Browsing by Subject "Precision agriculture"
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Item Ground-based Technologies for Cotton Root Rot Control(2013-04-24) Cribben, Curtis DThe overall goal of this research is to develop ground-based technologies for disease detection and mapping which can maximize the effectiveness and efficiency of cotton root rot (CRR) treatments. Accurately mapping CRR could facilitate a much more economical solution than treating entire fields. Three cotton fields around CRR-prone areas of Texas have been the sites for three years of data collection. A complete soil apparent electrical conductivity (ECa) survey was conducted for each field with an EM38DD sensor. Multiple linear regression was used to relate physical and chemical soil properties to the ECa values obtained from the EM38DD. The variability in soil ECa measurements can be best accounted for using calcium carbonate levels as well as clay and sand contents in the soil. T-tests were used to determine that soil pH, clay, sand, and inorganic carbon content were significantly related to CRR incidence as determined by aerial images of each location. Spectral data were obtained for freshly picked cotton leaves from healthy, disease-stressed, and dying or dead plants using an ASD VisNIR spectroradiometer. The leaf spectra were evaluated using linear discriminant analysis (LDA), the receiver operator characteristic, and wavelet analysis to relate them to classifications of infection level. It was determined that healthy and infected leaves can be correctly classified 85% of the time based on the spectral data. The results from this study suggest that differences in soil characteristics may not be pronounced enough to accurately map CRR in the soil; however, the precision treatment of CRR may possible using an optoelectronic sensor to diagnose infected plants based on leaf reflectance.Item Mapping in-field cotton fiber quality and relating it to soil moisture(2009-05-15) Ge, YufengThe overarching goal of this dissertation project was to address several fundamental aspects of applying site-specific crop management for fiber quality in cotton production. A two-year (2005 and 2006) field study was conducted at the IMPACT Center, a portion of the Texas A&M Research farm near College Station, Texas, to explore the spatial variability of cotton fiber quality and quantify its relationship with in-season soil moisture content. Cotton samples and in-situ soil moisture measurements were taken from the sampling locations in both irrigated and dry areas. It was found that generally low variability (CV < 10%) existed for all of the HVI (High Volume Instrument) fiber parameters under investigation. However, an appreciable level of spatial dependence among fiber parameters was discovered. Contour maps for individual fiber parameters in 2006 exhibited a similar spatial pattern to the soil electrical conductivity map. Significant correlations (highest r = 0.85) were found between most fiber parameters (except for micronaire) and in-season soil moisture in the irrigated areas in 2005 and in the dry area in 2006. In both situations, soil moisture late in the season showed higher correlation with fiber parameters than that in the early-season. While this relationship did not hold for micronaire, a non-linear relationship was apparent for micronaire in 2006. This can be attributed to the boll retention pattern of cotton plants at different soil moisture levels. In addition, a prototype wireless- and GPS-based system was fabricated and developed for automated module-level fiber quality mapping. The system is composed of several subsystems distributed among harvest vehicles, and the main components of the system include a GPS receiver, wireless transceivers, and microcontrollers. Software was developed in C language to achieve GPS signal receiving, wireless communication, and other auxiliary functions. The system was capable of delineating the geographic boundary of each harvested basket and tracking it from the harvester basket to the boll buggy and the module builder. When fiber quality data are available at gins or classing offices, they can be associated with those geographic boundaries to realize fiber quality mapping. Field tests indicated that the prototype system performed as designed. The resultant fiber quality maps can be used to readily differentiate some HVI fiber parameters (micronaire, color, and loan value) at the module level, indicating the competence of the system for fiber quality mapping and its potential for site-specific fiber quality management. Future improvements needed to make system suitable for a full-scale farming operation are suggested.Item Spatial and temporal variability in cotton yield in relation to soil apparent electrical conductivity, topography, and remote sensing imagery(Texas Tech University, 2005-12) Guo, Wenxuan; Maas, Stephan J.; Zartman, Richard E.; Bronson, Kevin F.; Segarra, Eduardo; Nagihara, SeiichiAnalysis of data from multiple fields over several years provides the ability to determine under what conditions precision agriculture may be suitable. The objectives of this study were to: a) evaluate the spatial variability in cotton (Gossypium hirsutum L.) yield; b) assess the temporal stability in cotton yield over different growing seasons; c) determine the spatial and temporal variability in cotton yield in relation to soil apparent electrical conductivity (ECa), terrain attributes, and bare soil brightness; d) delineate potential management zones based on ECa, terrain