Carrot Yield Mapping: A Precision Agriculture Approach Based on Machine Learning

Marcelo Chan Fu Wei,Leonardo Felipe Maldaner,José Paulo Molin,Pedro Medeiros Netto Ottoni

doi:10.3390/ai1020015

Abstract

Carrot yield maps are an essential tool in supporting decision makers in improving their agricultural practices, but they are unconventional and not easy to obtain. The objective was to develop a method to generate a carrot yield map applying a random forest (RF) regression algorithm on a database composed of satellite spectral data and carrot ground-truth yield sampling. Georeferenced carrot yield sampling was carried out and satellite imagery was obtained during crop development. The entire dataset was split into training and test sets. The Gini index was used to find the five most important predictor variables of the model. Statistical parameters used to evaluate model performance were the root mean squared error (RMSE), coefficient of determination (R2) and mean absolute error (MAE). The five most important predictor variables were the near-infrared spectral band at 92 and 79 days after sowing (DAS), green spectral band at 50 DAS and blue spectral band at 92 and 81 DAS. The RF algorithm applied to the entire dataset presented R2, RMSE and MAE values of 0.82, 2.64 Mg ha−1 and 1.74 Mg ha−1, respectively. The method based on RF regression applied to a database composed of spectral bands proved to be accurate and suitable to predict carrot yield.

Highlights

Yield maps are one of the most used features in precision agriculture (PA) [1]
A value of 28.59 kg was assigned to each carrot box to continue the continue the procedure to generate carrot yield maps
Regression model was successfully developed and to that, it is necessary to evaluate the possibility of estimating carrot yield from satellite imagery implemented predicting carrot yield based on raw temporal spectral bands with an error of 2.7 Mg with differentin spatial and temporal resolutions

Summary

Introduction

Yield maps are one of the most used features in precision agriculture (PA) [1]. Yield maps can be generated from data collected through different harvesting systems: (a) manually harvested (citrus and horticultural crops [3]) and (b) mechanically harvested (data from yield monitors, for example, in soybean and corn [4]). Yield mapping from manually harvested data is challenging since the harvesting process is highly laborious yet produces better sampling quality [5]. Yield mapping from yield monitors that collect high-density data with less human labor is limited by several factors that affect data quality such as the need for constant inspection of the sensor system and calibration before and during the harvesting process [6]

Objectives

Methods

Results

Conclusion