Precision agriculture (PA) can be defined as a set of technologies that have helped propel agriculture into the computerized information-based world, and is designed to help farmers get greater control over the management of farm operations. Because of its potential to spatially reduce yield variability within the field through variable rate application of nutrients it is thought to be a production risk management instrument. Subsurface drip irrigation (SDI) is another production risk management technology that is generating interest from the farming community as a result of new technological improvements that facilitate equipment maintenance and reduces water consumption.In the first article the production risk management potential of these two technologies was investigated both for each technology and for a combination of the two. Simulated yield data for corn, wheat and soybeans were obtained using EPIC, a crop growth simulation model. Mathematical programming techniques were used in a standard E-V framework to reproduce the production environment of a Kentucky commercial grain farmer in Henderson County. Results show that for risk averse farmers, the lowest yield variability was obtained with the SDI technology. The highest profit level was obtained when the two technologies were combined.Investment in two sets of equipments (PA and SDI) to maximize profitability and reduce risk could however expose many farm operations to financial risk. In the second article, a discrete stochastic sequential programming (DSSP) model was used to analyze the impact of PA and/or SDI equipment investment on the farm's liquidity and debt to asset ratio.In the last article, the cotton sector in Benin, West Africa, was utilized to study the transferability of PA technology to a developing country. Properly introduced, precision agriculture (PA) technology could help farmers increase profitability, improve management practices, and reduce soil depletion. An improved production system could also help farmers better cope with the policy risk related to cotton production. Results from the two models show that PA is less profitable for the risk neutral farmer but more profitable for the risk averse one when compared to conventional production practices. The adoption of the new technology also has very little impact on the choice of crop rotation made by the farmer.

<p>Soil residual herbicides are a vital component of the best management practices (BMPs), to provide early-season weed control in most cropping systems. The availability of a biologically effective dose of a soil residual herbicide in the soil solution is dependent on several soil parameters including soil texture, organic matter (OM), and pH.  Soil residual herbicides are currently applied as a uniform application rate over an individual field; yet soil properties can vary spatially within agricultural fields. Therefore, areas of the field are being over- and under-applied when using a uniform application rate. By integrating variable-rate (VR) technology with soil residual herbicides, the correct rate could be applied based on the intra-field soil variability. However, the extent of spatial soil variability within a field and the impact on herbicide application rates has not been well-characterized to inform whether soil residual herbicide applications should move towards variable rate applications. Therefore, the objectives of this research were to 1) determine the extent of intra-field variability of soil texture and organic matter in ten commercial Indiana fields, 2) quantify the reliability of five different combinations of spatial soil data sources, 3) determine the impact of soil sample intensity on map development and the classification accuracy for VR applications of soil residual herbicides, 4) quantify the impact of VR herbicide application on the total amount and spatial accuracy of herbicide applied according to product labels, and 5) determine if the intensive spatial characterization of soil properties is related to weed seedbank abundance and species richness to improve predictive weed management using soil residual herbicides.</p> <p><br></p> <p>Commercial soil data was generated by intensively collecting 60 soil samples in a stratified random sampling pattern in 10 agricultural fields across Indiana. Analysis of this data from commercial fields confirmed inherent field variability that would benefit from multiple management zones according to the labeled rate structures of pendimethalin, s-metolachlor, and metribuzin. Therefore, further research was conducted to determine an accurate and reliable method to delineate the fields into management zones for variable-rate residual herbicide applications based on the spatial soil variability and herbicide labels. </p> <p><br></p> <p>A modified Monte Carlo cross-validation method was used to determine the best source of spatial soil data and sampling intensity for delineating management zones for variable-rate applications of pendimethalin, s- metolachlor, and metribuzin. These sources of spatial soil data included: Soil Survey Geographic database (SSURGO) data, intensive soil samples, electrical resistivity sensors, and implement mounted optical reflectance sensors using VNIR reflectance spectroscopy. The mean management zone classification accuracy for maps developed from soil samples with and without electrical conductivity was similar for 75% of all maps developed across each field, herbicide, and sampling intensity. The method of using soil sampling data combined with electrical conductivity (SSEC) maps was most frequently the top performing source of spatial soil data. The most reliable sampling intensity was one sample per hectare which resulted