Multispectral images (MSIs) are valuable for precision agriculture due to the extra spectral information acquired compared to natural color RGB (ncRGB) images. In this paper, we thus aim to generate high spatial MSIs through a robust, deep-learning-based reconstruction method using ncRGB images. Using the data from the agronomic research trial for maize and breeding research trial for rice, we first reproduced ncRGB images from MSIs through a rendering model, Model-True to natural color image (Model-TN), which was built using a benchmark hyperspectral image dataset. Subsequently, an MSI reconstruction model, Model-Natural color to Multispectral image (Model-NM), was trained based on prepared ncRGB (ncRGB-Con) images and MSI pairs, ensuring the model can use widely available ncRGB images as input. The integrated loss function of mean relative absolute error (MRAE_loss) and spectral information divergence (SID_loss) were most effective during the building of both models, while models using the MRAE_loss function were more robust towards variability between growing seasons and species. The reliability of the reconstructed MSIs was demonstrated by high coefficients of determination compared to ground truth values, using the Normalized Difference Vegetation Index (NDVI) as an example. The advantages of using “reconstructed” NDVI over Triangular Greenness Index (TGI), as calculated directly from RGB images, were illustrated by their higher capabilities in differentiating three levels of irrigation treatments on maize plants. This study emphasizes that the performance of MSI reconstruction models could benefit from an optimized loss function and the intermediate step of ncRGB image preparation. The ability of the developed models to reconstruct high-quality MSIs from low-cost ncRGB images will, in particular, promote the application for plant phenotyping in precision agriculture.

The Internet of Things (IoT) with its plethora of applications brings up new challenges in optimizing power consumption, error performance, latency, and throughput in its communication devices. Recently, there has been increasing necessity of reconfigurable and multistandard transceivers in order to bring adaptability in the IoT devices to suit the nature of the application and satisfy the aforementioned performance metrics as well. In this paper, we focus on the efficient design of receivers compliant with IEEE 802.15.4 which is a prominent protocol for low power IoT applications. We propose a robust phase noise compensation method which efficiently removes the residual phase noise remaining after the coarse frequency offset compensation due to direct-sequence spread spectrum operation on large packets. We also propose a dual-mode receiver using the proposed compensation method and showcase its ability to cater to the diverse nature of IoT applications. The detailed architecture of proposed dual-mode receiver is presented along with its FPGA prototyping and ASIC implementation. We have analyzed overall power consumption by the proposed dual-mode receiver considering the packet error rate and retransmission scenario. The results show that the proposed receiver saves significant energy consumption by changing its mode in favorable channel environments.

In this study, we propose a novel physical layer architecture which is a hybrid of IEEE 802.15.4 and IEEE 802.11b for providing ubiquitous connectivity in IoT remote sensing platforms. The proposed architecture multiplexes the commonly available functional units of IEEE 802.15.4 and IEEE 802.11b, thereby achieving significant area and power savings. In order to achieve this, we modified the existing PHY layer to incorporate QPSK and RC pulse shaping for both the radios during the process of modulation. Performance analysis shows that the proposed architecture resulted in an area savings of 17.2 % and power savings of 17.8 % compared to traditional architecture without any performance degradation. In addition the CMOS transistors count is reduced by 6592, which is a significant reduction in complexity when compared to traditional independent radio's architectures.