## 2 Related Work
### Cosnut Yield and Its Prediction  
[PERSON] [16] developed research for discovering, evaluating, and estimating the local tall coconut production and productivity at Taliabu Island. Drone technology was integrated with the classical sampling population to improve the efficiency of data collection. The collected data was used to maximize the production of coconuts by utilizing input technology. This increased coconut production enabled coconut palms to generate high yields per unit area, which improved the sustainability of the integrated coconut industry. The Ordinary Least Square (OLS) regression and Quantile Regression (QR) approaches were introduced by [PERSON] et al. [17] for estimating production in Cobb Douglas functional form. If input was considered without lags, the amount of bearing palms created a positive effect during OLS estimation. An amount of bearing palms and one-year lag rainfall created a positive effect in nut production during QR results. The strong/weak relationship over the coconut yield distribution was found using the QR. However, the developed OLS overestimates and underestimates an appropriate relationship among covariates and response variables. [PERSON] [18] studied problems in coconut plantations that impact selling price, income, and economic value to welfare. A quantitative study using regression analysis was developed as a method for estimating causal relationships between variables. Here, samples for this work were randomly acquired from 150 coconut farmers in the Kalimantan region. The decrease in the number of trees was attributed to the transformation of mining, putting other plantations in the coconut industry at risk. Another risk identified was operational costs, which were not balanced with the selling price. [PERSON] et al. [19] developed a MaxEnt model for evaluating bioclimatic variables. In order to identify the ideal habitat for coconut production and locations with climates that would be viable given future climate circumstances, the MaxEnt model was used to evaluate bioclimatic parameters. Because of the created MaxEnt model's high prediction accuracy, it was frequently used for species dispersion. Species dispersion refers to the spatial distribution or arrangement of individuals within a species population or community. It describes how individuals are spread out or dispersed across a landscape, region, or habitat. However, the results were generalized from independent data due to spatial bias and overfitting. [PERSON] and [PERSON] (2020) examined the growth and variability of the coconut sector in the world context. Secondary data from 1990-1991 to 2020-2021 was obtained from the Faostat and ICC websites on area, coconut yield, and production. Coppock's instability index was used to investigate the pattern of growth according to the compound growth rate and calculate the instability. The area role and production yield were examined using decomposition analysis. The study found poor management practices, dangerous diseases, and pests were causing the negative growth. [PERSON] et al. (2021) presented four different models, including LASSO, Ridge, Artificial Neural Networks (ANNs), and Elastic Net (ELNET) regression, which were used to determine the best model for predicting coconut yields. This yield estimation utilized external factors and weather parameters of the Coimbatore district. The ELNET was better when compared to the other approaches as it achieved an effective regularization of the statistical model. The yearly output of data and monthly weather data for several districts on India's west coast were produced by [PERSON] et al. (2020) between 2000 and 2015. The weather indices were produced by adding together the various monthly average characteristics, including, relative humidity, solar radiation maximum and lowest temperatures, rainfall, and wind speed.