gain more confident conclusions concerning the association between fruit intake and FC in the future.</p

The essential oil of Artemisia capillaris protects against CCl4-induced liver injury in vivo

Abstract To study the hepatoprotective effect of the essential oil of Artemisia capillaris Thunb., Asteraceae, on CCl4-induced liver injury in mice, the levels of serum aspartate aminotransferase and alanine aminotransferase, hepatic levels of reduced glutathione, activity of glutathione peroxidase, and the activities of superoxide dismutase and malondialdehyde were assayed. Administration of the essential oil of A. capillaris at 100 and 50 mg/kg to mice prior to CCl4 injection was shown to confer stronger in vivo protective effects and could observably antagonize the CCl4-induced increase in the serum alanine aminotransferase and aspartate aminotransferase activities and malondialdehyde levels as well as prevent CCl4-induced decrease in the antioxidant superoxide dismutase activity, glutathione level and glutathione peroxidase activity (p < 0.01). The oil mainly contained &#946;-citronellol, 1,8-cineole, camphor, linalool, &#945;-pinene, &#946;-pinene, thymol and myrcene. This finding demonstrates that the essential oil of A. capillaris can protect hepatic function against CCl4-induced liver injury in mice

Sent2Agri System Based Crop Type Mapping in Yellow River Irrigation Area

Ａｇｒｉｃｕｌｔｕｒａｌ ｍｏｎｉｔｏｒｉｎｇ ｉｓ ｅｓｓｅｎｔｉａｌ ｆｏｒ ａｄｅｑｕａｔｅ ｍａｎａｇｅｍｅｎｔ ｏｆ ｆｏｏｄ ｐｒｏｄｕｃｔｉｏｎ ａｎｄ ｄｉｓｔｒｉｂｕｔｉｏｎ. Ｃｒｏｐ ｌａｎｄ ａｎｄ ｃｒｏｐ ｔｙｐｅ ｃｌａｓｓｉｆｉｃａｔｉｏｎꎬ ｕｓｉｎｇ ｒｅｍｏｔｅ ｓｅｎｓｉｎｇ ｔｉｍｅ ｓｅｒｉｅｓꎬ ｆｏｒｍ ａｎ ｉｍｐｏｒｔａｎｔ ｔｏｏｌ ｔｏ ｃａｐｔｕｒｅ ｔｈｅ ａｇｒｉｃｕｌｔｕｒａｌ ｐｒｏｄｕｃｔｉｏｎ ｉｎｆｏｒｍａｔｉｏｎ. Ｔｈｅ ｒｅｃｅｎｔｌｙ ｌａｕｎｃｈｅｄ Ｓｅｎｔｉｎｅｌ￣２ ｓａｔｅｌｌｉｔｅｓ ｐｒｏｖｉｄｅ ｕｎｐｒｅｃｅｄｅｎｔｅｄ ｍｏｎｉｔｏｒｉｎｇ ｃａｐａｃｉｔｉｅｓ ｉｎ ｔｅｒｍｓ ｏｆ ｓｐａｔｉａｌ ｒｅｓｏｌｕｔｉｏｎꎬ ｓｗａｔｈ ｗｉｄｔｈꎬ ａｎｄ ｒｅｖｉｓｉｔ ｆｒｅｑｕｅｎｃｙ. Ｔｈｅ Ｓｅｎｔｉｎｅｌ￣２ ｆｏｒ Ａｇｒｉｃｕｌｔｕｒｅ (Ｓｅｎ２￣Ａｇｒｉ) ｓｙｓｔｅｍ ｈａｓ ｂｅｅｎ ｄｅｖｅｌｏｐｅｄ ｔｏ ｆｕｌｌｙ ｅｘｐｌｏｉｔ ｔｈｏｓｅ ｃａｐａｃｉｔｉｅｓꎬ ｂｙ ｐｒｏｖｉｄｉｎｇ ｆｏｕｒ ｒｅｌｅｖａｎｔ ｅａｒｔｈ ｏｂｓｅｒｖａｔｉｏｎ ｐｒｏｄｕｃｔｓ ｆｏｒ ａｇｒｉｃｕｌｔｕｒａｌ ｍｏｎｉｔｏｒｉｎｇ. Ｕｎｄｅｒ ｔｈｅ Ｄｒａｇｏｎ ４ Ｐｒｏｇｒａｍꎬ ｔｈｅ ｃｒｏｐ ｍａｐｐｉｎｇ ｗｉｔｈ ｖａｒｉｏｕｓ ｓａｔｅｌｌｉｔｅ ｉｍａｇｅｓ ａｎｄ ａ ｓｐｅｃｉｆｉｃ ｆｏｃｕｓ ｏｎ ｔｈｅ Ｙｅｌｌｏｗ Ｒｉｖｅｒ ｉｒｒｉｇａｔｅｄ ａｇｒｉｃｕｌｔｕｒａｌ ａｒｅａ ｉｎ ｔｈｅ Ｎｉｎｇｘｉａ Ｈｕｉ Ａｕｔｏｎｏｍｏｕｓ Ｒｅｇｉｏｎ ｉｎ Ｃｈｉｎａ ｗａｓ ｃａｒｒｉｅｄ ｏｕｔ ｗｉｔｈ ｔｈｅ Ｓｅｎｔｉｎｅｌ￣２ ｆｏｒ Ａｇｒｉｃｕｌｔｕｒｅ ｓｙｓｔｅｍ (Ｓｅｎｔ２Ａｇｒｉ). ９ ｔｙｐｅｓ ｏｆ ｃｒｏｐｓ ｗｅｒｅ ｃｌａｓｓｉｆｉｅｄ ａｎｄ ｔｈｅ ｃｒｏｐ ｔｙｐｅ ｍａｐ ｉｎ ２０１７ ｗａｓ ｐｒｏｄｕｃｅｄ ｂａｓｅｄ ｏｎ ３５ ｓｃｅｎｅｓ Ｓｅｎｔｉｎｅｌ ２Ａ/ Ｂ ｉｍａｇｅｓ. Ｔｈｅ ｏｖｅｒａｌｌ ａｃｃｕｒａｃｙ ｃｏｍｐｕｔｅｄ ｆｒｏｍ ｔｈｅ ｅｒｒｏｒ ｃｏｎｆｕｓｉｏｎ ｍａｔｒｉｘ ｉｓ ８８％ꎬ ｗｈｉｃｈ ｉｎｃｌｕｄｅｓ ｔｈｅ ｃｒｏｐｐｅｄ ａｎｄ ｕｎｃｒｏｐｐｅｄ ｔｙｐｅｓ. Ａｆｔｅｒ ｔｈｅ ｒｅｍｏｖａｌ ｏｆ ｔｈｅ ｕｎｃｒｏｐｐｅｄ ａｒｅａꎬ ｔｈｅ ｏｖｅｒａｌｌ ａｃｃｕｒａｃｙ ｆｏｒ ａ ｃｒｏｐｐｅｄ ｄｅｃｒｅａｓｅ ｔｏ ７３％. Ｉｎ ｏｒｄｅｒ ｔｏ ｆｕｒｔｈｅｒ ｉｍｐｒｏｖｅ ｔｈｅ ｃｒｏｐ ｃｌａｓｓｉｆｉｃａｔｉｏｎ ａｃｃｕｒａｃｙꎬ ｔｈｅ ｔｒａｉｎｉｎｇ ｄａｔａｓｅｔ ｓｈｏｕｌｄ ｂｅ ｆｕｒｔｈｅｒ ｉｍｐｒｏｖｅｄ ａｎｄ ｔｕｎｅｄ