Calculation of objects thermal imaging parameters from unmanned aerial vehicles

Целью работы является исследование влияния всего комплекса параметров на характеристики получаемых тепловых изображений при съемке поверхности Земли с беспилотных летательных аппаратов. Рассчитаны значения минимальной детектируемой и минимальной разрешаемой разности температур в зависимости от параметров тепловизора, съемки и размеров объекта (пространственной частоты) для трех серийных малогабаритных тепловизоров, используемых при авиационной съемке объектов земной поверхности с беспилотных авианосителей. Аналитические формулы для оценки минимальной разрешаемой разности температур получены на основе математической модели тепловизора как линейной системы отдельных компонентов системы на основе методики, отличающейся от общепринятой. Оценки выполнены для двух случаев: наблюдения теплового изображения оператором на экране дисплея и для случая отсутствия оператора, когда электронное изображение анализируется пороговым алгоритмом. Впервые учтено влияние скорости движения носителя на общую модуляционную передаточную функцию системы и, соответственно, температурное и пространственное разрешения тепловизоров. Основными компонентами, которые необходимо учитывать при расчетах полной модуляционной передаточной функции съемочной системы, являются: объектив тепловизора, приемник излучения, движение носителя и зрительная система наблюдателя. При этом наибольшее влияние на разрешаемые системой температуры оказывают параметры фотоприемника и скорость движения носителя

РАСЧЕТ ПАРАМЕТРОВ ТЕПЛОВИЗИОННОЙ СЪЕМКИ ОБЪЕКТОВ С БЕСПИЛОТНЫХ АВИАНОСИТЕЛЕЙ

This article is aimed at studying the influence of the entire complex parameters on the characteristics of the obtained thermal images when shooting the Earth’s surface from unmanned aerial vehicles. The values of the minimum detectable and minimum resolvable temperature differences are calculated depending on the parameters of the thermal imager, the survey and the size of the object (spatial frequency) for three serial miniature thermal cameras used in aerial surveys of the Earth's surface from unmanned aerial vehicles. Analytical formulas for estimating the minimum resolvable temperature difference are obtained on the basis of a mathematical model of the thermal imager as a linear system of individual components based on the technique that differs from the generally accepted one. Estimates were made for two cases: observation of a thermal image by an operator on a display screen and for the case when an electronic image is analyzed by a threshold algorithm with no operator engaged. For the first time, the influence of the carrier velocity on the overall modulation transfer function of the system and, accordingly, the temperature and spatial resolution of thermal imagers was taken into account. The main components that must be considered when calculating the full modulation transfer function of the system are: a thermal imager lens, a radiation detector, carrier movement and the observer's visual system. Moreover, the parameters of the detector and the speed of the carrier have the greatest influence on the temperatures resolvable by the system.Целью работы является исследование влияния всего комплекса параметров на характеристики получаемых тепловых изображений при съемке поверхности Земли с беспилотных летательных аппаратов. Рассчитаны значения минимальной детектируемой и минимальной разрешаемой разности температур в зависимости от параметров тепловизора, съемки и размеров объекта (пространственной частоты) для трех серийных малогабаритных тепловизоров, используемых при авиационной съемке объектов земной поверхности с беспилотных авианосителей. Аналитические формулы для оценки минимальной разрешаемой разности температур получены на основе математической модели тепловизора как линейной системы отдельных компонентов системы на основе методики, отличающейся от общепринятой. Оценки выполнены для двух случаев: наблюдения теплового изображения оператором на экране дисплея и для случая отсутствия оператора, когда электронное изображение анализируется пороговым алгоритмом. Впервые учтено влияние скорости движения носителя на общую модуляционную передаточную функцию системы и, соответственно, температурное и пространственное разрешения тепловизоров. Основными компонентами, которые необходимо учитывать при расчетах полной модуляционной передаточной функции съемочной системы, являются: объектив тепловизора, приемник излучения, движение носителя и зрительная система наблюдателя. При этом наибольшее влияние на разрешаемые системой температуры оказывают параметры фотоприемника и скорость движения носителя

