Using Machine Learning Models to Predict Hydroponically Grown Lettuce Yield.

Ali Mokhtar,Hongming He,Saad Sh Sammen,Nadhir Al-Anasari,Yeboah Gyasi-Agyei,Mohamed Abuarab,Wessam El-Ssawy

doi:10.3389/fpls.2022.706042

Abstract

Prediction of crop yield is an essential task for maximizing the global food supply, particularly in developing countries. This study investigated lettuce yield (fresh weight) prediction using four machine learning (ML) models, namely, support vector regressor (SVR), extreme gradient boosting (XGB), random forest (RF), and deep neural network (DNN). It was cultivated in three hydroponics systems (i.e., suspended nutrient film technique system, pyramidal aeroponic system, and tower aeroponic system), which interacted with three different magnetic unit strengths under a controlled greenhouse environment during the growing season in 2018 and 2019. Three scenarios consisting of the combinations of input variables (i.e., leaf number, water consumption, dry weight, stem length, and stem diameter) were assessed. The XGB model with scenario 3 (all input variables) yielded the lowest root mean square error (RMSE) of 8.88 g followed by SVR with the same scenario that achieved 9.55 g, and the highest result was by RF with scenario 1 (i.e., leaf number and water consumption) that achieved 12.89 g. All model scenarios having Scatter Index (SI) (i.e., RMSE divided by the average values of the observed yield) values less than 0.1 were classified as excellent in predicting fresh lettuce yield. Based on all of the performance statistics, the two best models were SVR with scenario 3 and DNN with scenario 2 (i.e., leaf number, water consumption, and dry weight). However, DNN with scenario 2 requiring less input variables is preferred. The potential of the DNN model to predict fresh lettuce yield is promising, and it can be applied on a large scale as a rapid tool for decision-makers to manage crop yield.

Highlights

The changing conditions of climate and weather patterns during the past years have fueled the current problems of land and water scarcity and continue to cause harm in the agricultural sector (Majid et al, 2021)
A deep neural network (DNN) was applied to predict maize yield during 2008–2016, and the results showed that DNN was clearly better than Least Absolute Shrinkage and Selection Operator (LASSO), shallow neural network (SNN), and regression tree (RT) (Khaki and Wang, 2019)
The lowest T-statistic was recorded by support vector regressor (SVR) with scenario 2, and the highest was recorded by DNN with scenario 2

Summary

Introduction

The changing conditions of climate and weather patterns during the past years have fueled the current problems of land and water scarcity and continue to cause harm in the agricultural sector (Majid et al, 2021). Artificial intelligence (AI), such as neural networks, has been applied in hydrology to deal with complex phenomena (Elbeltagi et al, 2020; Abdel-Fattah and Abdo, 2020; Mokhtar et al, 2021) and is used to control the growth of hydroponic plants (Mehra et al, 2018). For some systems, such as the nutrient film technique (NFT), a fresh solution of nutrients is continuously supplied to the crops to compensate for the uptake of nutrients and water by the plants. The input of nutrients is based on the nutrient/water uptake ratio concept, i.e., nutrient weight per unit volume of water absorbed (Sonneveld and Voogt, 2001; Neocleous and Savvas, 2019)

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Conclusion