Beans (Phaseolus vulgaris L.) yields forecast using normalized difference vegetation index
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Abstract
One of the main tasks of modern agrarian science is to enhance the productivity using available natural, labor, and technical resources. Introduction of remote sensing achievements in precision agriculture systems could be very helpful for early correction of cultivation technology and yield prediction. Based on the results at the irrigated field of Agricultural Cooperative Farm “Radianska Zemlia” (Paryshevo, Kherson oblast, 46.706631 N, 32.274669 E) with common beans (Phaseolus vulgaris L.), the polynomial regression (PR) model for the crop yield prediction using the highest values of normalized difference vegetation index (NDVI) within the crop growing season, which were recorded at the stage of blossoming – pods formation (V8 – R2), is developed. The model provided good fitting (RSQ value was 0.8069) with great accuracy of predictive performance (MAPE value was 7.12%). Using the model and the model-based gradual yields scale could be useful both for the operative adjustment of the crop cultivation technology and beans yield prediction. Additionally, an artificial neural network-based (ANN) model was developed for providing somewhat better fitting and predictive performance: RSQ value was 0.8988, MAPE value was 7.08%. The combined equation based on the results of polynomial regression model adjustment with superstition of the artificial neural network forecasting results (PRANN model) provided better fitting accuracy than the original polynomial model (RSQ value was 0.8080) along with the best forecast precision (MAPE value was 6.83%). The combined PRANN model is the best option for crop yields modeling and forecasting based on the values of NDVI.
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