Topsoil Moisture Estimation for Precision Agriculture Using Unmanned Aerial Vehicle Multispectral Imagery

There is an increasing trend in crop production management decisions in precision agriculture based on observation of high resolution aerial images from unmanned aerial vehicles (UAV). Nevertheless, there are still limitations in terms of relating the spectral imagery information to the agricultural targets. AggieAir™ is a small, autonomous unmanned aircraft which carries multispectral cameras to capture aerial imagery during pre-programmed flights. AggieAir enables users to gather imagery at greater spatial and temporal resolution than most manned aircraft and satellite sources. The platform has been successfully used in support of a wide variety of water and natural resources management areas. This paper presents results of an on-going research in the application of the imagery from AggieAir in the remote sensing of top soil moisture estimations for a large field served by a center pivot sprinkler irrigation system

Drone Technology in Precision Agriculture: Are There No Environmental Concerns?

The adoption of drones in precision agriculture is expanding at a rapid rate, and expected to rise even faster as improvements in the technology result in cheaper models. Studies on the economic impact of drone technology in precision agriculture present optimistic projections of increased global food production. But increased food production almost always comes with significant environmental concerns. This paper examines the environmental concerns of drone technology in precision agriculture. The methodology of this paper is theoretical analysis and extrapolation of current literature in order to reveal the gap which future research needs to fill. While proposing a new area that has not received the close attention of experts and researchers, the paper reveals future scenarios of environmental issues around the various methods of drone applications in agricultural practices. Keywords: Drone technology, precision agriculture, agricultural practices, environmental impact, food security words DOI: 10.7176/JEES/10-9-08 Publication date:September 30th 202

Gjennomgang av presisjonslandbruk med bruk av droner for jordfuktighet estimering – Mot et mer bærekraftig landbruk

This master thesis is reviewing the latest published research on remote sensing technology in the agricultural sector, for soil moisture estimations towards a more sustainable precision agriculture. Modern, exciting new technological innovations will also be presented, along with the sustainable aspect of conventional agriculture with more precise agricultural practices. The synergy between UAS, SMC and sustainability are the focus of attention for this review thesis, as the possibilities and opportunities this can open for us can be of significant advancement in profitability and precision agriculture. As precision agriculture evolves and grows, the potential and opportunities also follow. The new field of unmanned aerial systems demonstrates this. There are several sectors the unmanned aerial vehicle is being welcomed with open arms, only within the agricultural sector, it has shown to be of great value for crop yield and biomass estimation. It takes little energy to run and operate and it can be from a green power source. As we all should move towards a more sustainable and eco-friendly lifestyle, industries, businesses and corporations are no exceptions. Agriculture is a major contributor to the climate change and environmental destruction, we should make a change to a more sustainable method of farming, with precision agriculture we are making this shift. The objective of this thesis is to contribute to the fundamental research for future implementation and introduction of remote sensing technology with a UAV. This thesis highlights these areas, to assist in closing the gap between researchers and endusers. By increasing the precision and applying inputs like artificial fertiliser and pesticides/herbicides at a correctly variable amount and time, a reduction of the inputs and the environmental disruption should follow, which results in an increase in the profitability for the farmers, and less environmental damages.Denne masteroppgaven gjennomgår den siste publiserte forskning av fjernmålings teknologi i landbrukssektoren, av jordfuktighets beregninger for ett mer bærekraftig presisjons jordbruk. Moderne spennende nye teknologiske utviklinger vil også bli presentert, sammen med det bærekraftig aspekt av konvensjonelt landbruk med mer nøyaktig jordbrukspraksis. Samarbeidet mellom UAS, SMC og bærekraft er i fokus i denne avhandlingen, som diskuterer mulighetene dette kan åpne for. Ved at presisjons jordbruk utvikler seg og vokser, følge også nye muligheter og metoder for utførelse av arbeidsoppgaver. Det nye fagfeltet av ubemannede luft systemer (UAS) demonstrerer dette. Det er flere sektorer som ønsker UAS velkommen, bare innenfor landbrukssektoren har det vist seg å være av stor verdi for vanningsanlegg planlegging og inspisering, avling og biomasse estimering. Det tar lite energi å operere og betjene systemet, energikilden kan være fornybar. Vi skal alle bevege oss mot ett mer bærekraftig og miljøvennlig livsstil, bransjer, bedrifter og selskaper er ingen unntak. Landbruket er en stor bidragsyter til klimaendringer og miljøskader, vi bør ta et skifte til en mer bærekraftig utvikling for landbruket, presisjon landbruk kan bidra med dette. Målet med denne avhandlingen er å bidra til grunnleggende forskning for fremtidig implementering og innføring av fjernmåling teknologi med en UAV. Denne oppgaven belyser disse områdene, og bidra i å lukke gapet mellom forskere og forbrukere. Ved å forbedre presisjonen på midler som kunstgjødsel eller sprøytemidler, på riktig tidspunkt med riktig mengde, vil resultere i en redusert menge utførelse av midler som vil igjen gi bonden større profittmargin, og mindre konsekvenser på miljøet

