As the cost of unmanned aerial systems (UAS) and their sensing payloads decrease the practical applications for such systems have begun expanding rapidly. Couple the decreased cost of UAS with the need for increased crop yields under minimal applications of agrochemicals, and the immense potential for UAS in commercial agriculture becomes immediately apparent. What the agriculture community needs is a cost effective method for the field-wide monitoring of crops in order to determine the precise application of fertilizers and pesticides to reduce their use and prevent environmental pollution. To that end, this thesis presents an unmanned aerial system aimed at monitoring a crop's status. The system presented uses a Yamaha RMAX unmanned helicopter, operated by Virginia Tech']s Unmanned Systems Lab (USL), as the base platform. Integrated with helicopter is a dual-band multispectral camera that simultaneously captures images in the visible and near-infrared (NIR) spectrums. The UAS is flown over a quarter acre corn crop undergoing a fertilizer rate study of two hybrids. Images gathered by the camera are post-processed to form a Normalized Difference Vegetative Index (NDVI) image. The NDVI images are used to detect the most nutrient deficient corn of the study with a 5% margin of error. Average NDVI calculated from the images correlates well to measured grain yield and accurately identifies when one hybrid reaches its yield plateau. A secondary test flight over a late-season tobacco field illustrates the system's capabilities to identify blocks of highly stressed crops. Finally, a method for segmenting bleached tobacco leaves from green leaves is presented, and the segmentation results are able to provide a reasonable estimation of the bleached tobacco content per image. / Master of Science

During their growth, crops may experience a variety of health issues, which often lead to a reduction in crop yield. In order to avoid financial loss and sustain crop survival, it is imperative for farmers to detect and treat crop health issues. Interest in the use of unmanned aerial vehicles (UAVs) for precision agriculture has continued to grow as the cost of these platforms and sensing payloads has decreased. The increase in availability of this technology may enable farmers to scout their fields and react to issues more quickly and inexpensively than current satellite and other airborne methods. In the work of this thesis, methods have been developed for applications of UAV remote sensing using visible spectrum and multispectral imagery. An algorithm has been developed to work on a server for the remote processing of images acquired of a crop field with a UAV. This algorithm first enhances the images to adjust the contrast and then classifies areas of the image based upon the vigor and greenness of the crop. The classification is performed using a support vector machine with a Gaussian kernel, which achieved a classification accuracy of 86.4%. Additionally, an analysis of multispectral imagery was performed to determine indices which correlate with the health of corn crops. Through this process, a method for correcting hyperspectral images for lighting issues was developed. The Normalized Difference Vegetation Index values did not show a significant correlation with the health, but several indices were created from the hyperspectral data. Optimal correlation was achieved by using the reflectance values for 740 nm and 760 nm wavelengths, which produced a correlation coefficient of 0.84 with the yield of corn. In addition to this, two algorithms were created to detect stink bugs on crops with aerial visible spectrum images. The first method used a superpixel segmentation approach and achieved a recognition rate of 93.9%, although the processing time was high. The second method used an approach based upon texture and color and achieved a recognition rate of 95.2% while improving upon the processing speed of the first method. While both methods achieved similar accuracy, the superpixel approach allows for detection from higher altitudes, but this comes at the cost of extra processing time. / Master of Science

The opening of large archives of satellite data such as LANDSAT, MODIS and the SENTINELs has given researchers unprecedented access to data, allowing them to better quantify and understand local and global land change. The need to analyze such large data sets has led to the development of automated and semi-automated methods for satellite image time series analysis. However, few of the proposed methods for remote sensing time series analysis are available as open source software. In this paper we present the R package dtwSat. This package provides an implementation of the time-weighted dynamic time warping method for land cover mapping using sequence of multi-band satellite images. Methods based on dynamic time warping are flexible to handle irregular sampling and out-of-phase time series, and they have achieved significant results in time series analysis. Package dtwSat is available from the Comprehensive R Archive Network (CRAN) and contributes to making methods for satellite time series analysis available to a larger audience. The package supports the full cycle of land cover classification using image time series, ranging from selecting temporal patterns to visualizing and assessing the results.

