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Phenology average for the cotton crop. Legend: V0 = beginning of the plant emergency; V1 = V0 to the main rib of the second leaf; V2 = V1 to the main rib of the third leaf; V3 = V2 to the main rib of the fourth leaf; V4 = V3 to the main rib of the fifth leaf; B1 = first visible floral bud; B3 = first floral bud visible on the third branch; F1 = opening of the first flower in the first fruit branch; F3 = opening of the first flower in the third fruit branch and C1 = first apple of the first open branch (boll cotton).
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Accurate forecasts of cotton yield are of great interest for the development of the market, increasing the sustainability of the sector worldwide. Thus, the objectives of this study were: 1) to evaluate the influence of climatic elements on cotton yield in Brazil, 2) to predict cotton yield using machine learning algorithms based on climatic elemen...
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... in these weather patterns can negatively impact cotton production worldwide. The average cotton phenology can be seen in Figure 1. ...
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... selection of the best-calibrated algorithm was performed using the following statistical indices: 1) Pearson correlation (r); 2) Adjusted coefficient of determination (R 2 adj); 3) Mean Absolute Percentage Error (MAPE) (Equations 12 to 14). In obtaining the greatest reliability of the regressions, we selected only the significant regressions by the F test at 5% probability. ...
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... the regression, the KNN determines a value of a given attribute from the sample of neighboring data from the training set. The performance of all algorithms in the calibration is shown in Figure 10. As the forecast is being carried out until flowering, which promotes anticipation of ± 80 days, with MAPE considered low. ...
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... superiority of TREE was also evidenced. The performance of TREE in the test is shown in Figure 11.A. TREE is a nonparametric algorithm widely used in several areas ( Veenadhari et al. 2011Veenadhari et al. , 2014Goyal 2014), but little used in crop forecast. A MAPE value of 18.35% is considered adequate when testing forecasting models using climate data as independent variables ( Marcari et al. 2015; Moreto and Rolim 2015). ...
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... yield ranged from 2065.05 kg ha −1 (Naviraí-MS) to 3046.64 kg ha −1 (Montividiu-GO) in the Midwest region of Brazil. The TREE algorithm was the one that showed the best performance to follow all the spatial variability of cotton yield both in the calibration (Figure 12 AB) and in the test (Figure 12.CD), using climatic elements as independent variables. ...
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... yield ranged from 2065.05 kg ha −1 (Naviraí-MS) to 3046.64 kg ha −1 (Montividiu-GO) in the Midwest region of Brazil. The TREE algorithm was the one that showed the best performance to follow all the spatial variability of cotton yield both in the calibration (Figure 12 AB) and in the test (Figure 12.CD), using climatic elements as independent variables. ...
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... the TREE calibration, the average deviations between the actual data and the predicted data were only 57.2 kg ha −1 (Figure 12.F). Deviations of this magnitude are low, considering an 80-day forecast. ...
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... the test of the algorithms, the data deviations for the TREE were higher for the calibration. For example, the deviations for the GO, MS, and MT states were 432.06 (± 57.2) kg ha −1 (Paraúna), 755.59 (± 57.2) kg ha −1 (São Gabriel do Oeste), and 796.48 (± 57.2) kg ha −1 (Pedra Preta), respectively (Figure 12.E). ...
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... Overall, the statistical analysis and forecasting of cotton yield dynamics is an important tool for agricultural researchers and policymakers. By identifying trends, making predictions, and identifying factors that affect yield, this type of analysis can help improve cotton production and ensure food security for communities that depend on cotton as a source of income and sustenance [17][18][19]. ...
... The second task of the analysis is to explain the mechanism for changing the levels of the series; to solve it, regression analysis is usually used. In the third, the description of the change in the time series and the explanation of the mechanism for the formation of the series are often used for statistical forecasting, which in most cases comes down to extrapolation of the detected development trends [16][17][18][19]. ...
