Aerial inspection of agricultural regions can provide crucial information to safeguard from numerous obstacles to efficient farming. Farmland anomalies such as standing water, weed clusters, hamper the farming practices, which causes improper use of farm area and disrupts agricultural planning. Monitoring of farmland and crops through Internet-of-Things (IoT)-enabled smart systems has potential to increase the efficiency of modern farming techniques. Unmanned Aerial Vehicle (UAV)-based remote sensing is a powerful technique to acquire farmland images on a large scale. Visual data analytics for automatic pattern recognition from the collected data is useful for developing Artificial intelligence (AI)-assisted farming models, which holds great promise in improving the farming outputs by capturing the crop patterns, farmland anomalies and providing predictive solutions to the inherent challenges faced by farmers. In this work, we propose a deep learning framework AgriSegNet for automatic detection of farmland anomalies using multiscale attention semantic segmentation of UAV acquired images. The proposed model is useful for monitoring of farmland and crops to increase the efficiency of precision farming techniques.

The demand for miniaturized point‐of‐care chemical/biochemical sensors has driven the development of field‐effect transistors (FETs) based pH sensors over the last 50 years. This paper aims to review the fabrication technologies, device structures, sensing film materials, and modeling techniques utilized for FET‐based pH sensors. We present the governing principles of potentiometric sensors, with major focus on the working principles of ion‐sensitive FETs (ISFETs). We extensively review different sensing film materials deposited by various techniques, which is critical to the sensing performance of ISFETs. The popular fabrication technologies have been presented, with special emphasis on state‐of‐the‐art silicon‐on‐insulator based technology, which can achieve high sensitivity by utilizing the dual‐gate effect. Furthermore, recent advancements in nano‐ISFETs has been elucidated. We also discuss the adoption of unmodified complementary metal‐oxide semiconductor (CMOS) ISFETs using standard CMOS processes, which has enabled the fabrication of integrated ISFET arrays, which are especially suited for ion‐imaging applications. Moreover, recent developments in extended‐gate FETs has been discussed, which have gained lot of attention due to their design flexibility and ease of fabrication, which is desirable for wearable sensing applications. In addition, recently there have been efforts to utilize nonsilicon channel materials for pH‐sensing application to obtain superior performance and various channel materials have been reviewed. Finally, we have extensively reviewed the ISFET device modeling and simulation techniques using various computer‐aided design tools, which aid in sensor design and characterization.

Unmanned Aerial Vehicles (UAVs) are the promising “Flying IoT” devices of the future, which can be equipped with various sensors and cognitive capabilities to perform numerous tasks related to remote sensing, search and rescue operations, object tracking, segmentation of roads and buildings, surveillance, etc. However, these AI-driven tasks require heavy computation and may lead to suboptimal performance with embedded processors on a power-constrained battery-operated drone. This work proposes a novel deep learning approach for performing robust semantic segmentation of aerial scenes captured by UAVs. In our setup, the power-constrained drone is used only for data collection, while the computationally intensive tasks are offloaded to a GPU cloud server. Our architecture performs robust semantic segmentation by learning the segmentation maps from jointly utilizing of aerial scenes along with the respective “elevation maps” in a semi-supervised approach. We propose a three-tier deep learning architecture, wherein the first module aims at preliminary feature extraction from aerial scenes using a backbone feature extractor. The second module captures the spatial dependency between the aerial scenes and their respective elevation maps to obtain better semantic information, which is achieved by a bi-directional LSTM. The third module is aimed at enhancing the performance of semantic segmentation through a semi-supervised approach with an encoder to generate segmentation maps and a decoder to reconstruct feature maps. This semi-supervised feature learning ensures robust extraction along with scalability. The proposed architecture was validated on real-world aerial datasets and achieves state-of-the-art results for aerial image segmentation.