2024

• Record 193 of
  
  Title:Prediction of Bee Population and Number of Beehives Required for Pollination of a 20-Acre Parcel Crop
  
  Author(s):Jin, Yukun(1); Wei, Tianyi(1); Shi, Jingru(1); Chen, Tingwen(1); Yang, Kai(2,3)
  Source: Signals and Communication Technology  Volume: Part F2203  Issue:   DOI: 10.1007/978-3-031-47100-1_12  Published: 2024  
  Abstract:The decline of the bee population poses threats to the production of considerable types of crops that require pollination. The prediction of the bee’s future population has therefore become a valuable research topic. For Problem one, we tried to solve it in mainly two ways: using the Grey Forecast Model and using differential equations. For data that were missing, we processed them by normalization at first and then regressed to find the abnormal data, and filled the missing data with average data after deleting abnormal data. For the Grey forecast, we use three types of models and compared their respective results with true values to pick the one with the most accurate output and use it to predict the population of bees. For the differential equation method, we simply express the rate of increase in population in terms of several variables (in the differential equation) and solve the equation to obtain the future population. For Problem two, we do a sensitivity test on the bee population. We applied the Random Forest model here to determine the importance of each variable. During the evaluation of the model, we test four sets of data and compare the Random Forest results with the true value. It turned out to be that the final model predicts the population precisely, which has proven that it is reliable. At last, we change the sensitivity of each variable for a 100% change and tell the importance of the variables. For Problem three, we get the model of the possibility of a plant being visited by a bee in a beehive system at any distance, and then we use this matrix to simulate the area and calculate the possibility at any point. After determining a possible lower bound, we can get the area that can reach the bound which is the area the current beehive system can serve. By changing the number and the positions of beehives, we can get the maximum area the system can serve at any time. We can also calculate the possibility considering the planting density and the population of bees so it can be related to problem 1. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
  Accession Number: 20240515465509
• Record 194 of
  
  Title:Analysis of Bee Population and the Relationship with Time
  
  Author(s):Li, Muyang(1); Liu, Xiaole(1); Qi, Chen(1); Liu, Lexuan(1); Yang, Kai(2,3)
  Source: Signals and Communication Technology  Volume: Part F2203  Issue:   DOI: 10.1007/978-3-031-47100-1_10  Published: 2024  
  Abstract:This essay proposes two methods to analyze bee populations in a given period. The first method is a quantitative analysis of the correlation between time and population, establishing a time–population model for bees. However, this method fails to provide a precise enough result. For improvement, the analysis of bee populations is augmented with more comprehensive factors (both positive and negative), creating a unified measure to calculate the total change in population percentage by assigning weights to each individual factor. During the construction of these two methods, we completed the following five steps: Find relevant data with a numerical correlation between time and population: Data containing relevant information like time and population were downloaded from credible sources. Then, the data were fitted with linear regression to reveal the relationship between the population and time. Find possible factors that affect bee populations: External and internal factors were identified through a literature review of research articles and reputable online sources. Among these, five factors were deemed the most critical and to be used in this chapter later. Assign weights to each factor through the Entropy Weight Method (EWM) and Analytic Hierarchy Process (AHP): With EWM or AHP, a different set of weights was assigned to the factors. However, in this paper, neither of these two was used alone. Instead, a unified model that learns from both methods and hence generates a better weight for each factor is proposed and explained. Analysis of beehives needed to pollinate a 20-acre area: Parameters for the model were identified, defined, and populated using relevant data. Finally, the minimum and the maximum number of beehives that satisfy the requirements were calculated and an average of the values was obtained. Testing of the model on Buhlmann 1985: With the fully calculated weights of different factors through the integrated method, the model was tested to see if the weight assignments were reasonable. To do this, the result obtained from this model is compared with data approached by Buhlmann (1985) as an evaluation of this model. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
  Accession Number: 20240515465518