Plant growth prediction model for lettuce (Lactuca sativa.) in plant factories using artificial neural network

A. Rizkiana, A. P. Nugroho, N. M. Salma, S. Afif, R. E. Masithoh, L. Sutiarso, T. Okayasu

研究成果: ジャーナルへの寄稿会議記事査読

6 被引用数 (Scopus)


One of the applications of precision agriculture is the monitoring of plant growth in a plant factory production to observe the behavior and predict the estimated yield of plant production. Plant growth is unique and is affected by internal and external factors, such as environmental conditions and nutrition supply. The estimation of plant growth considering the environmental conditions as well as initial plant height is necessary for plant management during the production cycle. Therefore, to answer the challenge, the purpose of this study was to develop a model of plant growth prediction using the resilient backpropagation Artificial Neural Network (ANN) method with environmental parameter input at the plant factory and evaluate the model. The ANN model was tested using a different number of nodes at the hidden layer, which are 1 to 7 nodes with the input of daily average temperature, average daily humidity, EC, and light intensity and then produces high lettuce increase output for 45 days. The model was developed and tested using the lettuce (Lactuca sativa) in plant factory production. As a result of the evaluation, the best prediction model with ANN is using the network architecture 4-7-1 with the results of the interpretation of R2 on the training data, and testing data are 0.987 and 0.728. From the verification test of the developed model, it can be found that the most affecting way to optimize lettuce growth is the rate of EC in nutrition. The results of the RMSE model validation is 0.032. Accordingly, the developed model can be used to predict the height increase of Lettuce (Lactuca sativa) plants in a plant factory.

ジャーナルIOP Conference Series: Earth and Environmental Science
出版ステータス出版済み - 5月 4 2021
イベント4th International Conference on Green Agro-industry and Bioeconomy, ICGAB 2020 - Malang, Virtual, インドネシア
継続期間: 8月 25 2020 → …

!!!All Science Journal Classification (ASJC) codes

  • 環境科学(全般)
  • 地球惑星科学(全般)


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