Assessment of Surface Soil Moisture Using High-Resolution Multi-Spectral Imagery and Artificial Neural Networks

Many crop production management decisions can be informed using data from high-resolution aerial images that provide information about crop health as influenced by soil fertility and moisture. Surface soil moisture is a key component of soil water balance, which addresses water and energy exchanges at the surface/atmosphere interface; however, high-resolution remotely sensed data is rarely used to acquire soil moisture values. In this study, an artificial neural network (ANN) model was developed to quantify the effectiveness of using spectral images to estimate surface soil moisture. The model produces acceptable estimations of surface soil moisture (root mean square error (RMSE) = 2.0, mean absolute error (MAE) = 1.8, coefficient of correlation (r) = 0.88, coefficient of performance (e) = 0.75 and coefficient of determination (R2) = 0.77) by combining field measurements with inexpensive and readily available remotely sensed inputs. The spatial data (visual spectrum, near infrared, infrared/thermal) are produced by the AggieAir™ platform, which includes an unmanned aerial vehicle (UAV) that enables users to gather aerial imagery at a low price and high spatial and temporal resolutions. This study reports the development of an ANN model that translates AggieAir™ imagery into estimates of surface soil moisture for a large field irrigated by a center pivot sprinkler system

Spatial Scale Gap Filling Using an Unmanned Aerial System: A Statistical Downscaling Method for Applications in Precision Agriculture

Applications of satellite-borne observations in precision agriculture (PA) are often limited due to the coarse spatial resolution of satellite imagery. This paper uses high-resolution airborne observations to increase the spatial resolution of satellite data for related applications in PA. A new variational downscaling scheme is presented that uses coincident aerial imagery products from “AggieAir”, an unmanned aerial system, to increase the spatial resolution of Landsat satellite data. This approach is primarily tested for downscaling individual band Landsat images that can be used to derive normalized difference vegetation index (NDVI) and surface soil moisture (SSM). Quantitative and qualitative results demonstrate promising capabilities of the downscaling approach enabling effective increase of the spatial resolution of Landsat imageries by orders of 2 to 4. Specifically, the downscaling scheme retrieved the missing high-resolution feature of the imageries and reduced the root mean squared error by 15, 11, and 10 percent in visual, near infrared, and thermal infrared bands, respectively. This metric is reduced by 9% in the derived NDVI and remains negligibly for the soil moisture products

Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines

Spatial surface soil moisture can be an important indicator of crop conditions on farmland, but its continuous estimation remains challenging due to coarse spatial and temporal resolution of existing remotely-sensed products. Furthermore, while preceding research on soil moisture using remote sensing (surface energy balance, weather parameters, and vegetation indices) has demonstrated a relationship between these factors and soil moisture, practical continuous spatial quantification of the latter is still unavailable for use in water and agricultural management. In this study, a methodology is presented to estimate volumetric surface soil moisture by statistical selection from potential predictors that include vegetation indices and energy balance products derived from satellite (Landsat) imagery and weather data as identified in scientific literature. This methodology employs a statistical learning machine called a Relevance Vector Machine (RVM) to identify and relate the potential predictors to soil moisture by means of stratified cross-validation and forward variable selection. Surface soil moisture measurements from irrigated agricultural fields in Central Utah in the 2012 irrigation season were used, along with weather data, Landsat vegetation indices, and energy balance products. The methodology, data collection, processing, and estimation accuracy are presented and discussed. © 2016 by the authors

