Evaluation of Remote Sensing and Meteorological parameters for Yield Prediction of Sugarcane (Saccharum officinarum L.) Crop

Abstract

In the Agriculture sector, the farmers need a reliable estimation for pre-harvest crop yield prediction to decide their import-export policies. The present work aims to assess the impact of remote sensing-based derived products with Climate data on the accuracy of a prediction model for the sugarcane yield. The regression method was used to develop an empirical model based on VCI, Historical Sugarcane Yield, and Climatic Parameters of 75 districts of six major sugar-producing states of India. The MOD13Q1 product of MODIS on Board Terra Satellite at 16-day intervals was accessed during the growing season of sugarcane crop with 36 meteorological parameters for experimentation. The accuracy of the model was evaluated using R 2 , Root Mean square Metric (RMSE), Mean Absolute Error (MAE), and mean square error (MSE). The preliminary results concluded that the proposed methodology achieved the highest accuracy with (R 2 =0.95, MAE=5.18, MSE=34.5, RMSE=5.87). The conclusion of the study highlighted that the coefficient of determination can be improved significantly by incorporating maximum and minimum temperature parameters with Remote sensing derived vegetation indices for the sugarcane yield.

Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review

“…The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/rs16050863/s1, Table S1: Basic statistics on RMSE (ton ha −1 ) of the selected papers' models, where DM means Data Mining; Table S2: Attributes based on field information; Table S3: Attributes based on spectral bands and vegetation indices; Table S4: Attributes based on meteorological data; Table S5: Attributes based on SAR data; Table S6: Attributes based on terrain information; Table S7: Other attribute types [111][112][113][114][115][116].…”

Predicting Sugarcane Yield via the Use of an Improved Least Squares Support Vector Machine and Water Cycle Optimization Model

“…However, in the traditional machine learning model, the sugarcane yield prediction considers too many single influencing factors, and the prediction accuracy is hardly satisfactory. On the one hand, regional differences lead to different environmental variables of sugarcane growth, which have a greater impact on the prediction accuracy of sugarcane yield models [16,17]. On the other hand, the decrease in the prediction accuracy of the crop growth model is a result of the overall change in climate due to temporal differences [18].…”