A Novel Deep Learning Approach for Greenhouse Crop Growth Prediction
Keywords:
Greenhouse, Crop Growth Prediction, Deep Learning, Artificial Neural Network, BayesianNetworkAbstract
The precise management of environmental conditions ensures increased crop production, and crop growth prediction in greenhouses plays a big part in agricultural design and governance in greenhouses. Using growth prediction in greenhouses, growers and farmers can better plan for the future and save money. But, it's a very tough process. Radiations, CO2, temperature, condition of seedlings, soil conditions and fertilization, illness rates, and many other aspects all affect crop production in a greenhouse. A wide range of factors affect crop output, and it's not easy to build a precise model that accounts for all of them. This investigation makes use of a novel Bayesian optimized artificial neural network (BOANN) to predict the development of greenhouse crops. For this study, diverse datasets of greenhouses from various periods are gathered and preprocessed using min-max normalization to standardize the raw data. Kernel-based principal component analysis (K-PCA) and the wrapper technique are used, respectively, for feature extraction and feature selection. The experimental outcomes of datasets gathered from greenhouses over a range of periods demonstrate that the proposed BOANN approach outperforms other existing approaches in terms of prediction rate, mean square error (MSE), f1-measure, and recall.
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