Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods.

Tobacco is an important economic crop and the main raw material of cigarette products. Nowadays, with the increasing consumer demand for high-quality cigarettes, the requirements for their main raw materials are also varying. In general, tobacco quality is primarily determined by the exterior quality, inherent quality, chemical compositions, and physical properties. All these aspects are formed during the growing season and are vulnerable to many environmental factors, such as climate, geography, irrigation, fertilization, diseases and pests, etc. Therefore, there is a great demand for tobacco growth monitoring and near real-time quality evaluation. Herein, hyperspectral remote sensing (HRS) is increasingly being considered as a cost-effective alternative to traditional destructive field sampling methods and laboratory trials to determine various agronomic parameters of tobacco with the assistance of diverse hyperspectral vegetation indices and machine learning algorithms. In light of this, we conduct a comprehensive review of the HRS applications in tobacco production management. In this review, we briefly sketch the principles of HRS and commonly used data acquisition system platforms. We detail the specific applications and methodologies for tobacco quality estimation, yield prediction, and stress detection. Finally, we discuss the major challenges and future opportunities for potential application prospects. We hope that this review could provide interested researchers, practitioners, or readers with a basic understanding of current HRS applications in tobacco production management, and give some guidelines for practical works.

Inhibition of AMPK/PFKFB3 mediated glycolysis synergizes with penfluridol to suppress gallbladder cancer growth.

BACKGROUND: Penfluridol (PF) is an FDA-approved antipsychotic drug that has recently been shown to have anticancer activity. However, the anticancer effects and underlying mechanisms of PF are not well-established in gallbladder cancer (GBC). METHODS: The anticancer efficacy of PF on GBC was investigated via a series of cell functions experiments, including cell viability, colony formation, apoptosis assays, and so on. The corresponding signaling changes after PF treatment were explored by western blotting. Then, nude mice were utilized to study and test the anticancer activity of PF in vivo. Besides, glucose consumption and lactic production assays were used to detect the glycolysis alteration. RESULTS: In this study, we discovered that PF greatly inhibited the proliferation and invasion ability of GBC cells (GBCs). The glucose consumption and lactic generation ability of GBCs were dramatically elevated following PF treatment. Additionally, we discovered that inhibiting glycolysis could improve PF's anticancer efficacy. Further studies established that the activation of the AMPK/PFKFB3 signaling pathway medicated glycolysis after PF treatment. We proved mechanistically that inhibition of AMPK/PFKFB3 singling pathway mediated glycolysis was a potential synergetic strategy to improve the anticancer efficacy of PF on GBC. CONCLUSIONS: By inhibiting AMPK, the anticancer effects of PF on GBCs were amplified. As a result, our investigations shed new light on the possibility of repurposing PF as an anticancer drug for GBC, and AMPK inhibition in combination with PF may represent a novel therapeutic strategy for GBC. Video abstract.

[The new method monitoring crop water content based on NIR-Red spectrum feature space].

Moisture content is an important index of crop water stress condition, timely and effective monitoring of crop water content is of great significance for evaluating crop water deficit balance and guiding agriculture irrigation. The present paper was trying to build a new crop water index for winter wheat vegetation water content based on NIR-Red spectral space. Firstly, canopy spectrums of winter wheat with narrow-band were resampled according to relative spectral response function of HJ-CCD and ZY-3. Then, a new index (PWI) was set up to estimate vegetation water content of winter wheat by improveing PDI (perpendicular drought index) and PVI (perpendicular vegetation index) based on NIR-Red spectral feature space. The results showed that the relationship between PWI and VWC (vegetation water content) was stable based on simulation of wide-band multispectral data HJ-CCD and ZY-3 with R2 being 0.684 and 0.683, respectively. And then VWC was estimated by using PWI with the R2 and RMSE being 0.764 and 0.764, 3.837% and 3.840%, respectively. The results indicated that PWI has certain feasibility to estimate crop water content. At the same time, it provides a new method for monitoring crop water content using remote sensing data HJ-CCD and ZY-3.

[Estimating canopy water content in wheat based on new vegetation water index].

Moisture content is an important indicator for crop water stress condition, timely and effective monitoring crop water content is of great significance for evaluate crop water deficit balance and guide agriculture irrigation. In order to improve the saturated problems of different forms of typical NDWI (Normalized Different Water Index), we tried to introduce EVI (Enhanced Vegetation Index) to build new vegetation water indices (NDWI#) to estimate crop water content. Firstly, PROSAIL model was used to study the saturation sensitivity of NDWI, and NDWI# to canopy water content and LAI (Leaf Area Index). Then, the estimated model and verified model were estimated using the spectral data and moisture data in the field. The result showed that the new indices have significant relationships with canopy water content. In particular, by implementing modified standardized for NDWI1450, NDWI1940, NDWI2500. The result indicated that newly developed indices with visible-infrared and shortwave infrared spectral feature may have greater advantage for estimation winter canopy water content.