Feasibility of Early Yield Prediction per Coffee Tree Based on Multispectral Aerial Imagery: Case of Arabica Coffee Crops in Cauca-Colombia

Abstract

Crop yield is an important factor for evaluating production processes and determining the profitability of growing coffee. Frequently, the total number of coffee beans per area unit is estimated manually by physically counting the coffee cherries, the branches, or the flowers. However, estimating yield requires an investment in time and work, so it is not usual for small producers. This paper studies a non-intrusive and attainable alternative to predicting coffee crop yield through multispectral aerial images. The proposal is designed for small low-tech producers monitored by capturing aerial photos with a MapIR camera on an unmanned aerial vehicle. This research shows how to predict yields in the early stages of the coffee tree productive cycle, such as at flowering by using aerial imagery. Physical and spectral descriptors were evaluated as predictors for yield prediction models. The results showed correlations between the selected predictors and 370 yield samples of a Colombian Arabica coffee crop. The coffee tree volume, the Normalized Difference Vegetation Index (NDVI), and the Coffee Ripeness Index (CRI) showed the highest values with 71%, 55%, and 63%, respectively. Further, these predictors were used as the inputs for regression models to analyze their precision in predicting coffee crop yield. The validation stage concluded that Linear Regression and Stochastic Descending Gradient Regression were better models with determination coefficient values of 56% and 55%, respectively, which are promising for predicting yield.

Developing a Sustainable Machine Learning Model to Predict Crop Yield in the Gulf Countries

“…Researchers attempted to improve the prediction models by enhancing the performance of the predictors, for instance through machine learning optimization [4] and the Internet of Things [5]. Additionally, many scholars have attempted to enhance prediction models through remote sensing [6][7][8]. Other scholars are interested in improving the performance of crop yield prediction by selecting the most important feature that can improve crop yield prediction, partially utilizing prediction model selection.…”