Crop yield can be maximized when the best genetic variety and most effective crop management practices are 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. This can lead to the failure of models under certain circumstances. Also, since crop models are restricted to the types of input they can accommodate, improvements to one model may not apply to other models.
To overcome 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 01, 2023.
“We selected deep-learning algorithms as a potential solution to mitigate fragmentation and redundancy. Deep learning has high applicability to broad target tasks as well as remarkable abstraction ability for enormous sets of data,” explains Prof. Son.
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