There are phenomena that are significant to research because of how they grow and change through time in practically every discipline. One could attempt to direct a process, forecast the future using knowledge of the past, or characterize the distinctive aspects of a series using a finite quantity of information. The techniques used to handle time series are heavily influenced by the techniques created by mathematical statistics for distribution series. The most basic to the most complicated time series analysis techniques exist in statistics today. The article discusses the statistical analysis of a time series, specifically the average yield of cotton in the Kashkadarya region, Uzbekistan, and the Republics, using data from the Central Statistical Office of Uzbekistan from 2001 to 2020. The study involved constructing point and interval estimates for the average cotton yield with a 95% guarantee, identifying different types of trends, and predicting future yields for the region. Through the use of the Durbin-Watson statistical criteria, it was discovered that there is an autocorrelation dependence in the average cotton yield, indicating that the yield for the current year is dependent on yields from past years. The methods used in this study can be applied to further research conducted by students and scientists.
This paper aims at the influence of the disorder original data on the prediction accuracy of the GM(1,1) model, a waveform data generation method is proposed to make the disorder data become monotonically increasing order data. Then the initial condition of the time response function is reset according to the new information first principle, and the accuracy of the improved model is tested. The prediction accuracy shows that in terms of peanut yield, the posterior error ratio and average relative error obtained by using the improved GM(1,1) model are 0.117 and 0.0499, respectively, which are smaller than the posterior error ratio and average relative error of traditional models. In terms of consumption, the posterior error ratio predicted by using the improved GM(1,1) model is 0.078, which is smaller than the posterior error ratio of traditional models, which is 0.093. Compared to the traditional model, the improved GM(1,1) model can better predict China’s peanut production and consumption with higher prediction accuracy. This article uses an improved GM(1,1) model with high prediction accuracy, and China’s peanut production and consumption will continue to increase from 2023 to 2025.
Cotton ( Gossypium spp.) is one of the important cash crops in the United States. Monitoring in-season growth metrics, from early season growth to harvest, is crucial for predictive and prescriptive cotton farming. In recent years, forecasting models have garnered considerable attention to predict canopy indices. This allows selection of management options during crop growth to boost cotton yield and profitability. Here, we used unmanned aerial system-derived canopy features, including canopy cover, canopy height, and excess green index, collected from 3500 plots at Driscoll in Corpus Christi, Texas during the years 2019, 2020, and 2021 for in-season growth forecasting. Training datasets in our model were produced by K-Means clustering and Dynamic Time Warping (DTW) techniques were used to compare various Long Short-Term Memory (LSTM) models in predicting the three canopy features. Accuracy was determined using Root Mean Square Error (RMSE). Results indicated higher predictive capacity of Convolutional Neural Networks (CNN) LSTM for canopy cover, and multi-layer stacked LSTMs for canopy height and excess green index respectively. Overall, results show tremendous potential for in-season growth forecasting and management of agricultural inputs like pesticides and fertilizers for improving crop health and productivity.
This study seeks a distinctive and efficient machine learning system for the prediction of Cotton Production using weather parameters and climate change impact on cotton production. Cotton is a crucial harvest for Pakistan referred to as “white gold”. Cotton is taken into account lifeline of Pakistan's economy. Pakistan is the fifth largest cotton producer. Cotton and textile exporters are the rear bone of Pakistan's economy. Being a cotton-based economy Pakistan aims to extend its share in the billion-dollar value-added global textile market. But in the process of cotton growth affected by meteorological conditions, extreme weather can cause cotton production, based on this kind of situation, machine learning technology to deal with meteorological data analysis, realize the accurate prediction of cotton production, on the influence of the main meteorological factors on cotton yield and diseases, the selection suitable for cotton varieties and resist meteorological disaster is of great significance.
