2020, Yadegari, Mohammad, Shamshiri, Redmond R., Shariff, Abdul Rashid Mohamed, Balasundram, Siva K., Mahns, Benjamin

Environmental concerns are growing about excessive applying nitrogen (N) fertilizers, especially in oil palm. Some conventional methods which are used to assess the amount of nutrient in oil palm are time-consuming, expensive, and involve frond destruction. Remote sensing as a non-destructive, affordable, and efficient method is widely used to detect the concentration of chlorophyll (Chl) from canopy plants using several vegetation indices (VIs) because there is an influential relation between the concentration of N in the leaves and canopy Chl content. The objectives of this research are to (i) evaluate and compare the performance of various vegetation indices (VIs) for measuring N status in oil palm canopy using SPOT-7 imagery (AIRBUS Defence & Space, Ottobrunn, Germany) to (ii) develop a regression formula that can predict the N content using satellite data to (iii) assess the regression formula performance on testing datasets by testing the coefficient of determination between the predicted and measured N contents. SPOT-7 was acquired in a 6-ha oil palm planted area in Pahang, Malaysia. To predict N content, 28 VIs based on the spectral range of SPOT-7 satellite images were evaluated. Several regression models were applied to determine the highest coefficient of determination between VIs and actual N content from leaf sampling. The modified soil-adjusted vegetation index (MSAVI) generated the highest coefficient of determination (R2 = 0.93). MTVI1 and triangular VI had the highest second and third coefficient of determination with N content (R2 = 0.926 and 0.923, respectively). The classification accuracy assessment of the developed model was evaluated using several statistical parameters such as the independent t-test, and p-value. The accuracy assessment of the developed model was more than 77%.

2020, Yamin, Muhammad, Ishak bin Wan Ismail, Wan, Saufi bin Mohd Kassim, Muhamad, Abd Aziz, Samsuzana Binti, Akbar, Farah Naz, Shamshiri, Redmond R., Ibrahim, Muhammad, Mahns, Benjamin

It is the need of time that oil palm farmers must perform the spatially planned soil analysis to know about the fertilizer sufficient and deficient zones of land. Colorimetric method is a suitable and fast solution of soil analysis for NPK determination using the digital soil test kit. NPK determination procedure with a digital soil test kit was undefined for oil palm. Furthermore, the digital soil test kit determines the passage of light through an opaque medium of soil solution with a specified reagent. Therefore, environmental light may interfere leading to wrong results of NPK measurement. Likewise, this equipment was non-incorporable with the controller of any VRT fertilizer applicator. In this research, these issues were addressed and the NPK measurement procedure was defined for oil palm plantation by modifying the ‘soil to water’ ratio in sample soil solution with an optimum environmental light range of 18-23 W/m2. ‘Soil to water’ ratios were found for nitrogen, phosphorus and potassium as 0.31 to 5.00, 1.00 to 5.00 and 4.50 to 5.00, respectively to fit the requirement of NPK for oil palm in the prescribed range of the equipment. Validation study of modified digital soil test kit showed that 91.7% N, 89.6% P and 93.8% K results of modified digital soil test kit were matched with analytical laboratory method. Thus, the reliability of NPK results using digital soil test kit was enhanced, making the kit incorporable with the controller of variable rate fertilizer applicator through remote monitoring based data acquisition system. The outcome of this research can be used in the development of an IoT network data fusion for dynamic assessment of the NPK variation in the soil and nutrient management in oil palm plantations.

2020, Rezvani, Sayed Moin-eddin, Abyaneh, Hamid Zare, Shamshiri, Redmond R., Balasundram, Siva K., Dworak, Volker, Goodarzi, Mohsen, Sultan, Muhammad, Mahns, Benjamin

Optimum microclimate parameters, including air temperature (T), relative humidity (RH) and vapor pressure deficit (VPD) that are uniformly distributed inside greenhouse crop production systems are essential to prevent yield loss and fruit quality. The objective of this research was to determine the spatial and temporal variations in the microclimate data of a commercial greenhouse with tomato plants located in the mid-west of Iran. For this purpose, wireless sensor data fusion was incorporated with a membership function model called Optimality Degree (OptDeg) for real-time monitoring and dynamic assessment of T, RH and VPD in different light conditions and growth stages of tomato. This approach allows growers to have a simultaneous projection of raw data into a normalized index between 0 and 1. Custom-built hardware and software based on the concept of the Internet-of-Things, including Low-Power Wide-Area Network (LoRaWAN) transmitter nodes, a multi-channel LoRaWAN gateway and a web-based data monitoring dashboard were used for data collection, data processing and monitoring. The experimental approach consisted of the collection of meteorological data from the external environment by means of a weather station and via a grid of 20 wireless sensor nodes distributed in two horizontal planes at two different heights inside the greenhouse. Offline data processing for sensors calibration and model validation was carried in multiple MATLAB Simulink blocks. Preliminary results revealed a significant deviation of the microclimate parameters from optimal growth conditions for tomato cultivation due to the inaccurate timer-based heating and cooling control systems used in the greenhouse. The mean OptDeg of T, RH and VPD were 0.67, 0.94, 0.94 in January, 0.45, 0.36, 0.42 in June and 0.44, 0.0, 0.12 in July, respectively. An in-depth analysis of data revealed that averaged OptDeg values, as well as their spatial variations in the horizontal profile were closer to the plants’ comfort zone in the cold season as compared with those in the warm season. This was attributed to the use of heating systems in the cold season and the lack of automated cooling devices in the warm season. This study confirmed the applicability of using IoT sensors for real-time model-based assessment of greenhouse microclimate on a commercial scale. The presented IoT sensor node and the Simulink model provide growers with a better insight into interpreting crop growth environment. The outcome of this research contributes to the improvement of closed-field cultivation of tomato by providing an integrated decision-making framework that explores microclimate variation at different growth stages in the production season.