attributes, and bare soil brightness obtained from satellite images and evaluate the consistency of the management zones over different growing seasons. This study was conducted on eight commercially managed cotton fields on the Southern High Plains of Texas from 2000 to 2003. Yield data were collected using harvesters equipped with yield monitors and global positioning systems (GPS). Digital elevation data were collected using a real time kinematic (RTK) GPS system. Elevation, slope, and curvatures were derived from the digital elevation data. The Normalized difference vegetation index (NDVI) was derived from multiple in-season Landsat remote sensing images. Bare soil brightness was obtained from two pre-season Landsat remote sensing images. Three potential management zones for each field in each year were delineated using the k-means and the fuzzy c-means methods. Two fields with high spatial variability in yield and soil properties were temporally stable in relative yield distribution over the four years, while the other fields were not stable. Remote sensing images explained up to 70% of yield variability in fields with high variability in yield. The strongest relationship between yield and remote sensing images occurred in the middle of the growing seasons. Soil apparent electrical conductivity, terrain attributes, and bare soil brightness explained up to 81% of yield variability, which varied with fields and years. A greater amount of yield variability was explained in drier years than in wet years. Apparent electrical conductivity and bare soil brightness were more important in explaining yield variability than terrain attributes. Both k-means and fuzzy c-means were able to separate yield and the soil properties, but k-means tended to delineate more consistent and distinct management zones. Fields with higher variability in yield and soil properties tended to have more consistent management zones over different growing seasons. Based on the results from this study, soil apparent electrical conductivity and bare soil brightness appear to be the most important soil characteristics evaluated in this study for determining management zones in the Southern High Plains of Texas. Fields with high spatial variability in yield and soil conditions appear to be better suited for PA applications.Item Spatial and temporal variability in cotton yield in relation to soil apparent electrical conductivity, topography, and remote sensing imagery(2005-12) Guo, Wenxuan; Maas, Stephan J.; Zartman, Richard E.; Bronson, Kevin F.; Segarra, Eduardo; Nagihara, SeiichiAnalysis of data from multiple fields over several years provides the ability to determine under what conditions precision agriculture may be suitable. The objectives of this study were to: a) evaluate the spatial variability in cotton (Gossypium hirsutum L.) yield; b) assess the temporal stability in cotton yield over different growing seasons; c) determine the spatial and temporal variability in cotton yield in relation to soil apparent electrical conductivity (ECa), terrain attributes, and bare soil brightness; d) delineate potential management zones based on ECa, terrain attributes, and bare soil brightness obtained from satellite images and evaluate the consistency of the management zones over different growing seasons. This study was conducted on eight commercially managed cotton fields on the Southern High Plains of Texas from 2000 to 2003. Yield data were collected using harvesters equipped with yield monitors and global positioning systems (GPS). Digital elevation data were collected using a real time kinematic (RTK) GPS system. Elevation, slope, and curvatures were derived from the digital elevation data. The Normalized difference vegetation index (NDVI) was derived from multiple in-season Landsat remote sensing images. Bare soil brightness was obtained from two pre-season Landsat remote sensing images. Three potential management zones for each field in each year were delineated using the k-means and the fuzzy c-means methods. Two fields with high spatial variability in yield and soil properties were temporally stable in relative yield distribution over the four years, while the other fields were not stable. Remote sensing images explained up to 70% of yield variability in fields with high variability in yield. The strongest relationship between yield and remote sensing images occurred in the middle of the growing seasons. Soil apparent electrical conductivity, terrain attributes, and bare soil brightness explained up to 81% of yield variability, which varied with fields and years. A greater amount of yield variability was explained in drier years than in wet years. Apparent electrical conductivity and bare soil brightness were more important in explaining yield variability than terrain attributes. Both k-means and fuzzy c-means were able to separate yield and the soil properties, but k-means tended to delineate more consistent and distinct management zones. Fields with higher variability in yield and soil properties tended to have more consistent management zones over different growing seasons. Based on the results from this study, soil apparent electrical conductivity and bare soil brightness appear to be the most important soil characteristics evaluated in this study for determining management zones in the Southern High Plains of Texas. Fields with high spatial variability in yield and soil conditions appear to be better suited for PA applications.Item Thermal imagery and spectral reflectance based system to Monitor