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<p>A Velodyne Puck VLP-16 LiDAR and a Carnegie Robotics Multisense S21 stereo camera were placed in an environmental testing chamber to investigate dust and lighting effects on depth returns. The environmental testing chamber was designed and built with varied lighting conditions with corn dust plumes forming the atmosphere. Specific software employing ROS, Python, and OpenCV were written for point cloud streaming and publishing. Dust chamber results showed while dust effects were present in point clouds produced by both instruments, the stereo camera was able to “see” the far wall of the chamber and did not image the dust plume, unlike the LiDAR sensor. The stereo camera was also set up to measure the volume of total mixed ration (TMR) and shelled grain in various volume scenarios with mixed surface terrains. Calculations for finding actual pixel area based on depth were utilized along with a volume formula exploiting the depth capability of the stereo camera for the results. Resulting accuracy was good for a target of 8 liters of shelled corn with final values between 6.8 and 8.3 liters from three varied surface scenarios. Lessons learned from the chamber and volume measurements were applied to loading large grain vessels being filled from a 750-bushel grain cart in the form of calculating the volume of corn grain and tracking the location of the vessel in near real time. Segmentation, masking, and template matching were the primary software tools used within ROS, OpenCV, and Python. The S21 was the center hardware piece. Resulting video and images show some lag between depth and color images, dust blocking depth pixels, and template matching misses. However, results were sufficient to show proof of concept of tracking and volume estimation. </p>

Peanut (Arachis hypogaea L.) is an important food crop in the USA and worldwide with high net returns but yield in excess of 4500 kg ha-1 is needed to offset the production costs. Because yield is limited by biotic and abiotic stresses, cultivars with stress tolerance are needed to optimize yield. The U.S. peanut mini-core germplasm collection is a valuable resource that breeders can use to improve stress tolerance in peanut. Phenotyping for plant height, leaf area, and leaf wilting have been used as proxies for the desired tolerance traits. However, proximal data collection, i.e. measurements are taken on individual plants or in the proximity, is slow. Remote data collection and machine learning techniques for analysis offer a high-throughput phenotyping (HTP) alternative to manual measurements that could help breeding for stress tolerance. The objectives of this study were to 1) develop HTP methods using aerial remote sensing; 2) evaluate the mini-core collection in SE Virginia; and 3) perform a detailed physiological analysis on a sub-set of 28 accessions from the mini-core collection under drought stress, i.e. the sub-set was selected based on contrasting differences under drought in three states, Virginia, Texas, and Oklahoma. To address these objectives, replicated experiments were performed in the field at the Tidewater Agricultural Research and Extension Center in Suffolk, VA, in 2017, 2018, and 2019, under rainfed, irrigated, and controlled conditions using rainout shelters to induce drought. Proximal data collection involved physiological, morphological, and yield measurements. Remote data collection was performed aerially and included collection of red-green-blue (RGB) images and canopy reflectance in the visible, near infra-red, and infra-red spectra. This information was used to estimate plant characteristics related to growth and drought tolerance. Under objective 1), we developed HTP for plant height with 85-95% accuracy, LAI with 85-88% accuracy, and wilting with 91-99% accuracy; this was done with significant reduction of time as compared to proximal data collection. Under objectives 2) and 3), we determined that shorter genotypes were more drought tolerant than taller genotypes; and identified CC650 less wilted and with increased carbon assimilation, electron transport, quantum efficiency, and yield than other accessions. / Doctor of Philosophy / Peanut is a profitable food crop in the USA but has high input costs. Pod yield over 4500 kg ha-1 is required for a profitable production, which is challenging in dry and hot years, and under disease pressure. Varieties tolerant to dry weather conditions (drought) and disease presence are required to