Application of UAV based high-resolution remote sensing for crop monitoring

Advances in technologies could offer enormous potential for crop monitoring applications, allowing the real-time acquisition of various environmental data. Technology such as high spatio-temporal imagery of unmanned aerial vehicles (UAV’s) can be widely used in crop monitoring applications. These technologies are expected to revolutionize the global agriculture practices, by enabling decision-making during the crop cycle days. Such results allow the effective practice of agricultural inputs, aiding precision agriculture pillars, i.e., applying the right practice in the right place, with the right amount and time. However, the actual exploitation of UAV’s has not been much strong in smart farming, mainly due to the challenges faced during selecting and deploying relevant technologies, including data acquisition and processing methods. The major problem is that there is still no consistent workflow for the use of UAV’s in such areas, as this mechanization is relatively new. In this article, the latest applications of UAV’s for crop monitoring are reviewed. It covers the most common applications, the types of UAV’s used and then we focused on data acquisition methods and technologies, employing the benefit and drawbacks of each. It also indicates the most popular image processing methods and summarizes the potential application in agricultural operations. 

Using High Resolution Images from UAV and Satellite Remote Sensing for Best Management Practice Analyses

Best Management Practices (BMPs) are commonly adopted to ameliorate the quality of runoff and reduce the frequency and intensity of flash floods in urban areas. To date, many of the BMP studies are conducted using coarse resolution data. However, the accuracy of such studies may be compromised due to the shortcomings inherent in the input data; as such, the evaluation of the BMP cost-effectiveness may not be accurate. The objective of this paper is to demonstrate the improvements of higher resolution images over coarse resolution data in BMP analyses. An unmanned aerial vehicle (UAV) was used to collect a more detailed and accurate picture of the digital surface model and digital elevation model. Landsat 8 multi-spectral imagery was classified by object-oriented classification to generate a land use/land cover map. The method used in this study provided more detailed and accurate information of the physical conditions of the study area, an improved subwatershed delineation, a more comprehensive list of the suitable locations for BMPs, and a more reliable estimate of the cost-effectiveness of the BMP ensembles than that generated using coarse resolution data. Using the fine resolution data, this study further determined the utility of the selected BMP ensembles under a changed future climate regime and identified the best BMP and BMP ensemble in reducing urban surface runoff. This method can be especially useful in areas without quality topography and land use data

Early season weed mapping in rice crops using multi-spectral UAV data

In this article, we propose an automatic procedure for classification of UAV imagery to map weed presence in rice paddies at early stages of the growing cycle. The objective was to produce a weed map (common weeds and cover crop remnants) to support variable rate technologies for site-specific weed management. A multi-spectral ortho-mosaic, derived from images acquired by a Parrot Sequoia sensor mounted on a quadcopter, was classified through an unsupervised clustering algorithm; cluster labelling into â weed/no weed classes was achieved using geo-referenced observations. We tested the best set of input features among spectral bands, spectral indices and textural metrics. Weed mapping performance was assessed by calculating overall accuracy (OA) and, for the weed class, omission (OE) and commission errors (CE). Classification results were assessed under an alarmist approach in order to minimise the chance of overestimating weed coverage. Under this condition, we found that best results are provided by a set of spectral indices (OA= 96.5%, weed CE = 2.0%). The output weed map was aggregated to a grid layer of 5 x 5 m to simulate variable rate management units; a weed threshold was applied to identify the portion of the field to be subject to treatment with herbicides. Ancillary information on weed and crop conditions were derived over the grid cells to support precision agronomic management of rice crops at the early stage of growth

CROP HEIGHT ESTIMATION WITH UNMANNED AERIAL VEHICLES

An unmanned aerial vehicle (UAV) can be configured for crop height estimation. In some examples, the UAV includes an aerial propulsion system, a laser scanner configured to face downwards while the UAV is in flight, and a control system. The laser scanner is configured to scan through a two - dimensional scan angle and is characterized by a maxi mum range. The control system causes the UAV to fly over an agricultural field and maintain, using the aerial propulsion system and the laser scanner, a distance between the UAV and a top of crops in the agricultural field to within a programmed range of distances based on the maximum range of the laser scanner. The control system determines, using range data from the laser scanner, a crop height from the top of the crops to the ground

Desenvolvimento de um sistema multiespectral para aplicações na agricultura de precisão, utilizando dispositivos embarcados

