6. Technical data

The custom-designed data acquisition and control system [17] for monitoring and manipulating of the microclimate parameters was built using Arduino Uno prototype board utilizing ATmega328P (Atmel®, San Jose, CA) microcontroller on the open source Arduino Uno prototyping platform programmable in Arduino sketch environment software with C (C Compiler, Brookfield, WI), a liquid crystal display, power supply, and serial port RS-232 communication cable (bidirectional with a maximum baud speed up to 115,200 bites per seconds) for transferring and storing collected data in PC. All vital components (i.e., clock generator, 2 KB of RAM, 32 KB of flash memory for storing programs and 1 KB of EEPROM for storing parameters, a 16-MHz crystal oscillator, digital input/output pins, USB connection, power regulator, power jack, and a reset button) for operating the microcontroller, as well as direct programming and access to input/output pins, were available on the prototype board. Four arrays of HSM-20G-combined sensors modules (Shenzhen Mingjiada Electronics LTD, Futian Shenzhen, China), external micro-secure digital (SD) cardboard for storing larger amount of sensor data, output connection, sensor input, and relay circuit board for on/off control purposes were used. The data acquisition interface was tested for accuracy and reliability with available commercial models, and with a control sample data collected from airport weather station at Sultan Abdul Aziz Shah-Subang in Malaysia.

$$P\_h = \frac{D \cdot LF\_{\text{max}} \cdot PGRED(T)}{K} \cdot \ln\left[\frac{(1-m)\cdot LF\_{\text{max}} + Q\_e(T)\cdot K \cdot PPFD}{(1-m)\cdot \cdot LF\_{\text{max}} + Q\_e(T)\cdot K \cdot PPFD \cdot \exp(-k\cdot LAI)}\right] \tag{1}$$

$$\begin{cases} \frac{d(LAI)}{dt} = \rho . \delta . \lambda (T\_d) . \frac{\exp\left[\beta . (N - N\_b)\right]}{1 + \exp\left[\beta . (N - N\_b)\right]} . \frac{dN}{dt} \quad : LAI \le LAI\_{\text{max}}\\\\ \frac{d(LAI)}{dt} = 0 & : LAI \ge LAI\_{\text{max}} \end{cases} \tag{2}$$

$$\frac{d\mathcal{W}\_{\rm F}}{dt} = \mathcal{G}\mathcal{R}\_{\rm net}a\_{\rm F}f\_{\rm F}(T\_d). \left[1 - \exp\left(-\mathfrak{F}(N - N\_{\rm FF})\right)\right] \cdot \mathcal{g}\left(T\_{\rm adaptive}\right) \text{ if } N > N\_{\rm FF} \tag{3}$$

$$\log\left(T\_{daytime}\right) = \max\left(0.09, \min\left(1, 1 - 0.154\left(T\_{daytime} - T\_{crit}\right)\right)\right) \tag{4}$$

$$\frac{d\mathcal{W}\_{\rm M}}{dt} = D\_{\rm F}(T\_d).(\mathcal{W}\_{\rm F} - \mathcal{W}\_{\rm M}), \text{ if } N > N\_{\rm FF} + \kappa\_{\rm F} \tag{5}$$

$$\alpha\_{\mathcal{Y}} = \bigcap\_{\alpha=0}^{\alpha=a\_{\mathcal{t}}} \mathbb{C}f t(\mathcal{M}, t, \alpha).d\alpha = \sum\_{i=1}^{N} \mathbb{C}f t(\mathcal{M}, t, \alpha\_i) \times \alpha\_i \tag{6}$$

$$ADP(\mathcal{M}, \alpha\_s) \ = 1 - 2\left(\int\_{a=0}^{a=a\_s} \alpha .d\alpha - \int\_{a=0}^{a=a\_s} Output(\mathcal{M}) .d\alpha\right) \tag{7}$$

#### Acknowledgements

The financial support provided by the University of Putra Malaysia, Grant Number GP-IPB/ 2013/9415600, and the scientific comments and suggestions from Professor Warren Dixon, Professor Jim Jones, and Professor Ray Bucklin at the University of Florida, and Professor Jan Bontsema at the Wageningen University and Research Center are duly acknowledged.
