TY - GEN
T1 - Early Detection of Toppling Susceptibility in Tulip Using Spectral Imaging
AU - Hageraats, Selwin
AU - Van Vilsteren, Sjoerd
AU - Polder, Gerrit
AU - Trompert, John
AU - Wildschut, Jeroen
PY - 2022/11
Y1 - 2022/11
N2 - Forcing tulips is a process that mimics the conditions of winter and early spring in a controlled environment to make the tulips bloom months earlier than they would if grown in the open field. In this process, it is essential that the relative humidity in the growing environment is kept sufficiently low. Otherwise, there is an increased risk of the tulips losing solidity and eventually toppling over. Since maintaining this lower humidity demands a lot of energy, it is most cost-efficient to maintain the highest possible relative humidity while still maintaining a rigid flower stem. In order to find the optimal balance between stem rigidity and energy consumption, a monitoring technique is developed that can recognize early signs of the type of high-humidity exposure that eventually leads to toppling. The monitoring technique is based on spectral imaging and a linear classifier that predicts whether the tulips have been forced in an adequately ventilated environment or a poorly ventilated, high-RH enclosure. The model's classification accuracy was found to reach 100% 6-10 days before toppling occurs.
AB - Forcing tulips is a process that mimics the conditions of winter and early spring in a controlled environment to make the tulips bloom months earlier than they would if grown in the open field. In this process, it is essential that the relative humidity in the growing environment is kept sufficiently low. Otherwise, there is an increased risk of the tulips losing solidity and eventually toppling over. Since maintaining this lower humidity demands a lot of energy, it is most cost-efficient to maintain the highest possible relative humidity while still maintaining a rigid flower stem. In order to find the optimal balance between stem rigidity and energy consumption, a monitoring technique is developed that can recognize early signs of the type of high-humidity exposure that eventually leads to toppling. The monitoring technique is based on spectral imaging and a linear classifier that predicts whether the tulips have been forced in an adequately ventilated environment or a poorly ventilated, high-RH enclosure. The model's classification accuracy was found to reach 100% 6-10 days before toppling occurs.
U2 - 10.1016/j.ifacol.2022.11.132
DO - 10.1016/j.ifacol.2022.11.132
M3 - Conference paper
VL - 55
T3 - IFAC-PapersOnline
SP - 159
EP - 164
BT - 7th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture AGRICONTROL 2022
A2 - Oksanen, T.
PB - IFAC
T2 - 7th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture AGRICONTROL 2022
Y2 - 14 September 2022 through 16 September 2022
ER -