sustain production. A collection of 112 peanut varieties is available for researchers to study the mechanisms of tolerance to drought and disease, and identify tolerant varieties to these stresses. Plant characteristics including height, leaf area, and leaf wilting can be used as proxies to estimate stress tolerance and yield, and identify tolerant varieties. How to measure these characteristics is very important. We think that using images collected by a drone and automated analysis by specific computer programs is the easiest, fastest, and most accurate way. Therefore, the objectives of my study were to 1) use drones and cameras to collect images, and computer programs to derive plant characteristics from these images, 2) evaluate the peanut collection to identify varieties with tolerance to drought and disease, and 3) evaluate in depth a sub-set of 28 varieties from this collection under controlled drought conditions to further learn about peanut mechanisms of tolerance to drought and diseases. Field experiments were conducted in 2017, 2018, and 2019, at the Tidewater Agricultural Research and Extension Center in Suffolk, VA. For some tests, we used rainout shelters to mimic drought. We measured plant height, leaf area, color, and wilting, canopy temperature, photosynthesis, and pod yield. From a drone, we collected images in the visible and invisible radiation and, using specific computer programs, estimated plant characteristics with 95% accuracy for height, 88% for leaf area, and 91% for leaf wilting under drought. We concluded that taller varieties were more susceptible to drought than shorter varieties. Peanut varieties CC650 and CC068 had higher end of season yield. The study showed that drought reduced several key mechanisms of photosynthesis including electron transport; and reduced the end of season yield. Variety CC650 performed better under drought than other varieties of the collection.

Identifying the proper tillage system which provides the best agronomic benefits for cotton production in the coastal plain soils of Virginia was the basis for this research. Strip-tillage was evaluated from 2015-2016 on-farm to determine the effects of annual and biennial treatments on plant growth and lint yield, as well as measuring the impacts on soil compaction. Also, small plot tillage experiments were conducted from 2013-2016 assessing no-till, conventional tillage, minimum tillage, and strip-tillage as well as the subsequent effects of these systems on four cotton varieties. Biennial strip-tillage produced similar lint yields to annual strip-tillage at 3 of 4 locations, with only one location showing a significant difference in lint yield of 135 kg ha-1. Persistence of subsoil tillage within the row from the previous year was observed at some locations and plant heights were not different at all locations, although annual strip-tillage provided deeper potential rooting depths both early season and at harvest. In short term tillage systems, minimal penalties in plant growth and lint yield were observed in no-till verses the other systems, primarily associated with greater soil compaction, shorter plant heights, and lower yields. An overall 8% reduction in yield was found with no-till systems, with no significant differences in yield among tillage systems observed in any year. Varietal effects on plant growth and yield were observed annually, with FM 1944 GLB2 being the shortest plants, and DP 1321 B2RF having the tallest plants. No tillage by variety interaction was observed, supporting the idea that varieties respond similarly across tillage systems. / Master of Science

No / Technology application to agricultural productivity is thought to be the solution to meet food demand of the growing population. In a rapidly changing world, with the prospect of decreasing arable land due to urbanization and industrialization, agricultural output requires a 70 % increase in production levels and efficient growth in the harvesting, distribution and consumption of the resources, to meet demand. There are innovations in Information and Communications Technology that can be applied to the agricultural sector in areas of precision farming, use of farm management software, wireless sensors, and use of agricultural machinery. Remote sensing technology is playing a key role through precision agriculture. This paper highlights ways in which precision agriculture is impacting on agriculture with the use of unmanned aerial vehicles for image capturing, processing and analysis.