in lower root mean squared error (RMSE) OM values, higher management zone classification accuracy, and more reliable predictions for the number of management zones within each field. </p> <p><br></p> <p>Using VR maps developed from SSEC with one sample per hectare sampling intensity, additional research was conducted to compare the amount of herbicide and field area that was over-or under-applied with a uniform application rate compared to a VR application for 10 corn and soybean residual herbicides. Although research from our previous study documented that spatial soil variability was extensive enough to require two or more management zones for all fields, the same labeled herbicide dose defined for multiple soil conditions led to 20% of all maps not requiring a variable rate application (VRA). Additionally, no difference was shown in the total amount applied of herbicide in an individual field between a variable and uniform application rate for all herbicides. Nonetheless, nearly half of all VR maps had 10% or more of the field area misapplied with a uniform application rate and justifies further research to determine if the proper placement of residual herbicide adds value through increased weed control in the field areas being under-applied. </p> <p><br></p> <p>Similar to soil residual herbicides, weed seedbank abundance and species richness were impacted by the variable soil conditions present within the field area. The seedbanks favor the establishment in areas of the field that promote vigorous germination, growth, and reproduction next to the competing crop. Therefore, soil sampling and weed seedbank greenhouse grow-outs were conducted in four fields to gain a better understanding in the relationship between the spatial soil and weed seedbank variability. All weed seedbank characteristics were shown to be spatially aggregated. Even though no individual or combination of soil parameters consistently explained the variability of weed seedbank abundance, species richness, or individual weed species across all four fields. However, clay content was the most persistent soil parameter to negatively impact (lower seedbank values) the soil weed seedbank.</p> <p><br></p> <p>Further field studies should be conducted across multiple sites to determine if variable-rate residual herbicide applications aid farmers by reducing the risk of crop injury in over-applied field areas and increased weed control in the areas being under-applied.  These studies should also access whether earlier emergence and/or greater weed densities occur in field areas receiving sublethal herbicide doses compared to areas receiving the optimal application rate. Additional research should investigate the utility of VR residual herbicide applications when tank-mixing multiple products during an application. Particularly, when the soil parameters used for selecting the herbicide rate are not defined the same across herbicide labels </p>

<p dir="ltr">The increasing global need for food security and sustainable agriculture underscores the urgency of advancing field phenotyping for enhanced plant breeding and crop management. Soybean, a major global protein source, is at the forefront of these advancements. Proximal sensing in soybean phenotyping offers a higher signal-to-noise ratio and resolution but has been underutilized in large-scale field applications due to low throughput and high labor costs. Moreover, there is an absence of automated solutions for in vivo proximal phenotyping of dicot plants. This thesis addresses these gaps by introducing a comprehensive, technologically sophisticated approach to modern field phenotyping.</p><p dir="ltr">Fully Automated Proximal Hyperspectral Imaging System: The first chapter presents the development of a cutting-edge hyperspectral imaging system integrated with a robotic arm. This system surpasses traditional imaging limitations, providing enhanced close-range data for accurate plant health assessment.</p><p dir="ltr">Robust Leaf Pose Estimation: The second chapter discusses the application of deep learning for accurate leaf pose estimation. This advancement is crucial for in-depth plant analysis, fostering better insights into plant health and growth, thereby contributing to increased crop yield and disease resistance.</p><p dir="ltr">PhenoBee – A Drone Mobility Platform: The third chapter introduces 'PhenoBee,' a dronebased platform designed for extensive field phenotyping. This innovative technology significantly broadens the capabilities of field data collection, showcasing its viability for widespread aerial phenotyping.</p><p dir="ltr">Adaptive Sampling for Dynamic Waypoint Planning: The final chapter details an adaptive sampling algorithm for efficient, real-time waypoint planning. This strategic approach enhances field scouting efficiency and precision, ensuring optimal data acquisition.</p><p dir="ltr">By integrating deep learning, robotic automation, aerial mobility, and intelligent sampling algorithms, the proposed solution revolutionizes the adaptation of in vivo proximal phenotyping on a large scale. The findings of this study highlight the potential to automate agriculture activities with high scalability and identify nutrient deficiencies, diseases, and chemical damage in crops earlier, thereby preventing yield loss, improving food quality, and expediting the development of agricultural products. Collectively, these advancements pave the way for more effective and efficient plant breeding and crop management, directly contributing to the enhancement of global food production systems. This study not only addresses current limitations in field phenotyping but also sets a new standard for technological innovation in agriculture.</p>