Enhancing Peanut Harvesting: Evaluating Factors That Influence Yield and Developing a Web-Based Field Drying Forecast Tool

Peanuts have one of the largest economic impacts of agronomic crops in the State of South Carolina. Peanut harvest operations involve several steps (digging, curing, combining) using equipment unique to peanut production, including peanut digger/shaker/inverters. This thesis evaluates several aspects of peanut harvest operations, including how precision agriculture technologies can enhance decision-making and enhance production. Several studies were conducted to analyze how various aspects of peanut harvesting operations contribute to decrease losses in yield and revenue. The first study evaluated the influence of operator experience on mean absolute guidance line deviation during peanut digging operations and the associated effects on recovered yield. This study was completed in two experiments where, in the first experiment, mean absolute guidance line deviation while digging peanuts was measured for operators with varying levels of operation experience. A second experiment was conducted to evaluate how well the perceived row center aligned with the actual row center, as a function of row orientation and seeking distance. Results from the first experiment showed operators with lower experience had significantly higher mean absolute guidance line deviations, relative to automatic steering and high experience operators (3.3 cm (1.3 in), 5.1 cm (2.0 in), 7.6 cm (3.0 in), for automatic steering, high experience, and low experience respectively). This deviation did not translate to significant differences in yield loss between groups, but yield loss was significantly correlated with guidance line deviation. One interpretation of this is that an inexperienced operator paired with an automatic guidance system can perform at the same level or better compared to a highly skilled operator using manual steering. Results from the second experiment showed no significant effects on cross-track distance from row orientation. However, seeking distance had a significant effect on the cross-track distance where the perceived row center was closer to the actual row center at the far distance. An economic evaluation was conducted based on a $25,000 automatic steering system cost, yield loss projections from this study and others, along with labor costs differences from replacing a high skilled operator with a low skilled operator. It was found that a system would payoff after digging between 96 to 128 ha of peanuts. Another objective of these studies was to analyze the effect of canopy compaction due to wheel traffic on recovered yield and moisture content in peanut digging operations. Peanut digger manufacturers recommend that dual rear wheels be removed when digging peanuts with 2 and 4 row diggers. The removal of dual wheels can be tedious and at times dangerous due to heavy weights and their large size. This removal is recommended as the dual wheels will compact two rows of plants and is thought to effect yield and quality. This study also explored the use of UAS DEMs and orthographic imagery to measure windrow volumes. This was conducted in two separate tests, one in a heavy soil texture and another in a light soil texture. Results showed that in light soil texture canopy traffic compaction had a significant effect on windrow volume and the moisture content at combining, where compacted plots had lower windrow volumes and lower kernel moisture contents at harvest. However, in heavy soil texture plots these effects were found not significant, but similar trends were shown. For both soil textures, canopy traffic compaction did not have a significant effect on recovered yield, however a negative trend in yield was seen in plots that were compacted. A final component of this work was to create a web-based application to allow farmers to estimate and forecast windrowed peanut drying and to support scheduling of peanut digging and harvesting operations. The application was based on a previously developed windrow drying model, combined with stie specific weather forecasts. The application also warns the user of any potential cold weather injury due to near freezing temperatures associated with high peanut moisture contents in the field. The culmination of the results and guidance from these studies can help producers improve peanut harvest decision making to reduce losses and improve profitability