Search

Interpolation techniques are usually employed in the yield maps generation. In this article, a method based in spatial low-pass filtering is presented and compared with interpolation techniques. The method consists in the application of low-pass filters typically used for image processing. The yield map is constructed in a similar way to a Digital Elevation Model (DEM), except for the fact that heights represent crop quantities.

The presence of weeds, bare spots, and variation in fruit yield within wild blueberry fields emphasizes the need for yield mapping for site-specific application of agrochemicals. An automated yield monitoring system (AYMS) consisting of a digital color camera, differential global positioning system, custom software, and a ruggedized laptop computer was developed and mounted on a specially designed Farm Motorized Vehicle (FMV) for real-time fruit yield mapping. Two wild blueberry fields were selected in central Nova Scotia to evaluate the performance of the AYMS. Calibration was carried out at 38 randomly selected data points, 19 in each field. The ripe fruit was hand-harvested out of a 0.5- × 0.5-m quadrant at each selected point and camera images were also taken from the same points to calculate the blue pixel ratio (fraction of blue pixels in the image). Linear regression was used to calibrate the actual fruit yield with percentage blue pixels. Real-time yield mapping was carried out with AYMS. Custom software was developed to acquire and process the images in real-time, and store the blue pixel ratio. The estimated yield per image along with geo-referenced coordinates was imported into ArcView 3.2 GIS software for mapping.

A machine vision-based citrus fruit counting system was developed for a continuous canopy shake and catch harvester. The system consisted of a 3CCD camera, four halogen lamps, an encoder, and a laptop computer. A total of 719 images were taken during an experiment on a test bench at the Citrus Research and Education Center (Lake Alfred, Fla.) and were used for analysis. The system was also tested on a canopy shake and catch harvester at a grove located in Fort Basinger, Florida, where a total of 773 images were acquired and 60 images were used for validation. Fruit weight was measured during image acquisition in 14 test bench experiments and in two field tests with a commercial canopy shake and catch harvester. An image processing algorithm that could identify fruit and measure its size was developed using a Bayesian classifier, morphological operations, and watershed segmentation. From the sets of color images, the number of fruit and total fruit areas were measured using the developed algorithm. Finally, the number of citrus fruits that were identified by the image processing algorithm during the test bench experiment was compared against actual fruit weight. The coefficient of determination (R2) between them was 0.962. To validate the canopy shake and catch harvester experiment, the number of fruit was counted manually from a total of 60 images. Density clustering was used to enhance the result of the Bayesian classifier. The manual count was compared with the image processing algorithm count. The R2 value was 0.891 between the actual and estimated number of fruit.

In this review, the process of estimating crop yield from yield monitor data is traced from the instant of harvest, through combine processes and sensing, to post-harvest processing, yield estimation and mapping. Unified notation is applied for easy comparison. Models for various components contributing to mapped yield estimates are detailed. These include approaches to determining basic properties such as combine position, gathering width and area swept out as a function of time. Special attention is given to comparison of the commonly-used deterministic flow model with several probabalistic alternatives that have been offered. Though there has been little success in using flow models to mitigate mixing effects, the results have been better for determining transport delay. Post-processing is discussed, including yield data screening as well as mapping. The recent integration of remote sensing with yield monitor data is described. In the proper context, this integration of remote sensing shows potential to increase the accuracy and spatial resolution of yield mapping.

A cooperative effort between researchers, manufacturers, and growers has been investigating precision farming applications in Florida citrus. Citrus yields, based on the location of volume-based containers, were mapped using a conventional fruit-loading truck, manual harvesters (> 99% of Florida citrus is manually harvested), and GIS/GPS components. These maps were overlaid on geo-referenced aerial photographs of the tree canopies. Two fruit weighing systems were mounted on a fruit-loading truck and integrated with the GIS/GPS components to investigate mapping weight-based yields. Results to date indicate the truck-mounted weighing systems were within 1 to 6% of certified scale weights on 20 t loads of fruit. Electronically recording harvester identity is being integrated with yield mapping to make the entire system more reliable and attractive to harvesters and growers.

