Analysis and Regionalization of Climate Ecological Conditions for Blueberry in Nanchong City Based on GIS

By using the meteorological data,GIS data and blueberry production data of Nanchong and the surrounding areas in Nanchong from 1991 to 2022,the climate ecological suitability of blueberry was analyzed based on the growth habit of blueberry,and the restrictive and high-impact factors were selected as zoning indicators to carry out climate ecological zoning.The results show that Nanchong has a humid climate,and the annual average temperature in most parts is 15.0-17.8℃;the cold temperature duration in winter is more than 770 h,and the average maximum temperature in July is more than 30.0℃,so it is suitable for planting southern high-bush blueberry,rabbit-eye blueberry and a hybrid of northern and southern high-bush blueberry.Sunshine,high temperature in midsummer,daily temperature range at the fruiting stage and terrain slope are the main limiting or high-impact ecological factors of blueberry production in Nanchong,among which the adverse effect of insufficient sunshine is the largest.The meteorological disasters affecting blueberry production in Nanchong mainly include rainy weather,rainstorm and flood,high temperature and drought.High temperature and heavy heat damage in midsummer in areas at an altitude below 400 m have a great impact on the production of late-maturing blueberry.High temperature and summer drought are often accompanied in the southeast,which will aggravate the adverse effects.The suitable area of blueberry in Nanchong is mainly distributed in the shallow hilly area and the low-mountain deep hilly and gentle slope area at an altitude of 350-700 m.The less suitable area is mainly distributed in the flat dam area of shallow hill-valley at an altitude of below 350 m,the low-mountain area at an altitude of above 700 m,and other hilly areas with a slope of 15-30°.The unsuitable area is mainly distributed in hills with slope>30°,some sporadic shallow hills and flat dams at an altitude of below 300 m,river banks,water wetland buffer zones,etc.In order to make full use of the climate resources and the advantages of blueberry varieties,early-and medium-maturing southern high-bush varieties should be mainly planted in the low-altitude area below 400 m in Nanchong,and medium-and late-maturing varieties should be mainly planted in the medium-altitude area above 400 m.

A Multi-Label Classification Method for Vehicle Video

In the last few years,smartphone usage and driver sleepiness have been unanimously considered to lead to numerous road accidents,which causes many scholars to pay attention to autonomous driving.For this complexity scene,one of the major challenges is mining information comprehensively from massive features in vehicle video.This paper proposes a multi-label classification method MCM-VV(Multi-label Classification Method for Vehicle Video)for vehicle video to judge the label of road condition for unmanned system.Method MCM-VV includes a process of feature extraction and a process of multi-label classification.During feature extraction,grayscale,lane line and the edge of main object are extracted after video preprocessing.During the multi-label classification,the algorithm DR-ML-KNN(Multi-label K-nearest Neighbor Classification Algorithm based on Dimensionality Reduction)learns the training set to obtain multi-label classifier,then predicts the label of road condition according to maximum a posteriori principle,finally outputs labels and adds the new instance to training set for the optimization of classifier.Experimental results on five vehicle video datasets show that the method MCM-VV is effective and efficient.The DR-ML-KNN algorithm reduces the runtime by 50%.It also reduces the time complexity and improves the accuracy.

LTSA-LE:A Local Tangent Space Alignment Label Enhancement Algorithm

According to smoothness assumption,local topological structure can be shared between feature and label manifolds.This study proposes a new algorithm based on Local Tangent Space Alignment(LTSA)to implement the label enhancement process.In general,we first establish a learning model for feature extraction in label space and use a feature extraction method of LTSA to guide the reconstruction of label manifolds.Then,we establish an unconstrained optimization model based on the optimal theory presented in this paper.The model is suitable for solving problems with a large number of sample points.Finally,the experiment results show that the algorithm can effectively improve the training speed and multilabel dataset prediction accuracy.