crop condition(2005-12) Nayak, Shriniwas Surendra; Oler, James W.; Maas, Stephan J.; Lascano, Robert J.Remote sensing has gone a long way from being a scientist’s tool to being frequently used by local farmers for day-to-day irrigation decisions. Use of aircraft and satellite for remote sensing is expensive and does not have the ability to be site-specific. Handheld sensors are available for site-specific applications, but these sensors involve human intervention and cannot be used for continuous monitoring. This thesis focused on incorporating the design of a hand-held sensor in combination with GPS to implement a site-specific precision agricultural system that would continuously monitor crop water stress. This system evaluates crop stress by monitoring crop reflectance in infrared (660 nm) and near-infrared (810 nm) region of the reflectance spectrum and also crop canopy temperature. This system was mounted on a center pivot irrigation system at Texas Agricultural Experimentation Station at Helms farm, Halfway, TX. The reflectance and thermal data obtained from this system was used to evaluate crop stress. The system consisted of 36 Infrared Thermocouple (IRT) and 36 Near-Infrared (NIR, 810 nm) and Infrared (RED, 660 nm) color sensor mounted on the pivot. The Raw data from this sensor was collected and calibrated using algorithm written in Visual Basic 6. Also, Moran’s Vegetation Index and temperature (VIT) method was used to identify crop stress. This method was incorporated in the software design to create crop stress maps using ARCGIS 8.Item Thermal imagery and spectral reflectance based system to monitor crop condition(Texas Tech University, 2005-12) Nayak, Shriniwas SurendraRemote sensing has gone a long way from being a scientist’s tool to being frequently used by local farmers for day-to-day irrigation decisions. Use of aircraft and satellite for remote sensing is expensive and does not have the ability to be site-specific. Handheld sensors are available for site-specific applications, but these sensors involve human intervention and cannot be used for continuous monitoring. This thesis focused on incorporating the design of a hand-held sensor in combination with GPS to implement a site-specific precision agricultural system that would continuously monitor crop water stress. This system evaluates crop stress by monitoring crop reflectance in infrared (660 nm) and near-infrared (810 nm) region of the reflectance spectrum and also crop canopy temperature. This system was mounted on a center pivot irrigation system at Texas Agricultural Experimentation Station at Helms farm, Halfway, TX. The reflectance and thermal data obtained from this system was used to evaluate crop stress. The system consisted of 36 Infrared Thermocouple (IRT) and 36 Near-Infrared (NIR, 810 nm) and Infrared (RED, 660 nm) color sensor mounted on the pivot. The Raw data from this sensor was collected and calibrated using algorithm written in Visual Basic 6. Also, Moran’s Vegetation Index and temperature (VIT) method was used to identify crop stress. This method was incorporated in the software design to create crop stress maps using ARCGIS 8.Item Three Essays on the Economics of Precision Agriculture in Cotton Production(2011-05) Nair, Shyam; Wang, Chenggang; Maas, Stephan J.; Segarra, Eduardo; Knight, Thomas; Johnson, JeffPrecision agriculture technologies aim at adjusting input application rates to spatial and temporal requirements of the crop increasing the input use efficiency and reducing the negative environmental impact associated with agricultural chemicals. Two most important aspects of precision agriculture in cotton are development of precision agriculture technologies and dissemination of the developed technologies to the end users. The three essays in this dissertation address both of these issues by analyzing a survey of the status of precision agriculture adoption by cotton farmers in 12 states of Southern US and developing cotton irrigation strategies optimizing temporal and spatial allocation of limited water supply. In the first essay, a nested logit model was used to analyze the adoption of different variability detection technologies and the likelihood of adoption of the variable rate application conditioned on the type of variability detection technology chosen by the decision maker. The results revealed that the farmers choosing more than two variability detection technologies are more likely to adopt variable rate application technology. In the second essay a biological model was used along with an economic optimization model to determine the optimal strategy for temporal allocation of irrigation water at different levels of available irrigation water (6, 9, 12, and 15 acre-inch). From this study, it was evident that irrigating only 30%, 45%, 55%, and 70% of the field and keeping the rest of the field rainfed was the best strategy to maximize the profit under 6, 9, 12, and 15 inches of available irrigation water, respectively. The third study examined different strategies to allocate a limited amount of irrigation water among three stages of cotton growth. At 15 inches of available irrigation water, the strategy that maximizes risk adjusted profit was to use 90% of the available irrigation water from first bloom to first open boll and the rest from appearance of the first open boll to 60% open boll. At all other levels of available irrigation water, the best strategy was to apply all the available irrigation water from appearance of first bloom to appearance of first open boll.