Chemical herbicides have been widely used in agriculture since the middle of the 20th century since they limit the competition of resources between crops and weeds. Herbicides are known to cause environmental degradation and to risk organisms and human health. Herbicides are chemical compounds that target weeds in different ways due to their chemical structures and concentrations. There is a growing concern with the usage of herbicides and the opposition to herbicides is growing due to the many risks the substances. There is an increased demand for sustainable options to chemical herbicides. Mechanical weeding has been used traditionally for weed management but has been forgotten due to the high efficiency of chemical herbicides. Precision agricultural technologies (PATs) can limit weeds mechanically. In this thesis PATs refer to robots equipped with AI, sensors, and other autonomous functionalities. This thesis studies how farmers perceive PATs and their ability to limit herbicides. The focus is on which factors need to be considered for farmers to adopt PATs for weed limitation, as well as what outcomes farmers expect from weeding tools. This research is a qualitative study conducted with semi-structured interviews with farmers. The most prominent themes were the business of agriculture, weed management, environmental perspectives, and perception of tools. These themes were used to find the farmers’ perceptions of PATs, and what outcomes a tool is expected to deliver. The theory of mediated activity (MAT) is used to analyse what the farmers want from their tools and how they perceive the PATs. Diffusion of innovation theory (DIT) is used to analyse the adaptability and willingness to adopt innovation, such as PATs. The key factors affecting adaptability are profit, efficiency, flexibility, and environmentally sustainable outcomes. These factors affect the farmers the most when adopting new tools and these are what the farmers are looking for in tools. To increase farmers' adoption of novel innovations they need to be presented with proof that the tools will bring the key factors. If PATs can offer the farmers the key factors the PATs can help to limit herbicide usage and therefore become a tool of sustainable transition.

Master of Agribusiness / Department of Agricultural Economics / Gregory Ibendahl / One of the latest buzzes amongst agriculture is the storage and analysis of “Big Data.” There are a number of questions surrounding the quality, quantity, and capacity of big data to form real-world decisions based upon past information. Much like the teachings of history, the storybook that big data can reveal about a grower’s operation may hold the answers to the question of: “what is necessary to increase food production which will be required to feed an ever-growing world?” With the increase in interest in precision agriculture, sustainability practices, and the processing of the immense spatial dataset generated on the farm, the next challenge at hand will be in determining how to make technology not only streamlined, but also profitable. Over the past few years, precision agriculture technology has become widely adopted as an agronomic decision making tool. Much like a scientific experiment, the greater the number of similar observations, the greater the degree of confidence can be placed upon a decision. As a means of increasing the number of observations that a farmer can use to base a decision upon, there is becoming increasing demand in being able to combine the data of similar farming operations in order to increase the size and scope of the dataset to generate better decisions benefitting many farms instead of just one. The growing interest in forming community data pools for farm data demonstrates the need for a study for determining how farming practices can be properly benchmarked. The goal was be to evaluate how to use farm data to make economic decisions in a similar manner as one would make agronomic decisions using similar observations. The objective was to design the proper protocol for benchmarking the farm’s potential, and evaluating potential increases in technical efficiency by adopting precision agriculture technology. To accomplish this, a data envelopment analysis was conducted using scale efficiency as a means of determining the frontier of efficient farms. The resounding goal for this study in the future will be to use the model as a means of implementing the secondary process of pooling precision agriculture data to analyze efficiencies gained by the adoption of technology. By demonstrating the value of generating peer groups to increase observations and refine farming practices, farmers can find increased profitability and efficiency by using resources that may already be held within the operation.

