DeepCrop: A Deep Learning-Based Process-Based Crop Model for Maximizing Yield
Key Highlights :
Crop yield is an essential factor for the success of any agricultural endeavor. To maximize yield, the best genetic variety and most effective crop management practices must be used for cultivation. Scientists have developed various machine learning models to predict the factors that produce the greatest yield in specific crop plants. However, traditional models cannot accommodate high levels of variation in parameters or large data inputs, which can lead to the failure of models under certain circumstances.
To address this limitation, researchers from Korea led by Professor Jung Eok Son from Seoul National University have created a novel deep-learning based crop model known as "DeepCrop", for hydroponic sweet peppers. The model can accommodate several input variables and has fewer limitations on the amount of data it can process. Hence, it can be employed in most settings and can be extended to similar applications.
The researchers tested the predictions of DeepCrop by cultivating the crop twice a year for two years in greenhouses. Their results were published in Plant Phenomics on March 1, 2023. DeepCrop is a process-based model that can simulate crop growth in response to various factors and environmental conditions. It can be scaled up to include many input types or greater amount of data. One reason for the high versatility of DeepCrop is that it is constructed exclusively with neural networks. Neural networks are combinations of algorithms that process the interactions between input data to make useful predictions. Since simulations are created on a computer-based platform, DeepCrop requires minimal infrastructure.
To validate the predictions of DeepCrop, the team cultivated sweet peppers in preset greenhouse conditions. A comparison of predicted and actual plant growth patterns suggested that DeepCrop outperformed other existing process-based crop models, as indicated by its modeling efficiency. The model was also the least likely to make prediction errors. The capacity of DeepCrop to produce useful predictions even with varying inputs and parameters suggests that it can determine relationships between input data regardless of data type.
The results of this study also suggest that deep-learning models can be useful for a wide range of applications in crop science. DeepCrop can improve the accessibility of crop models and mitigate fragmentation problems in crop model studies. With its high applicability and abstraction ability, DeepCrop can be used to maximize crop yield and ensure successful agricultural endeavors.
