Using Artificial Neural Network (ANN) for Short-Range Prediction of Cotton Yield in Data-Scarce Regions

• Published in: Agronomy (Basel) Vol. 12; no. 4; p. 828
• Main Authors: Yildirim, Tugba, Moriasi, Daniel N., Starks, Patrick J., Chakraborty, Debaditya
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-04-2022
• Subjects: Accuracy; Agricultural production; Agricultural resources; Altitude; artificial neural network; Artificial neural networks; Atmospheric aerosols; Climate change; Cotton; Crop yield; Datasets; Drought; Drought index; early indicator; Economic impact; Evapotranspiration; Heat; machine learning; Neural networks; Normalized difference vegetative index; Precipitation; Predictions; Rain; Remote sensing; Resource management; short-range yield prediction; Soybeans; Standardized precipitation index; Surface water; Vegetation; Vegetation index; Turkey
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy12040828

Short-range predictions of crop yield provide valuable insights for agricultural resource management and likely economic impacts associated with low yield. Such predictions are difficult to achieve in regions that lack extensive observational records. Herein, we demonstrate how a number of basic or readily available input data can be used to train an Artificial Neural Network (ANN) model to provide months-ahead predictions of cotton yield for a case study in Menemen Plain, Turkey. We use limited reported yield (13 years) along cumulative precipitation, cumulative heat units, two meteorologically-based drought indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)), and three remotely-sensed vegetation indices (Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI)) as ANN inputs. Results indicate that, when EVI is combined with the preceding 12-month SPEI, it has better sensitivity to cotton yield than other indicators. The ANN model predicted cotton yield four months before harvest with R2 > 0.80, showing potential as a yield prediction tool. We discuss the effects of different combinations of input data (explanatory variables), dataset size, and selection of training data to inform future applications of ANN for early prediction of cotton yield in data-scarce regions.