Prediction of crop yield is an essential task for maximizing the global food supply, particularly in developing countries. This study investigated lettuce yield prediction using four machine learning models, namely, support vector regressor, extreme gradient boosting, random forest, and deep neural network.
It was cultivated in three hydroponics systems, 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 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 which achieved 12.89 g.
All model scenarios having Scatter Index 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. However, DNN with scenario 2 requiring fewer 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.
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Orsini, Francesco & Mendez-Espinoza, Ana & Carotti, Laura & El-Ssawy, Wessam & Al-Ansari, Nadhir & Mokhtar, Ali & He, Hongming & Saad, Sh & Shauket, Saad & Gyasi-Agyei, Yeboah & Abuarab, Mohamed. (2022). Using Machine Learning Models to Predict Hydroponically Grown Lettuce Yield. Plant Science. 13. 1-10. 10.3389/fpls.2022.706042.