Development of a Near Ground Remote Sensing System

Abstract

Unmanned Aerial Vehicles (UAVs) have shown great potential in agriculture and are increasingly being developed for agricultural use. There are still a lot of experiments that need to be done to improve their performance and explore new uses, but experiments using UAVs are limited by many conditions like weather and location and the time it takes to prepare for a flight. To promote UAV remote sensing, a near ground remote sensing platform was developed. This platform consists of three major parts: (1) mechanical structures like a horizontal rail, vertical cylinder, and three axes gimbal; (2) power supply and control parts; (3) onboard application components. This platform covers five degrees of freedom (DOFs): horizontal, vertical, pitch, roll, yaw. A stm32 ARM single chip was used as the controller of the whole platform and another stm32 MCU was used to stabilize the gimbal. The gimbal stabilizer communicates with the main controller via a CAN bus. A multispectral camera was mounted on the gimbal. Software written in C++ language was developed as the graphical user interface. Operating parameters were set via this software and the working status was displayed in this software. To test how well the system works, a laser distance meter was used to measure the slide rail’s repeat accuracy. A 3-axis vibration analyzer was used to test the system stability. Test results show that the horizontal repeat accuracy was less than 2 mm; vertical repeat accuracy was less than 1 mm; vibration was less than 2 g and remained at an acceptable level. This system has high accuracy and stability and can therefore be used for various near ground remote sensing studies.

Monitoring Seasonal Pasture Quality Degradation in the Mediterranean Montado Ecosystem: Proximal versus Remote Sensing

“…Over the past few years, important developments have been achieved in sensor technology and analytical algorithms, enabling RS to supply comprehensive data for pastures management [5]. These emerging technologies that facilitate the mapping and analysis of pasture variability [17] are more and more crucial for sustainable animal production. Evaluation of variability is both the first important step and a required condition in the implementation of "Precision Agriculture" (PA) technologies.…”

Strawberry Yield Prediction Based on a Deep Neural Network Using High-Resolution Aerial Orthoimages

“…Zarco-Tejada et al [21] used high resolution hyperspectral imagery acquired from a UAV for leaf carotenoid content estimation. Several reasons can be drawn for the popularity of UAVs: (1) a drone's flying height can be controlled within 0.5-500 m, so it can get closer to the ground and obtain higher-resolution images [22]; (2) UAVs have strong environmental adaptability and low requirements for weather conditions [23]-they can capture high quality images even on cloudy or rainy days [24]; (3) they only need a small amount of space to take off (multi-rotor and helicopters take off and land vertically, while fixed-wing ones can take off via ejection and land via parachute [25]) and, thus, there is no need for an airport or launch center for UAVs; and (4) drones are becoming cheaper and easier to carry. Their modular designs make them easy to modify for various tasks in different situations [26].…”

A Holistic Approach to the Evaluation of the Montado Ecosystem Using Proximal Sensors

“…At the core of PA is the effective management of spatial and temporal variability related to all aspects of agricultural production for the purpose of improving crop performance and environmental quality [ 11 ]. This in turn requires the availability of efficient and accurate techniques for measuring within-field variations in soil properties and crop development at a very fine spatial scale [ 12 ]. Assessing variability is the first critical step and a necessary condition in PA, therefore, any PA system must first address the availability of measurement technologies that allow the mapping and understanding of this variability.…”

“…[ 7 ]. Usually, the first approach is based on soil and/or relief information, derived from topographic maps, direct soil sampling, non-invasive sampling of soil apparent electrical conductivity (EC a ) measured by electromagnetic induction or electrical resistivity sensors, and crop canopy characteristics measured by proximal or remote sensing sensors [ 8 , 12 ]. Normalized difference vegetation index (NDVI) is the most common crop parameter used in MZ delineation, which can be derived from satellite, airplane, or unmanned aerial vehicle (UAV) imagery, or can be created using commercial proximal optical crop sensors such as Crop Circle, Yara N-Sensor, GreenSeeker [ 8 ] or OptRx.…”