New strategies for row-crop management based on cost-effective remote sensors

Agricultural technology can be an excellent antidote to resource scarcity. Its growth has led to the extensive study of spatial and temporal in-field variability. The challenge of accurate management has been addressed in recent years through the use of accurate high-cost measurement instruments by researchers. However, low rates of technological adoption by farmers motivate the development of alternative technologies based on affordable sensors, in order to improve the sustainability of agricultural biosystems. This doctoral thesis has as main objective the development and evaluation of systems based on affordable sensors, in order to address two of the main aspects affecting the producers: the need of an accurate plant water status characterization to perform a proper irrigation management and the precise weed control. To address the first objective, two data acquisition methodologies based on aerial platforms have been developed, seeking to compare the use of infrared thermometry and thermal imaging to determine the water status of two most relevant row-crops in the region, sugar beet and super high-density olive orchards. From the data obtained, the use of an airborne low-cost infrared sensor to determine the canopy temperature has been validated. Also the reliability of sugar beet canopy temperature as an indicator its of water status has been confirmed. The empirical development of the Crop Water Stress Index (CWSI) has also been carried out from aerial thermal imaging combined with infrared temperature sensors and ground measurements of factors such as water potential or stomatal conductance, validating its usefulness as an indicator of water status in super high-density olive orchards. To contribute to the development of precise weed control systems, a system for detecting tomato plants and measuring the space between them has been developed, aiming to perform intra-row treatments in a localized and precise way. To this end, low cost optical sensors have been used and compared with a commercial LiDAR laser scanner. Correct detection results close to 95% show that the implementation of these sensors can lead to promising advances in the automation of weed control. The micro-level field data collected from the evaluated affordable sensors can help farmers to target operations precisely before plant stress sets in or weeds infestation occurs, paving the path to increase the adoption of Precision Agriculture techniques

Application of UASs to augment ground surveys in cranberry agriculture development: a proof of concept for the integration of UAS into the site identification and monitoring of cranberry farms in Newfoundland

Assessing the potential for developing wetland environments into cranberry agricultural lands is time consuming and expensive. The addition of unmanned aerial systems (UAS) to augment current ground survey techniques has the potential to increase assessment accuracy and cranberry production while reducing costs. Newfoundland’s extensive wetlands offer significant opportunities for the development of cranberry agricultural lands. Due to a large international demand for raw cranberries, there is great potential economic benefit in the rapid development of cranberry farms. This study focused on using UASs to assess wetland areas in Newfoundland by applying suitability criteria developed by the Newfoundland Government. This was done through the use of GIS, image classification, and photogrammetry to assess these criteria over three site locations. The viability of expanding UAS data collection over larger areas to develop a province-wide model was explored through an assessment of current fixed wing UAS technology. Given the novelty of this area of study, this research aimed to serve as a proof of concept where the validity of results was measured against real world applicability, not statistical analysis. The results showed that because UASs cannot assess all of the required wetland criteria, they are not a viable replacement for current ground surveys, but do have the potential to augment current techniques. UASs make it possible to survey larger areas, as well as reduce time and cost. The assessment of current fixed wing UAS technology concluded that given the continuously improving technology and further testing, there is the potential for these systems to collect comparable data over a larger area. Overall, the study concluded that through the strategic integration of the UAS techniques developed in this study with existing ground survey methods, Newfoundland has the potential to increase cranberry agricultural development and capitalize on the global demand for this crop

Paradigmas de aprendizaje automático aplicados a la teledetección: imágenes RGB e imágenes multiespectrales.

213 p.La tendencia actual en el uso de sensores para recopilar datos georreferenciados con una alta redundancia, se basa en la aplicación de métodos robustos y automatizados para extraer información geoespacial. Los resultados derivan en un cambio de paradigmas en tecnologías geoespaciales, que hasta este momento no han generado un límite en su aplicación. Sumado a ello, los avances en tecnologías sobre ordenadores, aprendizaje máquina, detección de patrones y visión computacional muestran una clara tendencia a la generación de estudios avanzados sobre imágenes, lo cual impulsa a la investigación de la información geoespacial con un progreso exponencial.El presente trabajo realiza un recorrido sobre paradigmas de aprendizaje automático aplicados en imágenes aéreas (RGB) y satelitales (multiespectrales), metodologías que han sido aplicadas en campo con interesantes resultados

Paradigmas de aprendizaje automático aplicados a la teledetección: imágenes RGB e imágenes multiespectrales.

213 p.La tendencia actual en el uso de sensores para recopilar datos georreferenciados con una alta redundancia, se basa en la aplicación de métodos robustos y automatizados para extraer información geoespacial. Los resultados derivan en un cambio de paradigmas en tecnologías geoespaciales, que hasta este momento no han generado un límite en su aplicación. Sumado a ello, los avances en tecnologías sobre ordenadores, aprendizaje máquina, detección de patrones y visión computacional muestran una clara tendencia a la generación de estudios avanzados sobre imágenes, lo cual impulsa a la investigación de la información geoespacial con un progreso exponencial.El presente trabajo realiza un recorrido sobre paradigmas de aprendizaje automático aplicados en imágenes aéreas (RGB) y satelitales (multiespectrales), metodologías que han sido aplicadas en campo con interesantes resultados