Remote sensing of quality traits in cereal and arable production systems:A review

Cereal is an essential source of calories and protein for the global population.Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers,grading harvest and categorised storage for enterprises,future trading prices,and policy planning.The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits.Many studies have also proposed models and methods for predicting such traits based on multiplatform remote sensing data.In this paper,the key quality traits that are of interest to producers and consumers are introduced.The literature related to grain quality prediction was analyzed in detail,and a review was conducted on remote sensing platforms,commonly used methods,potential gaps,and future trends in crop quality prediction.This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.

Should phenological information be applied to predict agronomic traits across growth stages of winter wheat?

Most existing agronomic trait models of winter wheat vary across growing seasons, and it is an open question whether a unified statistical model can be developed to predict agronomic traits using a vegetation index(VI) across multiple growing seasons. In this study, we constructed a hierarchical linear model(HLM) to automatically adapt the relationship between VIs and agronomic traits across growing seasons and tested the model’s performance by sensitivity analysis. Results demonstrated that(1) optical VIs give poor performance in predicting AGB and PNC across all growth stages, whereas VIs perform well for LAI, LGB, LNC, and SPAD.(2) The sensitivity indices of the phenological information in the AGB and PNC prediction models were 0.81–0.86 and 0.66–0.73, whereas LAI, LGB, LNC, and SPAD prediction models produced sensitivity indexes of 0.01–0.02, 0.01–0.02, 0.01–0.02, and 0.02–0.08, respectively.(3) The AGB and PNC prediction models considering ZS were more accurate than the prediction models based on VI. Whether or not phenological information is used, there was no difference in model accuracy for LGB,LNC, SPAD, and LAI. This study may provide a guideline for deciding whether phenological correction is required for estimation of agronomic traits across multiple growing seasons.

Novel evolutionary insights into nemacheilid cavefi sh: evidence from comparative analysis of mitochondrial genomes

Cavefi sh can be important models for understanding the relationships among evolution,adaptation,and development in extreme environments.However,cavefi sh remain poorly studied,particularly at the genome level.Here,we sequenced the complete mitogenome of three cavefi sh in the family Nemacheilidae(Paranemachilus pingguoensis,Oreonectes polystigmus,and Heminoemacheilus longibarbatus),which were collected from karst caves in South China.The mitogenomes each contained 37 genes(13 protein coding,22 tRNA,and two rRNA genes)and a single control region,with the same genetic arrangement and distribution as those found in vertebrates.The non-synonymous/synonymous mutation ratios(Ka/Ks)of the mitogenomes indicated that the protein-coding genes(PCGs)of the three cavefi sh evolved under purifying selection.The mitogenomes of the three cavefi sh exhibit nucleotide composition biases for PCGs,tRNAs,rRNAs,and the whole genome,indicating that the mitochondrial DNA might have been subjected to evolutionary selection in response to extreme cave environments.Divergence time and evolutionary history analyses suggested that the speciation and diversifi cation of the cavefi sh coincided with the Miocene uplift of the southern Qinghai-Tibet Plateau,which greatly changed cave habitats.Overall,our study sheds light on the mitogenomes,phylogeny,and evolutionary history of nemacheilid cavefi sh.

Experimental validation of adsorption filter model under dynamic VOC concentrations:Prediction of long-term efficiency

Indoor volatile organic compound(VOC)concentrations are often dynamic because the ventilation and emission rates of VOC usually change.Adsorption filters used for air purification may operate with a capacity that fluctuates with unsteady VOC concentrations in buildings.Modeling the dynamic interactions between adsorption filters and indoor air is crucial for predicting their performance under real-world conditions.This study presents a numerical model of partially reversible adsorption equilibrium coupled with a mass transfer model to create a predictive model for adsorption efficiency in environments with dynamic VOC concentrations.A honeycomb adsorption filter for benzene adsorption was simulated and tested,including the breakthrough and purging curve and the long-term efficiency in an experimental chamber with dynamic concentrations.The results reveal that the curve generated with the partially reversible adsorption equilibrium model closely aligns with the measured one.Furthermore,the model was coupled with a chamber model and the simulation results were compared with those calculated using the filter model with a single adsorption isotherm.When VOCs were emitted intermittently in the chamber and there was sufficient ventilation,the concentration peaks in the chamber derived from the models with different assumptions on adsorption reversibility were significantly different from each other.Moreover,it was observed that the reversible adsorption capacity of the filter was crucial for long-term operation in rooms with dynamic concentration.Despite the reversible adsorption capacity constituting only 6.7%of the total adsorption capacity of the tested filter,it contributes to a significant“peak shaving and valley filling”effect,even when the irreversible adsorption capacity is saturated.The adsorption reversibility should be taken as an important parameter for selecting adsorbents for dynamic concentration conditions.

Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.

