Providing Aerial Images Through UAVs

Fact sheet outlining newer technologies in UAVs for agriculture.Precision agriculture relies on accurate maps of soil properties and yield potential to uncover high- and low-yielding sections of a field. Some of these maps are obtained by remote sensing (gathering information from a distance), such as photography. Since its inception, precision agriculture has relied on remote sensing by planes and satellites to obtain various types of photos.MAES, UMD-AGN

Information systems focused on precision agricultural technologies applicable to sugar cane, a review

Los cultivos de caña de azúcar son una de las principales actividades económicas en Colombia, por ende son esenciales para el desarrollo agrícola del país. Además, las Tecnologías de la Información y las Comunicaciones (TIC) se han empezado a utilizar e implementar en todo el ciclo de vida del cultivo. Consiguientemente, las TIC son importantes al momento de definir sistemas basados en Agricultura de Precisión (AP), capaces de incrementar el rendimiento del cultivo y optimizar el uso de recursos económicos y de fertilizantes, entre otras funciones. Este artículo presenta una revisión acerca de sistemas de información basados en AP y aplicables a cultivos de caña de azúcar, haciendo énfasis en las tecnologías utilizadas, la gestión de datos y sus arquitecturas. Asimismo, se presenta la propuesta de los autores: un sistema de información integral de tres capas basado en AP, capaz de facilitar la optimización en distintas etapas del ciclo de vida de la caña de azúcar. El artículo concluye describiendo el trabajo futuro y el desarrollo de la implementación del sistema propuesto.Crops of sugar cane are one of the main economic activities in Colombia. Hence, this kind of crops is essential for the agricultural development of the country. Additionally, information and communication technologies (ICTs) are currently used and implemented throughout the entire life of the crop. Therefore, ICTs are important at the time of defining PA-based systems, capable of increasing crop efficiency and optimizing use of economic resources as fertilizers, water, and pesticides, among other functions. This article presents a review about the PA-based information systems applicable to sugar cane crops and making emphasis on technologies used, data management, and their architectures. Besides, the article makes a proposal of authors: a AP-based three-layer integral information system capable of facilitating optimization in different life stages of the sugar cane. This article concludes by describing the future work and the implementation of the system proposed

Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development

Unmanned Aerial Vehicles and Systems (UAV or UAS) have become increasingly popular in recent years for agricultural research applications. UAS are capable of acquiring images with high spatial and temporal resolutions that are ideal for applications in agriculture. The objective of this study was to evaluate the performance of a UAS-based remote sensing system for quantification of crop growth parameters of sorghum (Sorghum bicolor L.) including leaf area index (LAI), fractional vegetation cover (fc) and yield. The study was conducted at the Texas A&M Research Farm near College Station, Texas, United States. A fixed-wing UAS equipped with a multispectral sensor was used to collect image data during the 2016 growing season (April±October). Flight missions were successfully carried out at 50 days after planting (DAP; 25 May), 66 DAP (10 June) and 74 DAP (18 June). These flight missions provided image data covering the middle growth period of sorghum with a spatial resolution of approximately 6.5 cm. Field measurements of LAI and fc were also collected. Four vegetation indices were calculated using the UAS images. Among those indices, the normalized difference vegetation index (NDVI) showed the highest correlation with LAI, fc and yield with R2 values of 0.91, 0.89 and 0.58 respectively. Empirical relationships between NDVI and LAI and between NDVI and fc were validated and proved to be accurate for estimating LAI and fc from UAS-derived NDVI values. NDVI determined from UAS imagery acquired during the flowering stage (74 DAP) was found to be the most highly correlated with final grain yield. The observed high correlations between UAS-derived NDVI and the crop growth parameters (fc, LAI and grain yield) suggests the applicability of UAS for withinseason data collection of agricultural crops such as sorghum

Protótipo e método para análise de cultivos no espectro visível e infravermelho a partir de um veículo aéreo multirotor

