The plant factory is extensive cultivation to produce a high quality of vegetables under a controllable environment. The concept of Precision Agriculture (PA) was introduced to improve the plant factory production by the implementation of a crop growth monitoring system. Crop growth can be estimated by monitoring of crop height and canopy foliage by the use of computer vision technology. In our previous study, we have introduced a plant height monitoring system based on depth perception using a stereo camera. However, the validity of the various type of leave is necessary to be tested. The objective of this study was to implement the crop growth monitoring system to monitor plant development with various type of leave for system validation and evaluation. The crop growth monitoring system composed of a stereo camera implementing the depth perception for estimating the distance from camera to highest point in the crop. The implementation of the system with various types of leaves and characteristics has been conducted for (a) Samhon, (b) Lettuce, and (c) Pagoda. The developed crop growth monitoring system could perform the time series estimation of crop height with a maximum error of RMSE 0.875 cm on Pagoda, and MAPE of 5.56% on Lettuce. The system demonstrates better estimation on Samhong with minimum error RMSE of 0.408cm and 2.27 % of MAPE. Overall validation for the estimated height vs actual measurement indicates that the coefficient of determination higher than 0.7 means that it has substantial features for estimating the plant height.
|Journal||IOP Conference Series: Earth and Environmental Science|
|Publication status||Published - Aug 6 2020|
|Event||3rd International Conference on Agricultural Engineering for Sustainable Agriculture Production, AESAP 2019 - Bogor, Indonesia|
Duration: Oct 14 2019 → Oct 15 2019
All Science Journal Classification (ASJC) codes
- Environmental Science(all)
- Earth and Planetary Sciences(all)