UAV-Borne Dual-Band Sensor Method for Monitoring Physiological Crop Status

Unearthing soil-plant-microbiota crosstalk: Looking back to move forward

The soil is vital for life on Earth and its biodiversity. However, being a non-renewable and threatened resource, preserving soil quality is crucial to maintain a range of ecosystem services critical to ecological balances, food production and human health. In an agricultural context, soil quality is often perceived as the ability to support field production, and thus soil quality and fertility are strictly interconnected. The concept of, as well as the ways to assess, soil fertility has undergone big changes over the years. Crop performance has been historically used as an indicator for soil quality and fertility. Then, analysis of a range of physico-chemical parameters has been used to routinely assess soil quality. Today it is becoming evident that soil quality must be evaluated by combining parameters that refer both to the physico-chemical and the biological levels. However, it can be challenging to find adequate indexes for evaluating soil quality that are both predictive and easy to measure in situ. An ideal soil quality assessment method should be flexible, sensitive enough to detect changes in soil functions, management and climate, and should allow comparability among sites. In this review, we discuss the current status of soil quality indicators and existing databases of harmonized, open-access topsoil data. We also explore the connections between soil biotic and abiotic features and crop performance in an agricultural context. Finally, based on current knowledge and technical advancements, we argue that the use of plant health traits represents a powerful way to assess soil physico-chemical and biological properties. These plant health parameters can serve as proxies for different soil features that characterize soil quality both at the physico-chemical and at the microbiological level, including soil quality, fertility and composition of soil microbial communities.

Design of Gas Monitoring Terminal Based on Quadrotor UAV

The problem of air pollution is an increasingly serious worldwide. Therefore, in order to better monitor the gas components in the atmosphere, the design of a gas monitoring terminal based on a quadrotor UAV, including software and hardware design, is hereby carried out. Besides, a pump-suction series cavity is designed to reduce the influence of airflow disturbance on the UAV, which is verified to possess a certain anti-interference ability through Computational Fluid Dynamics(CFD) simulation experiments. In addition, a linear regression algorithm is used for sensor calibration and a polynomial piecewise regression method is used for temperature compensation. The experimental results show that the R2 of the model reaches 0.9981, the fitting degree is rather high, and the output is closer to the real gas concentration value after calibration. At the same time, the temperature compensation parameters are determined, which considerably improves the accuracy of the entire hardware terminal. Finally, the vehicle exhaust monitoring experiment is conducted, and the experimental results show that this scheme can successfully detect the exhaust position of the vehicle exhaust under the interference of the downwash flow of the UAV, thereby proving the reliability and accuracy of the monitoring terminal.

Power Allocation and Energy Cooperation for UAV-Enabled MmWave Networks: A Multi-Agent Deep Reinforcement Learning Approach

Unmanned Aerial Vehicle (UAV)-assisted cellular networks over the millimeter-wave (mmWave) frequency band can meet the requirements of a high data rate and flexible coverage in next-generation communication networks. However, higher propagation loss and the use of a large number of antennas in mmWave networks give rise to high energy consumption and UAVs are constrained by their low-capacity onboard battery. Energy harvesting (EH) is a viable solution to reduce the energy cost of UAV-enabled mmWave networks. However, the random nature of renewable energy makes it challenging to maintain robust connectivity in UAV-assisted terrestrial cellular networks. Energy cooperation allows UAVs to send their excessive energy to other UAVs with reduced energy. In this paper, we propose a power allocation algorithm based on energy harvesting and energy cooperation to maximize the throughput of a UAV-assisted mmWave cellular network. Since there is channel-state uncertainty and the amount of harvested energy can be treated as a stochastic process, we propose an optimal multi-agent deep reinforcement learning algorithm (DRL) named Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to solve the renewable energy resource allocation problem for throughput maximization. The simulation results show that the proposed algorithm outperforms the Random Power (RP), Maximal Power (MP) and value-based Deep Q-Learning (DQL) algorithms in terms of network throughput.

Editorial: Special Issue "Emerging Sensor Technology in Agriculture"

Research and innovation activities in the area of sensor technology can accelerate the adoption of new and emerging digital tools in the agricultural sector by the implementation of precision farming practices such as remote sensing, operations, and real-time monitoring [...].

Measurement Method for Height-Independent Vegetation Indices Based on an Active Light Source

A coefficient C_W, which was defined as the ratio of NIR (near infrared) to the red reflected spectral response of the spectrometer, with a standard whiteboard as the measuring object, was introduced to establish a method for calculating height-independent vegetation indices (VIs). Two criteria for designing the spectrometer based on an active light source were proposed to keep C_W constant. A designed spectrometer, which was equipped with an active light source, adopting 730 and 810 nm as the central wavelength of detection wavebands, was used to test the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) in wheat fields with two nitrogen application rate levels (NARLs). Twenty test points were selected in each kind of field. Five measuring heights (65, 75, 85, 95, and 105 cm) were set for each test point. The mean and standard deviation of the coefficient of variation (CV) for NDVI in each test point were 3.85% and 1.39% respectively, the corresponding results for RVI were 2.93% and 1.09%. ANOVA showed the measured VIs possessed a significant ability to discriminate the NARLs and had no obvious correlation with the measurement heights. The experimental results verified the feasibility and validity of the method for measuring height-independent VIs.