A diaphragm-based grain flow sensor has been developed for use on a grain combine. The diaphragm sensor is similar to conventional impact flow sensors, but it uses a flexible fabric-reinforced rubber diaphragm to isolate the grain from the load cell that measures the impact force. The original geometry of the elevator delivery area is preserved; no plates or other devices protrude into the grain flow. Accuracy of the sensor was determined by comparison to a catch bin and a weighing conveyor. The relationship between impact force and grain flow was linear at high grain flows, but somewhat non-linear at low flow rates. Diaphragm life has exceeded 6800 Mg (250,000 bushels).

Location coordinate information is needed in precision agriculture to map in-field variability, and to serve as a control input for variable rate application. Differential global positioning system (DGPS) measurement techniques were compared with other independent data sources for sample point location and combine yield mapping operations. Sample point location could be determined to within 1 m (3 ft) 2dRMS using C/A code processing techniques and data from a high-performance GPS receiver. Higher accuracies could be obtained with carrier phase kinematic positioning methods, but this required more time and was a less robust technique with a greater potential for data acquisition problems. Data from a DGPS C/A code receiver was accurate enough to provide combine position information in yield mapping. However, distance data from another source, such as a ground-speed radar or shaft speed sensor, was needed to provide sufficient accuracy in the travel distance measurements used to calculate yield on an area basis.

An optically–based cotton flow rate measurement device was developed for use in a site–specific yield mapping system. Variations of the final design were field–tested in Tennessee during three harvest seasons. System performance was evaluated by harvesting loads of cotton, integrating flow rate measurements over time to obtain a predicted total mass for each load, and comparing the predicted mass values with actual mass measurements acquired using scales. Integrated flow rate measurements consistently predicted actual load masses with a mean absolute (unsigned) error of 4.0% for all of the loads harvested during the tests. The mean error (signed) was –0.1% with a sample standard deviation of 5.1%. Sensors were never cleaned during any of the harvest seasons, and performance degradation due to debris accumulation on sensor faces was not observed. A subset of test data was analyzed to determine how changing field conditions affected system performance. Data were evaluated for detectable performance effects due to differences in cotton variety, seed cotton moisture content, average cotton flow rate, total load weight, and/or the amount of time elapsed since last system calibration. Cotton variety and average cotton flow rate were shown to affect load mass prediction errors. These results suggest that the system calibration should be checked when the variety being harvested changes, as additional calibrations may be necessary to provide optimum results, and that further optimization of flow rate prediction algorithms may be possible.

Yield mapping has become an important function in site–specific management systems. Decisions of economic significance are made based upon the patterns and summary statistics of the yield data recorded during harvest. Unfortunately, yield maps frequently contain data points that are not accurate estimates of the yield at that point. The yield estimate at any given point is affected by a number of factors, including the number of combines used in a field, the shape of the field, yield and moisture calibration, harvest pattern, and operator practices. By relying on limits and patterns in yield data, filtering can be done prior to mapping to remove some of the problems. Yield data generated by producer cooperators was obtained for use in several research projects. An unsupervised filtering technique for exported yield files was developed and tested on 10 fields of corn, sorghum, and rice. The inaccurate yield points were identified with filter functions based on yield limits, moisture limits, travel distance, yield surges, and less than full header width. This filtering algorithm resulted in a higher field average and lower standard deviation than either the unfiltered data or data filtered with maximum and minimum thresholds alone. The yield data filter removed up to 11% of the data points, with yield distributions being primarily affected at the upper and lower extremes. The filter was judged successful in improving yield map accuracy.

A labor tracking and yield mapping system for manual citrus harvesting was developed and evaluated in two commercial harvesting operations in Florida. The system consisted of button recognition and barcode units, integrated with a differential global positioning system (DGPS) unit mounted on a truck, which was used to handle fruit collection containers (tub or bin) in the citrus grove. When the truck operator stopped to load each full tub or bin, the button and barcode of the harvester who filled the container was activated. This action recorded the number or name assigned to the harvester and the DGPS location of the container. The button unit was more reliable and was designed with an audible (alarm) and a visible (light) feedback to provide the truck operator verification of the recorded information. The system tallied the number of containers each harvester had harvested for the day and allowed the entry of other information pertinent to the harvesting operation. The DGPS data were used to map the yields with ArcView software. Field tests showed the labor tracking system was reliable and user friendly.