Proposed Method for Statistical Analysis of On-Farm Single Strip Treatment Trials

On-farm experimentation (OFE) allows farmers to improve crop management over time. The randomized complete blocks design (RCBD) with field-length strips as individual plots is commonly used, but it requires advanced planning and has limited statistical power when only three to four replications are implemented. Harvester-mounted yield monitor systems generate high resolution data (1-s intervals), allowing for development of more meaningful, easily implementable OFE designs. Here we explored statistical frameworks to quantify the effect of a single treatment strip using georeferenced yield monitor data and yield stability-based management zones. Nitrogen-rich single treatment strips per field were implemented in 2018 and 2019 on three fields each on two farms in central New York. Least squares and generalized least squares approaches were evaluated for estimating treatment effects (assuming independence) versus spatial covariance for estimating standard errors. The analysis showed that estimates of treatment effects using the generalized least squares approach are unstable due to over-emphasis on certain data points, while assuming independence leads to underestimation of standard errors. We concluded that the least squares approach should be used to estimate treatment effects, while spatial covariance should be assumed when estimating standard errors for evaluation of zone-based treatment effects using the single-strip spatial evaluation approach.

Towards a Machine Vision-Based Yield Monitor for the Counting and Quality Mapping of Shallots

In comparison to field crops such as cereals, cotton, hay and grain, specialty crops often require more resources, are usually more sensitive to sudden changes in growth conditions and are known to produce higher value products. Providing quality and quantity assessment of specialty crops during harvesting is crucial for securing higher returns and improving management practices. Technical advancements in computer and machine vision have improved the detection, quality assessment and yield estimation processes for various fruit crops, but similar methods capable of exporting a detailed yield map for vegetable crops have yet to be fully developed. A machine vision-based yield monitor was designed to perform size categorization and continuous counting of shallots in-situ during the harvesting process. Coupled with a software developed in Python, the system is composed of a video logger and a global navigation satellite system. Computer vision analysis is performed within the tractor while an RGB camera collects real-time video data of the crops under natural sunlight conditions. Vegetables are first segmented using Watershed segmentation, detected on the conveyor, and then classified by size. The system detected shallots in a subsample of the dataset with a precision of 76%. The software was also evaluated on its ability to classify the shallots into three size categories. The best performance was achieved in the large class (73%), followed by the small class (59%) and medium class (44%). Based on these results, the occasional occlusion of vegetables and inconsistent lighting conditions were the main factors that hindered performance. Although further enhancements are envisioned for the prototype system, its modular and novel design permits the mapping of a selection of other horticultural crops. Moreover, it has the potential to benefit many producers of small vegetable crops by providing them with useful harvest information in real-time.

A Note on the Application of an AgLeader® Cotton Yield Monitor for Measuring Peanut Yield: An Investigation in Two US states.

ABSTRACT Previous researchers demonstrated the ability to adapt an AgLeader® Cotton Monitor to a peanut combine. It was demonstrated that the field weight could be accurately predicted with average errors of less than 10% across all trials when at least five calibration loads are applied. This project focused on expanding previous work performed at the University of Georgia and other peanut optical yield monitor work by incorporating a protective deflector plate for the sensors, obtaining multiple field weights, and using the peanut sale sheets to correlate yield monitor yield to sale weight. This study was a two-university, two-state effort, including Oklahoma State University (Oklahoma), and Mississippi State University (Mississippi). Data collected during this study included multiple loads which included yield monitor weight, field weight, field moisture content, and all the information presented on the standard USDA peanut grade sheet, when available. The multi-state effort allowed for the incorporation of the two major peanut types and for the incorporation of different soil types. The goal of this study was to develop guidelines for using, calibrating, and adapting the AgLeader® Cotton Monitor for peanut harvest. Five calibration loads referenced to buy-point net weight were typically needed to bring error within acceptable limits. Results indicated that multiple local calibrations were needed to ensure high data validity and yield estimation across multiple harvest environments. The data showed that peanut type (virginia, runner and spanish) and variable soil conditions impacted yield estimation.

