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.

Rice (Oryza sativa L.) is one of the most important staple crops in China. Increasing atmospheric greenhouse gas concentrations and associated climate change may greatly affect rice production. We assessed the potential impacts of climate change on cold rice production in the Heilongjiang province, one of China's most important rice production regions. Data for a baseline period (1961–1990) and the period 2010–2050 in A2 and B2 scenarios were used as input to drive the rice model ORYZA2000 with and without accounting for the effects of increasing atmospheric CO₂ concentration. The results indicate that mean, maximum, and minimum temperature during the rice growing season, in the future period considered, would increase by 1.8 ℃ under the A2 scenario and by 2.2 ℃ under the B2 scenario compared with those in the baseline. The rate of change in average maximum and minimum temperatures would increase by 0.6 ℃ per 10-year period under the A2 scenario and by 0.4 ℃ per 10-year period under the B2 scenario. Precipitation would increase slightly in the rice growing season over the next 40 years. The rice growing season would be shortened and the yield would increase in most areas in the Heilongjiang province. Without accounting for CO₂ effect, the rice growing season in the period 2010–2050 would be shortened by 4.7 and 5.8 days, and rice yields would increase by 11.9% and 7.9%, under the A2 and B2 scenarios, respectively. Areas with simulated rice yield increases greater than 30.0% were in the Xiaoxing'an Mountain region. The simulation indicated a decrease in yield of less than 15% in the southwestern Songnen Plain. The rate of change in simulated rice yield was 5.0% and 2.5% per 10 years under the A2 and B2 scenarios, respectively. When CO₂ effect was accounted for, rice yield increased by 44.5% and 31.3% under the A2 and B2 scenarios, respectively. The areas of increasing yield were sharply expanded. The area of decreasing yield in the western region of Songnen Plains disappeared when increasing CO₂ concentration was considered. The stability of rice yield would increase from 2010 to 2050. Overall, the simulation indicates that rice production will be affected positively by climate change in the next 40 years in the Heilongjiang province, China.