Este documento muestra los avances en el desarrollo de prototipos para adquirir información de sensado remoto en vehículos aéreos no tripulados para aplicaciones en agricultura de precisión. Se desarrollaron dos prototipos de cámara multiespectral para las bandas del azul, verde, rojo e infrarrojo cercano, usando las tarjetas Tiva™ C Series LaunchPad  y Raspberry Pi B, con diferencias sustanciales en el tiempo de procesamiento y almacenamiento de las imágenes. En este documento se describe el diseño y desarrollo de un sistema de adquisición de información multiespectral con el objetivo de analizar coberturas vegetales, inicialmente en plantaciones de palma de aceite. Este módulo de adquisición de información en campo se acopla a un Vehículo Aéreo no Tripulado; permitiendo maniobrabilidad en latitud y longitud, para de esta manera mejorar la eficiencia en la adquisición de datos espectrales en lotes pequeños, aumentando la resolución espacial y temporal con un sistema controlado desde tierra. This document shows advances in the development of prototypes to acquire remote sensing information in Unmanned Aerial Vehicles for applications in precision agriculture. We present the development of two prototypes consisting of multispectral cameras for the blue, green, red, and near infrared bands using Tiva® C Series LaunchPad and Raspberry Pi development boards, which presented substantial differences in processing time and images storage. In this document, we describe the design and development of a multispectral information acquisition system to analyze vegetal coverage, initially in an African oil palm plantation. This system couples with an Unmanned Aerial Vehicle, allowing latitude and longitude maneuverability. This improves the data gathering efficiency in small plots, increasing the spatial and temporal resolution from a system controlled on the ground.Este documento mostra o progresso no desenvolvimento de protótipos para adquirir informações de sensoriamento remoto em veículos aéreos não tripulados para aplicações em agricultura de precisão. Foram desenvolvidos dois protótipos de câmera multiespectral para as bandas do azul, verde, vermelho e infravermelho-próximo usando os cartões Tiva ™ C Series LaunchPad e Raspberry Pi B, com diferenças substanciais no tempo de processamento e armazenamento de imagens. Este documento descreve a concepção e o desenvolvimento de um sistema de aquisição de informação multiespectral, com o objetivo de analisar coberturas vegetais, inicialmente em plantações de óleo de palma. Este módulo de aquisição de informações de campo é acoplado a um Veículo Aéreo não tripulado; permitindo capacidade de manobra em latitude e longitude, para melhorar assim a eficiência na obtenção de dados espectrais em pequenos lotes, aumentando a resolução espacial e temporal com um sistema controlado a partir de terra

Signals in the Soil: Subsurface Sensing

In this chapter, novel subsurface soil sensing approaches are presented for monitoring and real-time decision support system applications. The methods, materials, and operational feasibility aspects of soil sensors are explored. The soil sensing techniques covered in this chapter include aerial sensing, in-situ, proximal sensing, and remote sensing. The underlying mechanism used for sensing is also examined as well. The sensor selection and calibration techniques are described in detail. The chapter concludes with discussion of soil sensing challenges

A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction

An early and reliable estimation of crop yield is essential in quantitative and financial evaluation at the field level for determining strategic plans in agricultural commodities for import-export policies and doubling farmer’s incomes. Crop yield predictions are carried out to estimate higher crop yield through the use of machine learning algorithms which are one of the challenging issues in the agricultural sector. Due to this developing significance of crop yield prediction, this article provides an exhaustive review on the use of machine learning algorithms to predict crop yield with special emphasis on palm oil yield prediction. Initially, the current status of palm oil yield around the world is presented, along with a brief discussion on the overview of widely used features and prediction algorithms. Then, the critical evaluation of the state-of-the-art machine learning-based crop yield prediction, machine learning application in the palm oil industry and comparative analysis of related studies are presented. Consequently, a detailed study of the advantages and difficulties related to machine learning-based crop yield prediction and proper identification of current and future challenges to the agricultural industry is presented. The potential solutions are additionally prescribed in order to alleviate existing problems in crop yield prediction. Since one of the major objectives of this study is to explore the future perspectives of machine learning-based palm oil yield prediction, the areas including application of remote sensing, plant’s growth and disease recognition, mapping and tree counting, optimum features and algorithms have been broadly discussed. Finally, a prospective architecture of machine learning-based palm oil yield prediction has been proposed based on the critical evaluation of existing related studies. This technology will fulfill its promise by performing new research challenges in the analysis of crop yield prediction and the development