The study analyses the impact of weather parameters on the productivity of cotton in Pakistan using the district level disintegrated data of yield, area, and climate variables (temperature, cloud cover, rainfall, and wind) from 2005-to 2020, also uses the Production of cotton from 2005-2020. These Sixteen years moving averages for each month, climate variables are used. The production function approach is used to analyze the relationship between crop yield and weather parameters up and down each month. Cotton has a great dependence on environmental factors during its growth, especially climate change. The occurrence of cotton pests and diseases has always been an important factor affecting total cotton production. Pests and diseases are also caused by environmental factors. Apply a Machine learning algorithm to analyze the pests and diseases of cotton because of environmental factors. Model construction and analysis of meteorological factors the Decision Tree, Random Forest, Linear Regression, and XGB algorithm using ensemble technique were established for cotton yield prediction in Pakistan and the performance of each model was compared. The comparison results show that the prediction results of the prediction model using the optimization algorithm are significantly improved, among which the XGB model using ensemble techniquehas the best performance, and the root mean square error (RMSE), and mean square error (MSE) of the prediction results are 0.07and 0.27 respectively.
The relationship between main meteorological factors and cotton yield was analyzed by XGB algorithm. The results showed that temperature, cloud cover, rainfall, and wind were the most important factors affecting cotton yield in Pakistan from each growth stage of cotton, the boll stage is the most susceptible to meteorological factor, and the bud stage is the second the geographical location, climatic characteristics and meteorological disasters that resulted in cotton production. So, because of these factors indication on time action can increase the production and overcome on the cotton declined production. In the future there are many improvement ways one thing we can do that is daily base weather parameters use for prediction and diseases related to weather elements. Increase of other weather parameters will be more affective in future.
Food security is always a pressing agenda worldwide. The grain production in many areas has decreased due to the reduction in agricultural research funding and infrastructure investment. In this paper, we employed the Extreme-Tree algorithm to determine the main effectors in grain production in Hexi Corridor, Gansu, China, during 2002–2018. First, we applied the three-stage super-SBM DEA to precisely assess agricultural production. Then, we used the Extremely randomized trees algorithm to quantify the importance of each factor. Our results show that the variant of average efficiency score at the first stage was minimal. After removing the influence of environmental factors on production efficiency, the more accurate efficiency score was decreasing from 2002 to 2018. The R² value of the Extra-Tree model was 0.989 in the grain production analysis. Our research shows that grain production in the Hexi Corridor was controlled by human-driven but not nature-driven during our research period. Based on the importance attribution analysis of each model, it showed that the importance of human-driven investment occupied 93.7% of grain production. The importance of nature-driving was about 6.3%. Accordingly, we proposed corresponding opinions and suggestions to government and growers.
The El Niño-Southern Oscillation (ENSO) phenomenon affects the global climate by changing temperature and precipitation patterns mainly in tropical climatic regions and median latitudes. Such event strongly influences agricultural activities and crop yields. The Niño Oceanic Index (ONI) of the US National Oceanic and Atmospheric Administration (NOAA) describes and monitors ENSO intensity from ocean temperature measurements. When ONI in the Niño 3.4 region was +0.5ºC above normal or -0.5ºC below normal for 5 consecutive 3-month running averages, El Niño (EN) or La Niña (LN) events, respectively, were established. The prediction of ENSO events is made by modeling at major global weather centers by atmosphere-ocean coupling models; however, no articles was found using decision tree classifier (DTC) for ENSO forecasting purposes. This modeling approach requires much less computational time and capacity. Furthermore DTC can be sufficiently accurate for agricultural purposes. Thus, the objective of this research was forecast as early as possible the El niño and La niña yearly events using a DTC technique from ONI data from 1950 to 2020. We used as input variables for DTC quarterly ONI values from 15 quarters prior the data of forecasting. The DTC showed an accuracy of 89%, 84% and 78% to predict La Niña, El Niño, and Neutral years respectively without training period. For validation the accuracy was 100%, 79% and 79% for La Niña, El Niño, and Neutral years respectively. The selected ONI quarters were July-August-September, January-February-March, and February-March-April of the previous year and January-February-March of the current year, allowing an 8-month advance forecast with an average accuracy of 78% (validation).