Plant health has a direct impact on the quality and quantity of agricultural products. Due to this fact, farmers must monitor crop conditions frequently. However, the current tools for achieving this are complex and inaccessible. Therefore, this article proposes a method for the characterization of crops that allows to monitor the plants using photographs in the visible and infrared spectrum acquired from a multi-rotor air vehicle, using low-cost cameras and free use tools for designing a prototype of processing information. The characterization is performed by identifying the normalized difference vegetation index (NDVI) in the photographic mosaics of the crops. This index provides information about plant health: Consequently, it is calculated and represented on a NDVI map, where the status of a crop is analyzed. The highest values of NDVI represent healthy plants, and the lowest do so for plants with problems, water, or others. The proposed  ethod allows the monitoring of crops in a temporary and spatial form, letting a producer to adopt measures that help the optimization of resources.La salud de las plantas tiene un impacto directo en la calidad y cantidad de los productos agrícolas. Debido   esto, los agricultores deben monitorear las condiciones de los cultivos con frecuencia, pero las herramientas actuales para llevar a cabo esta tarea son complejas e inaccesibles. Frente a esta situación, se propone en este artículo un método para la caracterización de cultivos que permita un monitoreo de las plantas a través fotografías en el espectro visible e infrarrojo adquiridas desde un vehículo aéreo multirrotor, mediante cámaras de bajo costo y herramientas de uso libre para el diseño de un prototipo de  rocesamiento de información. La caracterización se realizó mediante la identificación del índice de vegetación de diferencia normalizado (NDVI) en los mosaicos fotográficos de los cultivos. Este índice es capaz de proveer información acerca de la salud de las plantas, por lo cual se calcula y representa en un mapa NDVI en el que se analiza el estado del cultivo. Los valores más altos de NDVI representan a las plantas saludables, y los más bajos a las plantas con problemas, al agua u otros. El método propuesto permite monitorear cultivos de forma temporal y especial, con lo cual se llevaría al productor a tomar medidas que permitan la optimización de recursos. A saúde das plantas tem um impacto direto na qualidade e quantidade dos produtos agrícolas. Devido a isso, os agricultores devem monitorar as condições dos cultivos com frequência. Diante dessa situação, propõe-se neste artigo um método para a caracterização de cultivos que permita um monitoramento das plantas por meio de fotografias no espectro visível e infravermelho adquiridas a partir de um veículo aéreo multirotor, mediante câmeras de baixo custo e ferramentas de uso livre para o desenho de um protótipo de processamento de informação. A caracterização realizou-se mediante a identificação do índice de vegetação de diferença normalizada (NDVI) nos mosaicos fotográficos dos cultivos. Esse índice é capaz de prover informação a respeito da saúde das plantas, pelo qual se calcula e representa num mapa NDVI no qual se analisa o estado do cultivo. Os valores mais altos de NDVI representam as plantas saudáveis, e os mais baixos as plantas com problemas, a água ou outros. O método proposto permite monitorar cultivos de forma temporária e especial, com o qual se levaria o produtor a tomar medidas que permitam a otimização de recursos.&nbsp

Precision Agriculture using Internet of thing with Artificial intelligence: A Systematic Literature Review

Machine learning with its high precision algorithms, Precision agriculture (PA) is a new emerging concept nowadays. Many researchers have worked on the quality and quantity of PA by using sensors, networking, machine learning (ML) techniques, and big data. However, there has been no attempt to work on trends of artificial intelligence (AI) techniques, dataset and crop type on precision agriculture using internet of things (IoT). This research aims to systematically analyze the domains of AI techniques and datasets that have been used in IoT based prediction in the area of PA. A systematic literature review is performed on AI based techniques and datasets for crop management, weather, irrigation, plant, soil and pest prediction. We took the papers on precision agriculture published in the last six years (2013-2019). We considered 42 primary studies related to the research objectives. After critical analysis of the studies, we found that crop management; soil and temperature areas of PA have been commonly used with the help of IoT devices and AI techniques. Moreover, different artificial intelligence techniques like ANN, CNN, SVM, Decision Tree, RF, etc. have been utilized in different fields of Precision agriculture. Image processing with supervised and unsupervised learning practice for prediction and monitoring the PA are also used. In addition, most of the studies are forfaiting sensory dataset to measure different properties of soil, weather, irrigation and crop. To this end, at the end, we provide future directions for researchers and guidelines for practitioners based on the findings of this revie

Agrodroyd: sistema de monitoreo para cuidado y riego de productos agrícolas en cultivos urbanos

Práctica SocialPara este trabajo de grado, se presenta una práctica social con la implementación de un dispositivo de monitoreo para cuidado y riego de productos agrícolas en cultivos urbanos implementado en el colegio Ofelia Uribe de Acosta.INTRODUCCIÓN 1. ANTECEDENTES 2. PLANTEAMIENTO DEL PROBLEMA 3. OBJETIVOS 4. JUSTIFICACIÓN 5. MARCO DE REFERENCIA 6. METODOLOGIA 7. PROCEDIMIENTO 8. CONCLUSIONES Y TRABAJOS FUTUROS BIBLIOGRAFÍA ANEXOSPregradoIngeniero Electrónic

Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review

Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extractions to interpret results. Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The future of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance systems, and so on. This review briefly highlights the advantages of automatic detection of plant diseases to the growers

Terrain characterization for site selection and preparation

Terrain characterization is a key component in autonomous base camp site selection and preparation. Aerial terrain characterization allows for large areas of interest to be characterized in a safe and efficient manner. In this work three terrain characteristics, terrain elevation/slope, land cover/land use classes, and soil moisture content were determined using UAV-mounted sensors to inform base camp site selection and preparation decisions. To determine accurate and real-time elevation/slope values, a stale a priori digital elevation model (DEM) was merged with a high-resolution, updated LIDAR DEM using the mblend method. The mblend method achieved better results than the traditional cover method by ensuring fewer height discontinuities along the edge of the two DEMs. To perform land cover/land use mapping, three semantic segmentation models (PSPNet, U-Net, and Segnet) and three base models (VGG, ResNet, and MobileNet) were modified to include multispectral imagery and compared. Seven land cover classes were determined with an accuracy of 82.71% by model ResNet/SegNet. To determine soil moisture content (SMC), ten models were developed to predict soil moisture – two machine learning models, support vector machine (SVM) and extremely randomized trees (ET), were paired with 5 input variables. The results indicated that SMC could be predicted with greater accuracy by reducing the dimensionality of a hyperspectral dataset to resemble a standard multispectral dataset. The ET model produced better estimations of SMC when trained with the reduced dimensionality (RD) input set and concatenated multispectral (CM) set – obtaining an increase of 1.3% (RD) and 5.4% (CM) in R-squared values and a decrease of .13 and .22 in mean absolute error (MAE) when compared to the baseline set. Finally, a process overview and use case is presented to illustrate the terrain characterization process as a whole