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41	GRAIN HARVESTING LOGISTICAL TRACKING – UTILIZING GPS DATA TO BETTER UNDERSTAND GRAIN HARVESTING EFFICIENCY Cheyenne Eunice/ Cox Simmons (18431367) 29 April 2024 (has links) <p dir="ltr">Precision agriculture has been around for many, many years but as technology has rapidly grown with the population, farmers are looking for more ways to improve their operation with the help of these tools. These tools help farmers manage, understand, and decide when, how and what should be done regarding the tough decisions in the field based on their machinery statues. The tools that utilize GPS location and provide farm managers with useful information and feedback on large scales of value in the Harvesting and planting operation. With previous works done focusing on identify state machine activity utilizing GPS location data with the use of loggers and algorithms this study carries on from one field to the next identifying the truth data set for each and the accuracy of the algorithm. The goal is to add a more realistic view to the states identifying turning and transporting throughout the harvesting operation in truth data and from algorithm results. Also diving into truck activity with lower standard GPS tracking to see how accurately they can be predicted to complete the harvesting cycle from vehicle to vehicle. Overall, the combine and grain cart held at 88% accuracy for labeling all state activity during the harvesting operation for multiple fields, while for the model algorithm with the grain trucks having an overall accuracy of 94%.</p> Agricultural land management Agricultural land planning grain harvest crop technology grain movement AGRICULTURE SYSTEM grain management crop management decisions harvesting decisions
42	Digital Soil Mapping of the Purdue Agronomy Center for Research and Education Shams R Rahmani (8300103) 07 May 2020 (has links) This research work concentrate on developing digital soil maps to support field based plant phenotyping research. We have developed soil organic matter content (OM), cation exchange capacity (CEC), natural soil drainage class, and tile drainage line maps using topographic indices and aerial imagery. Various prediction models (universal kriging, cubist, random forest, C5.0, artificial neural network, and multinomial logistic regression) were used to estimate the soil properties of interest. Agricultural Land Management Agricultural Land Planning Sustainable Agricultural Development Agronomy Digital soil mapping (DSM) Organic matter content Cation exchange capacity Natural soil drainage classes geostatistical methods Cubist modeling Random Forest models Universal Kriging C5.0 or see5 modeling Artificial neural network Tile drainage lines Soil spatial variation Plant Phenotyping Aerial Imagery digital elevation model (DEM) terrain attributes Soil and Landscape Very poorly drained poorly drained somewhat poorly drained moderately well drained User's accuracy producer's accuracy kappa coefficient Multinomial logit regression Tile Probe topographic wetness index Topographic Position Index slope height cross sectional curvature Relative slope position channel network distance SSURGO soils data internal validation external validation

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GRAIN HARVESTING LOGISTICAL TRACKING – UTILIZING GPS DATA TO BETTER UNDERSTAND GRAIN HARVESTING EFFICIENCY

Digital Soil Mapping of the Purdue Agronomy Center for Research and Education