An autonomous machine vision guided plant sensing and monitoring system was designed and constructed to continuously monitor plant related features: color (red-green-blue, hue-saturation-luminance, and color brightness), morphology (top projected canopy area), textural (entropy, energy, contrast, and homogeneity), Normalized Difference Vegetative Index (NDVI) (as well as other similar indices from the color and NIR channels), and thermal (plant and canopy temperature). Several experiments with repeated water stress cycles, using the machine vision system, was conducted to evaluate the machine vision system's performance to determine the timeliness of induced plant water stress detection. The study aimed at identifying significant features separating the control and treatment from an induced water stress experiment and also identifying, amongst the plant canopy, the location of the emerging water stress with the found significant features. Plant cell severity had been ranked based on the cell's accumulated feature count and converted to a color coded graphical canopy image for the remote operator to evaluate. The overall feature analysis showed that the morphological feature, Top Projected Canopy Area, was found to be a good marker for the initial growth period while the vegetation indices (ENDVI, NDVIBlue, and NDVIRed) were more capable at capturing the repeated stress occurrences during the various stages of the lettuce crop. Furthermore, the crop's canopy temperature was shown to be a significant and dominant marker to timely detect the water stress occurrences. The graphical display for the remote user showed the severity of summed features to equal the detection of the human vision. Capabilities and limitations of the developed system and stress detection methodology were documented with recommendations for future improvements for the crop monitoring/production system. An example web based decision support platform was created for data collection, storage, analysis, and display of the data/imagery collected for a remote operator.

Remote sensing has enabled unprecedented earth observation from space and has proven to be an invaluable tool for agricultural applications and crop management practices. Here we detect seasonal metrics indicating the start of the season (SOS), the end of the season (EOS) and maximum greenness (MAX) based on vegetation spectral signatures and the normalized difference vegetation index (NDVI) for a time series of Landsat-8, Sentinel-2 and PlanetScope imagery of potato, wheat, watermelon, olive and peach/apricot fields. Seasonal metrics were extracted from NDVI curves and the effect of different spatial and temporal resolutions was assessed. It was found that Landsat-8 overestimated SOS and EOS and underestimated MAX due to its low temporal resolution, while Sentinel-2 offered the most reliable results overall and was used to classify the fields in Aljawf. Planet data reported the most precise SOS and EOS, but proved challenging for the framework because it is not a radiometrically normalized product, contained clouds in its imagery, and was difficult to process because of its large volume. The results demonstrate that a balance between the spatial and temporal resolution of a satellite is important for crop monitoring and classification and that ultimately, monitoring vegetation dynamics via remote sensing enables efficient and data-driven management of agricultural system

This research focuses on the development of a deterministic-probabilistic framework for agricultural land use and management, specifically for both annual crops, such as wheat, barley and maize, and permanent crops, such as vineyards. The goal is to predict crop greening and peak crop development progressively through the growing season, based on accumulating information as the crop develops and matures, and to provide an accompanying uncertainty statement (credible interval) with each prediction. The integrated area underneath the phenology curve can be associated, although not explicitly in our example, with per-area crop yield. The prediction model relies on remotely sensed data, including science data products from the Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) spaceborne instruments, field data from agro-meteorological stations, and statistical data from prior years. The development of the deterministic-probabilistic model focuses on northeastern Italy, a region of small agricultural plots set in a diverse physical landscape, which is typical of many areas of old-world and developing-nation agriculture. The estimation process uses the phenological cycle of the MODIS Enhanced Vegetation Index (EVI), extracted from the satellite imagery at 500 m spatial resolution. Landsat data, at 30-m spatial resolution, are fused with MODIS data, to provide fine-scale information better suited to small-field agriculture. By applying a piecewise logistic function to model the time trajectory of EVI values, crop development and peak greenness are estimated and characterized based on the main phenological stages determined from the remote imagery trained with ground station observations. The deterministic-probabilistic model is later validated with observations from reference testing stations and statistical crop and yield data obtained independently by administrative districts such as regional and national organizations. A temporal filter of the main phenological stages, here called a crop calendar, plays a critical role. A Bayesian approach to integrate stochastically the parameters related to a certain area provides a way to include the different datasets at the different dimensions and scales and to assess the probability to obtain a vegetation index within a given uncertainty. The model becomes, therefore, a typical generalized linear model problem, deterministically described by a piecewise logistic function, with the parameters describing the peak phenological curve estimated probabilistically, with their own uncertainty. / 2026-01-31T00:00:00Z

