A machine learning modeling framework forTriticum turgidumsubsp.durumDesf. yield forecasting in Italy

Abstract

The forecasting of crop yield is one of the most critical research areas in crop science, which allows for the development of decision support systems, optimization of nitrogen fertilization, and food safety. Many tested modeling approaches can be differentiated according to the models and data used. The models used are traditional crop models that require data that are often difficult to measure. New modeling approaches based on artificial intelligence algorithms have proven to be of high performance, flexible, and can be tested based on available data. In this study, four independent field experiments conducted on Triticum turgidum subsp. durum Desf. in central–southern Italy were used to train a set of machine learning (ML) algorithms to predict the yield using 16 variables: fertilization, nitrogen management, pedoclimatic, and remote sensing data. Four ML algorithms were calibrated and validated over two independent sites, and a linear regression model was used as a control. The calibrated models can predict the grain yield in the two regions by using ancillary data, topsoil physical and chemical properties, multispectral drone imagery, climatic data, and nitrogen fertilizer applied at the site. Among the four ML algorithms, stochastic gradient boosting (root‐mean‐square error = 0.58 t ha−1) outperformed others during calibration and transferability. Nitrogen application rate, seasonal precipitation, and temperature are the most important features for predicting wheat yield.

Multispectral Vegetation Indices and Machine Learning Approaches for Durum Wheat (Triticum durum Desf.) Yield Prediction across Different Varieties

“…Considering that ML algorithms were validated in a different dataset (different training and validation datasets), compared to Pearson's correlations that were tested on the whole dataset, further highlights the predictive capabilities of this approach. The RF at both survey epochs showed the best predictive performance in terms of all the statistics calculated (R 2 , RMSE and MAE), in accordance with several authors [34,[75][76][77]. Following was the performance of k-NN.…”

“…However, the simple linear application of VIs does not always allow reliable yield estimation, especially when spatial and spectral differences are slight [32,33]. A more advanced approach is the application of machine learning (ML) techniques to field and RS data, which allows for better interpretation of patterns and more robust estimations [34,35]. Different ML regression models have been successfully applied to agricultural data like linear, polynomial and logistical regressions, random forest (RF), support vector machines (SVM), neural networks (NN), k-nearest neighbours (k-NN), and stochastic gradient boosting [34].…”

“…A more advanced approach is the application of machine learning (ML) techniques to field and RS data, which allows for better interpretation of patterns and more robust estimations [34,35]. Different ML regression models have been successfully applied to agricultural data like linear, polynomial and logistical regressions, random forest (RF), support vector machines (SVM), neural networks (NN), k-nearest neighbours (k-NN), and stochastic gradient boosting [34]. Few studies have yet been conducted concerning durum wheat, especially in Italy, and numerous aspects like cultivar response to VIs, the best time for UAV monitoring, and the performance of ML approaches need to be fully investigated.…”

Automatic zone management definition in Mediterranean environment for Triticum durum

“…Other data fusion techniques are developed using machine learning (Fiorentini et al, 2022) and deep learning (Schillaci et al, 2021) methods to handle different types of inputs and nonlinear tasks (Chlingaryan et al, 2018).…”

“…(2018) propose a multivariate geostatistical sensor data fusion approach to combine ground penetrating radar and electromagnetic induction sensor for delineating homogeneous zones in terraced olive groves under organic cropping. Other data fusion techniques are developed using machine learning (Fiorentini et al., 2022) and deep learning (Schillaci et al., 2021) methods to handle different types of inputs and nonlinear tasks (Chlingaryan et al., 2018).…”