Yield Mapping of Wheat and Corn Using a Continuous, On-Combine Yield Monitor

2015 ◽  
pp. 91-91
Author(s):  
Ron Campbell
Keyword(s):  
Yield Mapping ◽  
Yield Monitor

scholarly journals Model to estimate the sampling density for establishment of yield mapping

2012 ◽  
Vol 16 (4) ◽  
pp. 449-457 ◽  
Author(s):  
Graciele R. Spezia ◽  
Eduardo G. de Souza ◽  
Lúcia H. P. Nóbrega ◽  
Miguel A. Uribe-Opazo ◽  
Marcos Milan ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Interpolation Method ◽  
Multiple Regression Model ◽  
Kappa Index ◽  
Yield Mapping ◽  
Yield Monitor ◽  
Sampling Density ◽  
Productive Area ◽  
Using Data ◽  
A Site

Yield mapping represents the spatial variability concerning the features of a productive area and allows intervening on the next year production, for example, on a site-specific input application. The trial aimed at verifying the influence of a sampling density and the type of interpolator on yield mapping precision to be produced by a manual sampling of grains. This solution is usually adopted when a combine with yield monitor can not be used. An yield map was developed using data obtained from a combine equipped with yield monitor during corn harvesting. From this map, 84 sample grids were established and through three interpolators: inverse of square distance, inverse of distance and ordinary kriging, 252 yield maps were created. Then they were compared with the original one using the coefficient of relative deviation (CRD) and the kappa index. The loss regarding yield mapping information increased as the sampling density decreased. Besides, it was also dependent on the interpolation method used. A multiple regression model was adjusted to the variable CRD, according to the following variables: spatial variability index and sampling density. This model aimed at aiding the farmer to define the sampling density, thus, allowing to obtain the manual yield mapping, during eventual problems in the yield monitor.

DEVELOPMENT AND TESTING OF A CITRUS YIELD MONITOR

2002 ◽  
Vol 18 (4) ◽  
Author(s):  
S. D. Tumbo ◽  
J. D. Whitney ◽  
W. M. Miller ◽  
T. A. Wheaton
Keyword(s):  
Yield Monitor

Field-testing of a sugar cane yield monitor in Brazil

2004 ◽  
Author(s):  
J. P. Molin ◽  
L. A. A. Menegatti
Keyword(s):  
Sugar Cane ◽  
Field Testing ◽  
Cane Yield ◽  
Yield Monitor

Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management

2014 ◽  
Author(s):  
W.S. Lee ◽  
Victor Alchanatis ◽  
Asher Levi
Keyword(s):  
Image Registration ◽  
Navigation System ◽  
Thermal Imaging ◽  
Imaging System ◽  
Citrus Fruit ◽  
Mobile Platform ◽  
Yield Estimation ◽  
Yield Mapping ◽  
Thermal Images ◽  
Mapping System

Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved. 

Citrus Yield Mapping System in Natural Outdoor Scenes using the Watershed Transform

2006 ◽  
Author(s):  
Citrus ◽  
Fruit size ◽  
Machine vision ◽  
Watershed transform ◽  
Yield mapping
Keyword(s):  
Yield Mapping ◽  
Watershed Transform ◽  
Mapping System ◽  
Outdoor Scenes
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