Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques

Abstract

Monitoring and prediction of within-field crop variability can support farmers to make the right decisions in different situations. The current advances in remote sensing and the availability of high resolution, high frequency, and free Sentinel-2 images improve the implementation of Precision Agriculture (PA) for a wider range of farmers. This study investigated the possibility of using vegetation indices (VIs) derived from Sentinel-2 images and machine learning techniques to assess corn (Zea mays) grain yield spatial variability within the field scale. A 22-ha study field in North Italy was monitored between 2016 and 2018; corn yield was measured and recorded by a grain yield monitor mounted on the harvester machine recording more than 20,000 georeferenced yield observation points from the study field for each season. VIs from a total of 34 Sentinel-2 images at different crop ages were analyzed for correlation with the measured yield observations. Multiple regression and two different machine learning approaches were also tested to model corn grain yield. The three main results were the following: (i) the Green Normalized Difference Vegetation Index (GNDVI) provided the highest R2 value of 0.48 for monitoring within-field variability of corn grain yield; (ii) the most suitable period for corn yield monitoring was a crop age between 105 and 135 days from the planting date (R4–R6); (iii) Random Forests was the most accurate machine learning approach for predicting within-field variability of corn yield, with an R2 value of almost 0.6 over an independent validation set of half of the total observations. Based on the results, within-field variability of corn yield for previous seasons could be investigated from archived Sentinel-2 data with GNDVI at crop stage (R4–R6).

Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

“…Considering the test dataset, the RF regression had RMSE values ranging from 4.63 to 5.47 Mg ha −1 , which was lower than the MLR model (RMSE closer to 6.0 Mg ha −1 ). Other studies reported RF prediction as more accurate than MLR [2,53].…”

“…Orbital images are commonly used in agriculture to identify spectral variations resulting from soil and crop characteristics at a large-scale, supporting diagnostics for agronomical crop parameters and helping farmers to make better management decisions. For example, over the years, orbital images were used to delimit management zones for annual crops [1], monitor within-field yield variability for many crops such as corn [2] and cotton [3], map vineyard variability [4], plan the wheat harvest [5], develop crop growth model [6], and map grasslands biomass [7,8], among others. Some of the main limitations related to orbital images are the lack of ground truth data (calibration) and the measurement accuracy of the agronomical variables [9].…”

Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields

“…The simple linear model was used as a benchmark to measure the relative performance of the models implemented. The other models (xgbTree, nnet, rf) evaluated in this work were selected based on the robustness they displayed in previous studies [4,5,59,60].…”

“…The modelling process implemented herein-machine learning techniques-were used to predict yields like in similar approaches currently used to develop agricultural models based on remote sensing imagery [70][71][72]. According to [60], machine learning techniques provide a higher accuracy and a more robust performance compared to conventional correlations as they learn to model complexity through training. In addition, remote sensing models to forecast crop yields can perform comparably or better than complex crop simulation models [4].…”

Latest Advances in Sensor Applications in Agriculture

“…Several studies on spectral data applications in field crops are available from remote and ground sensors using data-mining techniques for nitrogen status and grain yield [20,21]. Most of these studies were based on measurements acquired by one sensor and there is a lack of available information on how different combinations of sensors are informative.…”