Large-Scale Field Study of Impact-Based Yield Monitor Performance

The grain yield monitor is the most common evaluation tool for determining the productivity of grain cropping systems. Most evaluations of grain yield monitors have focused on lab scale tests of the sensor performance and its ability to be calibrated in field trials. This study focused on the performance of the impact-based grain yield monitor during a full corn harvest season with observations and conclusions drawn on the load-to-load variation, field level, and full season accuracy from data collected over five seasons and encompassing over 2,000 evaluation loads. The load variance expectation of the impact-based yield monitor was characterized and the yield difference requirements for statistical significance were developed to aid in yield monitor based evaluations of agronomic strip trials. Following manufacturer recommended calibrations, a single cropping season calibration in corn produced field mean errors of ±5% and a season mean error of 1%. Results showed statistically significant shift in the yield monitor accuracy for grain moisture content greater than 22.5% and a load accuracy dependency on the mean mass flow rate during the full harvest season calibration evaluation. Keywords: Combine harvesters, Mass flow, Precision agriculture, Real-time sensor, Yield map, Yield monitor.

Electronic Design of a Cotton Harvester Yield Monitor Calibration System

Several yield monitors are available for use on cotton harvesters, but none are able to maintain yield measurement accuracy across cultivars and field conditions that vary spatially and/or temporally. Thus, the utility of yield monitors as tools for on-farm research is limited unless steps are taken to calibrate the systems as cultivars and conditions change. This technical note details the electronic system design for a harvester-based yield monitor calibration system for basket-type cotton strippers. The system was based upon the use of pressure sensors to measure the weight of the basket by monitoring the static pressure in the hydraulic lift cylinder circuit. To ensure accurate weighing, the system automatically lifted the basket to a target lift height, allowed the basket time to settle, then weighed the contents of the basket. The software running the system was split into two parts that were run on an embedded low-level micro-controller and a mobile computer located in the harvester cab. The system was field tested under commercial conditions and found to measure basket load weights within 2.5% of the reference scale. As such, the system was proven to be capable of providing an on-board auto-correction to a yield monitor for use in multi-variety field trials.

Man-Machine-Interface Software Design of a Cotton Harvester Yield Monitor Calibration System

Several yield monitors are available for use on cotton harvesters, but none are able to maintain yield measurement accuracy across cultivars and field conditions that vary spatially and/or temporally. Thus, the utility of yield monitors as tools for on-farm research is limited unless steps are taken to calibrate the systems as cultivars and conditions change. This technical note details the man-machine-interface software system design portion of a harvester-based yield monitor calibration system for basket-type cotton strippers. The system was based upon the use of pressure sensors to measure the weight of the basket by monitoring the static pressure in the hydraulic lift cylinder circuit. To ensure accurate weighing, the system automatically lifted the basket to a target lift height, allowed basket time to settle, then weighed the contents of the basket. The software running the system was split into two parts that were run on an embedded low-level micro-controller, and a mobile computer located in the harvester cab. The system was field tested under commercial conditions and found to measure basket load weights within 2.5% of the reference scale. As such, the system was proven to be capable of providing an on-board auto-correction to a yield monitor for use in multi-variety field trials.

Embedded Micro-Controller Software Design of a Cotton Harvester Yield Monitor Calibration System

Several yield monitors are available for use on cotton harvesters, but none are able to maintain yield measurement accuracy across cultivars and field conditions that vary spatially and/or temporally. Thus, the utility of yield monitors as tools for on-farm research is limited unless steps are taken to calibrate the systems as cultivars and conditions change. This technical note details the embedded micro-controller software system design portion of a harvester-based yield monitor calibration system for basket-type cotton strippers. The system was based upon the use of pressure sensors to measure the weight of the basket by monitoring the static pressure in the hydraulic lift cylinder circuit. To ensure accurate weighing, the system automatically lifted the basket to a target lift height, allowed the basket time to settle, and then weighed the contents of the basket. The software running the system was split into two parts, which were run on an embedded low-level micro-controller and a mobile computer located in the harvester cab. The system was field tested under commercial conditions and found to measure basket load weights within 2.5% of the reference scale. As such, the system was proven to be capable of providing an on-board auto-correction to a yield monitor for use in multi-variety field trials.