Taikant tiksliojo ūkininkavimo sistemą atliekami dirvos, augalų būklės, tręšimo poreikio ir derlingumo matavimai. Naudojantis gautais duomenimis, darbo metu, koreguojami žemės ūkio mašinų technologiniai parametrai. Tokiu būdu optimizuojamos trąšų, kalkių, pesticidų, degalų ir laiko sąnaudos auginant ne tik žemės ūkio augalus maistui bei pašarams, bet ir energetinius augalus – biomasei. Eksperimentiniai tyrimai atlikti mobiliu Veris MSP įrenginiu su Soil pH Manager sistema. Įvertinus sudarytų dirvožemio pH žemėlapių tikslumą, galima teigti, kad įrenginio Veris MSP racionalus darbinis greitis yra 9 km/h. Greitį padidinus, įrenginys matavimo reikšmes fiksuoja didesniais tarpais, todėl lieka neišskirtos mažą plotą užimančios skirtingo pH lauko zonos. Važiuojant įrenginiu 3 km/h greičiu, įrenginio našumas siekė 6,50,5 ha/h, o 12 km/h greičiu – 26,52,2 ha/h. Atliktų dirvožemio rūgštumo tyrimų patikimumą įrodo gautas, dirvožemio pH nustatyto įrenginiu Veris MSP bei nustatyto laboratorijoje tiesinis tarpusavio ryšys (R2 = 0,81). Kalkių normą parinkus, pagal įrenginio duomenis sudarytus kalkinimo žemėlapius, sutaupoma apie 40% kalkių, lyginant su kalkinimu pagal žemėlapius, sudarytus laboratorijoje nustatytų pH reikšmių pagrindu. Kalkinant pagal laboratorijos duomenų vidurkį (vienoda 500 kg/ha norma), atskiri lauko plotai būtų nepakankamai pakalkinti arba perkalkinti. / Applying the system of precision agriculture, soil, plant condition, fertilisation demand, and fertility measures are taken. Using the data obtained, technological parameters of agricultural machines are adjusted in a course of work. In such a way the consumption of fertilisers, lime, pesticides, fuel, and time are optimised growing not only agricultural plants for food and feed, but also raising the materials for biomass. Experimental studies were carried out using a mobile unit Veris MSP with Soil pH Manager System. After the assessment of accuracy of the pH maps formed, it can be stated that the reasonable working speed of the unit Veris MSP is 9 km/h. Increasing the speed, the unit records the measurement values in greater spaces, thus leaving small areas of field with different pH values unmarked. Moving the unit at the speed of 3 km/h, the unit reached work efficiency of 6.5±0.5 ha/h and at 12 km/h – 26.5±2.2 ha/h. The reliability of the soil acidity research conducted is proved by the linear correlation of soil pH identified using the Veris MSP and the soil pH identified in the laboratory (R2 = 0.81). Selecting the lime rates according to the maps created using the data from the unit lime savings are about 40%, compared to the liming based on the maps created on pH values determined in the laboratory. When liming is done according to the average of laboratory data (equal to 500 kg/ha rate), some field areas are under-limed or over-limed.

O cultivo dos citros é de grande importância no mundo, e no Brasil é uma das culturas mais representativas das exportações do país. A agricultura de precisão (AP) é descrita como um conjunto de ferramentas, que possibilitam gerenciar o sistema de produção a partir de informações georreferenciadas e manejar variabilidades espaciais das lavouras. A AP é utilizada em larga escala em culturas anuais, mostrando resultados quanto ao melhor gerenciamento das informações e racionalização dos insumos. Considerando a importância dos citros para a economia do estado de São Paulo e desejando-se aprimorar o seu processo produtivo, objetivouse com este trabalho identificar as variabilidades espaciais dos atributos e elementos químicos no solo e nas folhas, e produtividade de pomares comerciais de laranja, comparando-se o manejo tradicionalmente utilizado pela fazenda com os efeitos das adubações em taxa variável. Para isso, foram utilizadas duas áreas localizadas na Fazenda Quatrirmãs, no município de Botucatu-SP, de 25,7 hectares cada uma, diferenciadas pelo tipo de solo, altitude e ano de plantio, que foram acompanhadas por duas safras desde o ano de 2008. Foram estabelecidos dois tratamentos: aplicações em dose fixa (DF) e dose variável (DV). Na primeira safra, foram retiradas amostras de solo em três diferentes padrões e densidades, para contrastar os níveis de fertilidade na faixa de adubação na profundidade de 0-0,2 m (2 amostras ha-1) e 0,2-0,4 m (1 amostra ha-1), e no centro da rua de 0-0,2 m de profundidade (1 amostra ha-1). A partir das análises das amostras