Conselho Nacional de Desenvolvimento Científico e Tecnológico / The objective of this study was to define a methodology for monitoring a flooded rice crop forecast for Rio Grande do Sul, and to evaluate the effect of the flood time on growth, development and rice productivity. Two experiments were conducted during the 2013/14 growing season, using a randomized blocks design with four replications. The treatments in Experiment 1 were flooding in V3, V5, V8 and V9, and in Experiment 2 the treatments were flooding in V5, V8, V9 and V10. The onset of flooding did not influence the emission of leaves, the final leaf number, the final number of tillers and crop development. Leaf growth rate is affected by the onset of flooding when rainfall was less than the crop evapotranspiration. It is not clear if kernel yield is or not affected by the time that flooding starts. To define a methodology for monitoring a flooded rice crop forecast for Rio Grande do Sul, the SimulArroz rice model were coupled to regional climate model RegCM4 for generation the daily seasonal forecast. Nine members of RegCM4 model were used, with different parameterization (01, 07, 10, 13, 19, 22, 31, 34 and 37) and four boots (01, 02, 3:04) per month, with daily data of minimum temperature, maximum temperature and solar radiation. Three points with 45 km resolution grid were used for generating data of the minimum temperature (°C) maximum temperature (°C) and solar radiation (MJ m-2 day-1), covering the municipalities of Restinga Seca, Itaqui and Uruguaiana. The predictions were compared with SimulArroz crop monitoring with INMET automatic weather stations data and data collected in three cropping areas in Restinga Seca and 2 in Itaqui. The compared variables were leaf emission (Haun Stage - HS), final leaf number, development stage (COUNCE et al., 2000) and productivity (Mg ha-1). The best predicting irrigated rice crop forecast in Rio Grande do Sul were: member 31 minimum temperature, member 34 maximum temperature and a member 01 solar radiation (M31M34M01); minimum and maximum temperature and solar radiation boot 01 member 19 (M19S01) and; minimum and maximum temperature and solar radiation boot 03 member 01 (M01S03). The seasonal forecast generated by RegCM4 model coupled to SimulArroz rice model made possible the numerical prediction of rice crop in Rio Grande do Sul. / O objetivo deste trabalho foi definir uma metodologia para acompanhamento e previsão de safra de arroz irrigado para o Rio Grande do Sul, e avaliar o efeito da época de inundação sobre variáveis de crescimento, desenvolvimento e produtividade de arroz irrigado. Foram conduzidos dois experimentos durante o ano agrícola 2013/14, em delineamento experimental de blocos ao acaso, com quatro repetições. Os tratamentos no Experimento 1 foram: inundação em V3, V5, V8 e V9, e no Experimento 2 os tratamentos foram: inundação em V5, V8, V9 e V10. A época de inundação não influenciou a emissão de folhas, o número final de folhas, o número final de perfilhos e o desenvolvimento da cultura. A taxa de crescimento foliar quando a precipitação foi menor que a evapotranspiração da cultura do arroz. Não é clara se a produtividade de grãos é ou não afetada pela época de inundação do solo. Para definir uma metodologia para acompanhamento e previsão de safra de arroz irrigado para o Rio Grande do Sul, foi utilizado como modelo de arroz o SimulArroz, acoplado ao modelo climático regional RegCM4 para geração dos dados meteorológicos diários da previsão sazonal. Foram utilizados nove membros do modelo RegCM4, com diferentes parametrizações (01, 07, 10, 13, 19, 22, 31, 34 e 37), e quatro inicializações (01, 02, 03 e 04) por mês, com dados diários de temperatura mínima, temperatura máxima e radiação solar.Três pontos de resolução de 45 km de grade foram utilizados para geração dos dados de temperatura mínima (°C), temperatura máxima (°C) e radiação solar (MJ m-2 dia-1), abrangendo os municípios de Restinga Seca, Itaqui e Uruguaiana. As previsões foram comparadas com o acompanhamento de safra do SimulArroz rodado com dados das estações meteorológicas automáticas do INMET, e com dados observados em 3 lavouras em Restinga Seca e 2 em Itaqui. As variáveis comparadas foram emissão de folhas (Haun Stage - HS), número final de folhas, estádio de desenvolvimento (COUNCE et al., 2000) e produtividade (Mg ha-1). As melhores previsões para realizar previsão de safra de arroz irrigado no Rio Grande do Sul foram: temperatura mínima do membro 31, temperatura máxima do membro 34 e radiação solar do membro 01 (M31M34M01); temperatura mínima, máxima e radiação solar da inicialização 01 do membro 19 (M19S01) e; temperatura mínima, máxima e radiação solar da inicialização 03 do membro 01 (M01S03). A previsão sazonal gerada pelo modelo RegCM4 acoplado ao modelo de arroz SimulArroz possibilitou a previsão numérica de safra de arroz para o Rio Grande do Sul.