This paper proposes AgriTalk, an inexpensive IoT platform for precision farming of soil cultivation. We conduct experiments on turmeric cultivation, which indicates that the turmeric quality is significantly enhanced through AgriTalk. Specifically, the curcumin concentration is up to 4500-5500 mg/100g, which is 5 times more than existing products. We demonstrate how to intuitively configure the connections between the sensors and the actuators with the desired farming intelligence, and to effectively maintain AgriTalk for precision farming. We conduct measurement, analytic analysis, and simulation experiments to investigate the IoT message delays of AgriTalk. Our study indicates that the delays for automatic control and automatic-manual control switching with long distances (more than 30 Km) are very short (less than 0.2 seconds) and AgriTalk can easily respond to quick and dynamic change of the field environment conditions in soil cultivation.

The paddy agronomy in the Asia-pacific region has gained a prominent role in connection with the major rice production area in over the decades. The research aims to investigate the aerial computing techniques to improve the sky farming techniques. Recently, the enhancement of unmanned aerial vehicle (UAV) and Internet of Things (IoT) with Deep Learning (DL) in paddy agronomy research has ensured the impact on data availability and predictive analytics. In this research, we focus on Deep Learning (DL) for identifying weeds, regions of crop failure, and crop health in paddy crops. Therefore, a DL architecture suitable for application in aerial computing UAV onboard intelligence is necessary. Furthermore, the DL architecture should be stable and consume as few computational resources as possible, given that it is applied on the UAV's onboard system. This paper proposes to use Tiny YOLO (T-Yolo)V4 as the base detector via following modules: (a) YOLO detection layer is added to the T-YOLO v4 to make the network more capable of detecting small objects. (b) Spatial pyramid pooling (SPP), convolutional block attention module (CBAM), Sand Clock Feature Extraction Module (SCFEM), Ghost modules, and more convolutional layers are added to the network to increase the accuracy of the network. Subsequently, a rice leaf diseases data set which contains the labeled images of rice leaf diseases such as Bacterial leaf blight, Rice blast, and brown spot is obtained. In addition, the image augmentations is applied to produce more samples of the three classes to create our own rice leaf diseases data set. Finally, the enhanced UAV Tiny Yolo Rice (UAV T-yolo-Rice) network has obtained the testing mean average precision (mAP) as $86 \%$ by training the proposed rice leaves' disease data set. More experimental results reveal that our proposed method outperforms the Rice Leaves' Diseases detection model by using the proposed UAV T-yolo-Rice network set can obtain the highest testing Mean Average Precision (mAP) than all the other models from previous studies. Even the Yolo V7 model produced by darknet cannot have the testing accuracy that is higher than the proposed network.

At present, drone delivery systems usually function only in global positioning system (GPS)-friendly environments and thus cannot deliver goods into customers’ houses, which is an element of urban last-mile delivery. In this study, we investigated solutions for enabling drones to fly autonomously in mixed indoor–outdoor environments. We propose a novel Internet of Things (IoT)-based drone delivery system, namely DroneTalk, for mail delivery. DroneTalk combines a GPS, an inertial measurement unit, and visual information to achieve mixed indoor–outdoor autopilot operation. Furthermore, DroneTalk integrates an autonomous drone control system with an IoT device management platform to enable a drone to achieve automatic online weather awareness. Most importantly, DroneTalk is a novel work considering communication, processing, and control latencies simultaneously during autopilot operation to avoid problems related to drone collisions. These problems have always existed but become more serious when drones are flying close to buildings. Simulation results indicate that the proposed system can attain a high user-defined flight success rate (i.e., 99% in our simulations) without any collisions; thus, the proposed system can be feasibly used in real-world environments.

IoTtalk is a platform for Internet of Things (IoT) device interaction, which nicely integrates a reconfigurable multi-sensor device called MorSensor with the proposed IoT management platform in the network domain. The sensors can be dynamically plugged in/out of a MorSensor device without being turned off, and IoTtalk automatically generates/reuses the application software for these sensors. We propose a dynamic ranging concept that automatically specifies the value range of a sensor so that it can send “meaningful” data to any connected output IoT device. Therefore, a MorSensor device can “talk” to other IoT devices, such as a light bulb or an electric fan. We also show that IoTtalk is a simple yet almost free solution for automatic sensor calibration. Finally, we illustrate that IoTtalk is a powerful tool for developing interactive science experiments.

Many people feed the pet fish in the aquarium tanks that need to be properly set up and maintained, or the fish will be destined to an unpleasant and short life. Therefore, it is critical to monitor water conditions closely and improve the water quality for the mini aquarium tanks. Based on an IoT solution called IoTtalk, this paper proposes the FishTalk system that utilizes the aquarium sensors to drive the actuators in real time. We describe the relationship between the aquarium sensors and the actuators and give concrete examples about the threshold setting. Our solution allows the designer to quickly deploy intelligent control for various water conditions. As an example, we implement an intelligent fish feeding mechanism such that the fish is neither over nor under fed, and at the same time, the fish owner can enjoy watching fish feeding remotely. We have also developed the analytic model, simulation, and measurement experiments to investigate the effects of the IoT message delays and loss on water condition control.