retiradas na faixa de adubação na profundidade de 0-0,2 m, foram calculadas as recomendações de aplicação de calcário e de fontes de nitrogênio, fósforo e potássio para o tratamento DV, que foram aplicadas por uma máquina protótipo capaz de variar as doses automaticamente, baseadas no mapa de prescrição. Na safra 2009/2010 foram retiradas amostras de solo e folhas georreferenciadas, separando-se os tratamentos. As colheitas foram registradas em ambas as safras e forneceram dados para o mapeamento da produtividade das áreas. Os resultados das amostragens georreferenciadas de solo mostraram variabilidade espacial dos atributos. As médias dos valores de P foram classificadas como médio, nas áreas em ambos os tratamentos. Os valores médios de K variaram entre alto e médio, para DF e DV, respectivamente, na área de solo argiloso e médio para os dois tratamentos da área 2 (solo arenoso). Os níveis médios de V% foram classificados como médio na área 1 e baixo na área 2, sem diferença entre tratamentos. As adubações em taxa variável resultaram em economia de 230 kg ha-1 de nitrogênio, 5 kg ha-1 de fósforo e 257 kg ha-1 de potássio, em média, nos dois talhões, quando somadas as safras estudadas. As produtividades foram elevadas em mais que o dobro nas áreas, aumentando de 19,3 para 39,3 t ha-1 (DF) e 41,8 t ha-1 (DV) na área 1, e de 15,3 para 33,4 t ha-1 (DF) e 33,3 t ha-1 (DV) na média dos valores da área 2. Os teores foliares de N, P e K não diferiram entre os tratamentos e suas médias foram identificadas como altas nas duas áreas, indicando o potencial do uso desta ferramenta nas áreas estudadas. / The cultivation of citrus has great importance in the world and in Brazil is one of the most representative crops for exports. Precision agriculture (PA) is described as a set of tools that allow managing the production system based on georeferenced information related to the spatial variability of crops. The PA is used in large-scale on annual crops showing results concerning improved information management and the rationalization of inputs use. Considering the importance of citrus to the economy of the Sao Paulo state and wishing to improve production practices the aim of this work was to identify the spatial variability of the attributes and elements of soil, leaves and yield of commercial orange orchards and compare management traditionally used by the farm with the effects of variable rate fertilization. The work was conducted in two areas located in Botucatu-SP, 25.7 hectares each, differentiated by soil type, elevation and year of planting. The areas were monitored and interfered for two seasons since the year 2008. Two treatments were established: applications of a constant rate (CR) and spatially variable rate (VR). In the first year, soil samples were collected in three different patterns and densities at a depth of 0-0.2 m (2 samples ha-1) and 0.2-0.4 m (1 sample ha-1) in the fertilization strip and in the between trees rows at 0-0.2 m depth (1 sample ha-1). From the analysis of samples taken in the range of fertilization at a depth of 0-0.2 m recommendations were calculated for liming and sources of nitrogen, phosphorus and potassium for VR treating that were applied by a prototype machine capable of varying rates automatically based on the prescription map. In the 2009/2010 year, the georeferenced samples were collected from soil and leaves georeferenced separating the treatments. The samples were recorded in both seasons and provided data for yield mapping of the areas. The results of georeferenced soil samples showed spatial variability of attributes. The average values of P were classified as \"medium\", for the areas in both treatments. The average values of K ranged from \"high\" and \"medium\" for CR and VR, respectively, in clay soil area and \"medium\" for the treatments of area 2. Mean levels of V% were classified as \"medium\" in the area and a \"low\" in area 2, with no difference between treatments. The variable rate fertilization resulted in savings of 230 kg ha-1 nitrogen, 5 kg ha-1 of phosphorus and 257 kg ha-1 potassium on average in the two plots. The yields increased from 19.3 to 39.3 t ha-1 (CR) and 41.8 t ha-1 (VR) in area 1 and from 15.3 to 33.4 t ha-1 (CR) and 33.3 t ha-1 (VR) of area 2. Leaf N, P and K did not differ between treatments and their averages were identified as high in both areas supporting the potential use of this tool.