Retrieval of horticultural crop morphology from color based on Elman neural network

Authors

  • Chen Cheng 1. College of Ecology, Lishui University, Lishui 323000, Zhejiang, China 2. College of Resources and Environment Sciences, China Agricultural University, Beijing 100193, China
  • Yue Lyu 1. College of Ecology, Lishui University, Lishui 323000, Zhejiang, China
  • Liping Feng 3. Beidahuang Information Co., Ltd., Harbin 130499, China
  • Haixia Qin 1. College of Ecology, Lishui University, Lishui 323000, Zhejiang, China
  • Xiaqi Che 1. College of Ecology, Lishui University, Lishui 323000, Zhejiang, China
  • Sining Chen 4. State Key Laboratory of Severe Weather Meteorological Science and Technology, Chinese Academy of Meteorological Sciences, Beijing 100081, China
  • Xianguan Chen 5. College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou 350002, China
  • Chengsen Zhao 1. College of Ecology, Lishui University, Lishui 323000, Zhejiang, China
  • Fenghua Ding 1. College of Ecology, Lishui University, Lishui 323000, Zhejiang, China
  • Zhenfa Li 6. Tianjin Climate Center, Tianjin 300074, China

DOI:

https://doi.org/10.25165/ijabe.v18i5.8435

Keywords:

horticultural crop, morphology, color, Elman neural network, root, stem and leaf

Abstract

The quantification of the relationship between morphological and color indicators in various organs of horticultural crops is of great significance for crop digital visualization research using computer vision technology. To study this relationship, observational data from a six-year experiment were collected, focusing on seven kinds of color component values of different organs including root, stem, and leaf. Using the collected color data as input, a simulation model was established based on the Elman neural network for six horticultural crops including zizania, cucumber, celery, spinach, parsley, and tea. Results indicated that the horticultural crop morphology model based on the Elman neural network exhibited high simulation accuracy with root mean square error (RMSE) ranging from 0.14 to 1.05 cm and normalized root mean square error (NRMSE) ranging from 2.02% to 11.34% for the maximum root length simulation model. The simulation model for stem length and diameter had an RMSE ranging from 1.42 to 4.96 cm and 0.25 to 1.17 mm, respectively, with NRMSE ranging from 18.19% to 25.65% and 15.13% to 27.25%, respectively. Similarly, chlorophyll content, leaf length, leaf width, and leaf area simulation models exhibited RMSE ranging from 2.80 to 8.22 SPAD, 0.44 to 18.04 cm, 0.22 to 3.49 cm, and 0.25 to 36.39 cm2, respectively, with NRMSE ranging from 8.63% to 21.04%, 15.00% to 22.87%, 15.12% to 33.58%, and 6.88% to 24.90%, respectively. These findings provide essential theoretical support for precision agriculture in areas of water and fertilizer management, plant growth diagnosis, and yield prediction. Keywords: horticultural crop, morphology, color, Elman neural network, root, stem and leaf DOI: 10.25165/j.ijabe.20251805.8435 Citation: Cheng C, Lyu Y, Feng L P, Qin H X, Che X Q, Chen S N, et al. Retrieval of horticultural crop morphology from color based on Elman neural network. Int J Agric & Biol Eng, 2025; 18(5): 259–267.

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Published

2025-10-27

How to Cite

Cheng, C., Lyu, Y., Feng, L., Qin, H., Che, X., Chen, S., … Li, Z. (2025). Retrieval of horticultural crop morphology from color based on Elman neural network. International Journal of Agricultural and Biological Engineering, 18(5), 259–267. https://doi.org/10.25165/ijabe.v18i5.8435

Issue

Vol. 18 No. 5 (2025): IJABE

Section

Information Technology, Sensors and Control Systems

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