Artificial Neural Networks Model for Photosynthetic Rate Prediction of Leaf Vegetable Crops under Normal and Nutrient-Stressed in Greenhouse

Abstract

Photosynthesis is one of the essential processes in plant physiology that produces glucose and oxygen to support plant growth. Nutrient stress conditions will affect the photosynthetic rate in plants. The model predicting photosynthetic rates based on environmental conditions, nutrients, and plant types will be highly beneficial for farmers in tweaking these variables to maximize plant photosynthesis. This research focused on assessing the impact of nutrient stress on the photosynthetic rate in leaf vegetable crops and aimed to create a model using artificial neural networks (ANN) to predict photosynthetic rates under nutrient-stress conditions. Leaf vegetable crops were cultivated in a greenhouse using the NFT hydroponic system with eight nutrient conditions. This paper introduces an ANN model featuring nine input variables, ten hidden layers, and a single output. This model aims to elucidate the relationship between these inputs and the output parameter. The statistical analysis revealed a notable disparity in the CO₂ assimilation rate among leaf vegetable crops subjected to nutrient stress treatment. The constructed ANN model demonstrated strong performance, achieving an R² value of 0.9416, an RMSE of 1.5898 during training, and an R² value of 0.9271 with an RMSE of 1.9649 in validation. A combination of statistical analysis and ANN modeling accurately explained the relationship and influence of input parameters, especially nutrient stress conditions, on the photosynthetic rate of leaf vegetable plants cultivated hydroponically in a greenhouse.