Leaf pigment retrieval using the PROSAIL model: Influence of uncertainty in prior canopy-structure information

Leaf pigments are critical indicators of plant photosynthesis,stress,and physiological conditions.Inversion of radiative transfer models(RTMs)is a promising method for robustly retrieving leaf biochem-ical traits from canopy observations,and adding prior information has been effective in alleviating the“ill-posed”problem,a major challenge in model inversion.Canopy structure parameters,such as leaf area index(LAI)and average leaf inclination angle(ALA),can serve as prior information for leaf pigment retrie-val.Using canopy spectra simulated from the PROSAIL model,we estimated the effects of uncertainty in LAI and ALA used as prior information for lookup table-based inversions of leaf chlorophyll(C _(ab))and car-otenoid(C_(ar)).The retrieval accuracies of the two pigments were increased by use of the priors of LAI(RMSE of C_(ab) from 7.67 to 6.32μg cm^(-2),C_(ar) from 2.41 to 2.28μg cm^(-2))and ALA(RMSE of C_(ab) from 7.67 to 5.72μg cm^(-2),C_(ar) from 2.41 to 2.23μg cm^(-2)).However,this improvement deteriorated with an increase of additive and multiplicative uncertainties,and when 40% and 20% noise was added to LAI and ALA respectively,these priors ceased to increase retrieval accuracy.Validation using an experimental winter wheat dataset also showed that compared with C_(ar),the estimation accuracy of C_(ab) increased more or deteriorated less with uncertainty in prior canopy structure.This study demonstrates possible limita-tions of using prior information in RTM inversions for retrieval of leaf biochemistry,when large uncer-tainties are present.

Comparison of three models for winter wheat yield prediction based on UAV hyperspectral images

Predicting crop yield timely can considerably accelerate agricultural production management and food policy-making,which are also important requirements for precise agricultural development.Given the development of hyperspectral imaging technology,a simple and efficient modeling method is convenient for predicting crop yield by using airborne hyperspectral images.In this study,the Unmanned Aerial Vehicle(UAV)hyperspectral and maturity yield data in 2014-2015 and 2017-2018 were collected.The winter wheat yield prediction model was established by optimizing Vegetation Indices(VIs)feature scales and sample scales,incorporating Partial Least Squares Regression(PLSR),Random Forest algorithm(RF),and Back Propagation Neural Network algorithm(BPN).Results showed that PLSR stands out as the optimal wheat yield prediction model considering stability and accuracy(RMSE=948.88 kg/hm2).Contrary to the belief that more input features result in higher accuracy,PLSR,RF,and BPN models performed best when trained with the top 3,8,and 4 VIs with the highest correlation,respectively.With an increase in training samples,model accuracy improves,reaching stability when the training samples reach 70.Using PLSR and optimal feature scales,UAV yield prediction maps were generated,holding significant value for field management in precision agriculture.

BET inhibitors enhance the anti-cancer effect of etoposide by suppressing the MRN-ATM axis in the DNA damage response

Etoposide is widely used for cancer chemotherapy in the clinic.However,long-term etoposide treatment can lead to adverse effects or drug resistance.To improve the situation,we evaluated the therapeutic efficiency of etoposide combined with inhibitors of bromodomain and extraterminal(BET)family proteins,which have recently emerged as novel anti-cancer targets due to their critical roles in cancer development.Firstly,we showed BRD4,one of the main targets of BET inhibitors,was involved in DNA damage response(DDR)via the homologous recombination(HR)repair pathway.

Differential pressure reset strategy based on reinforcement learning for chilled water systems

Air conditioning water systems account for a large proportion of building energy consumption.In a pressure-controlled water system,one of the key measures to save energy is to adjust the differential pressure setpoints during operation.Typically,such adjustments are based either on certain rules,which rely on operator experience,or on complicated models that are not easy to calibrate.In this paper,a data-driven control method based on reinforcement learning is proposed.The main idea is to construct an agent model that adapts to the researched problem.Instead of directly being told how to react,the agent must rely on its own experiences to learn.Compared with traditional control strategies,reinforcement learning control(RLC)exhibits more accurate and steady performances while maintaining indoor air temperature within a limited range.A case study shows that the RLC strategy is able to save substantial amounts of energy.

A comparative review of the state and advancement of Site-Specific Crop Management in the UK and China

Precision agriculture, and more specifically Site-Specific Crop Management(SSCM), has been implemented in some form across nearly all agricultural production systems over the past 25 years. Adoption has been greatest in developed agricultural countries. In this review article, the current situation of SSCM adoption and application is investigated from the perspective of a developed(UK) and developing(China) agricultural economy. The current state-of-the art is reviewed with an emphasis on developments in position system technology and satellite-based remote sensing. This is augmented with observations on the differences between the use of SSCM technologies and methodologies in the UK and China and discussion of the opportunities for(and limitations to)increasing SSCM adoption in developing agricultural economies. A particular emphasis is given to the role of socio-demographic factors and the application of responsible research and innovation(RRI) in translating agritechnologies into China and other developing agricultural economies. Several key research and development areas are identified that need to be addressed to facilitate the delivery of SSCM as a holistic service into areas with low precision agriculture(PA) adoption. This has implications for developed as well as developing agricultural economies.