Citrus Yield Mapping System Using Machine Vision

Annamalai, Palaniappan; Lee, Won Suk

doi:10.13031/2013.13701

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…The results from the algorithm using the logistic regression classifier showed that the R 2 value between the count of dropped fruit and the actual number of fruit was 0.87. Other reported R 2 values were 0.76 by Annamalai and Lee (2003), 0.79 by Annamalai et al (2004), and 0.83 to 0.88 by Stajnko et al (2004). For correctly identified citrus fruit, this study showed a much higher rate (88.1%) than Patel et al (2012), who reported 69% (i.e., a 31% error rate).…”

Section: Accuracy Of Counting and Mass Estimationcontrasting

confidence: 48%

“…Fruit recognition using outdoor images in open areas, however, is challenging because object colors in images vary greatly under different illumination conditions. Many studies of fruit detection in outdoor images have shown reduced performance (Annamalai and Lee, 2003;Annamalai et al, 2004;Stajnko et al, 2004;Patel et al, 2012). Annamalai and Lee (2003) developed a citrus yield mapping system using machine vision with outdoor imaging.…”

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confidence: 99%

“…Many studies of fruit detection in outdoor images have shown reduced performance (Annamalai and Lee, 2003;Annamalai et al, 2004;Stajnko et al, 2004;Patel et al, 2012). Annamalai and Lee (2003) developed a citrus yield mapping system using machine vision with outdoor imaging. The acquired images were converted to the hue, saturation, and value (HSV) color space, and then fruit objects were segmented using erosion, dilation, and thresholding.…”

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confidence: 99%

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A Machine Vision System for Quantification of Citrus Fruit Dropped on the Ground under the Canopy

et al. 2015

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The overall goal of this study was to develop a machine vision system to quantify dropped citrus fruits on the ground. Specific objectives were: (1) to build a machine vision system suitable for citrus grove field conditions, (2) to develop an image enhancement algorithm for varying illumination conditions, and (3) to develop an image processing algorithm to estimate citrus fruit drop count and mass. The image processing algorithm consisted of (1) illumination enhancement using a retinex algorithm, (2) classification, (3) segmentation using a watershed algorithm with h-minima transform, and (4) ellipse fitting for mass estimation. Performances of the algorithms were evaluated in terms of correct identification and false positive errors. The average correct identification rate was 88.1%, 83.6%, and 82.9% for logistic regression, k-nearest neighbor (kNN), and Bayesian classifiers, respectively. False positive errors were 13.7%, 40.9%, and 17.9% for logistic regression, kNN, Bayesian classifiers, respectively. The results demonstrate the system's ability to quantify dropped fruits with specific geo-referenced location information. Spatially varied fruit drop maps plotted from the results can assist growers in finding problematic areas in their citrus groves more efficiently while reducing inspection and treatment costs. Such maps can also facilitate treatment of citrus Huanglongbing (HLB) disease in combination with HLB intensity data, psyllid counts, fertilization programs, and other block-specific management practices.

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Section: Accuracy Of Counting and Mass Estimationcontrasting

confidence: 48%

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confidence: 99%

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confidence: 99%

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A Machine Vision System for Quantification of Citrus Fruit Dropped on the Ground under the Canopy

et al. 2015

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“…For example, a common modification is the addition of guidance systems to tractors and combines, which add the ability to accurately map fields and allow for the creation of planting and fertilizer prescriptions [3]. Additionally, these systems can be used to track yields for making field management decisions [4]. Other examples of modifications include adding modern fertilizer application equipment and technologies to planters [5], installing grain-saver kits to combines [6], implementing row-following technology to headers [7], etc.…”

Section: Introductionmentioning

confidence: 99%

Retrofitting and Testing of a Pull-Type Small-Grain Combine Harvester

2023

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A cost-effective harvesting method and equipment for small-scale farms is essential to ensure the viability and sustainability of their operations. This study aims to retrofit and test a pull-type combine harvester for effective and efficient operations in small grains. A 1960s-era functional combine harvester was retrofitted with a stripper header rotor in place of the sickle bar cutting system to increase the harvesting capacity. Preliminary field testing found that the original header auger and feeder house could not handle the changed crop composition at an increased capacity and did not properly convey the grain from the auger to the feeder house and onto the threshing system. Therefore, redesign modifications were conducted for the auger and feeder house to help increase the efficiency and capacity required to use the stripper header. Various design concepts were proposed, analyzed, and prototyped in this study. The machine performance of the material-conveying efficiency of the wheat-material-other-than-grain mixture was evaluated at various material throughput rates. The results showed that the auger shim and feeder house paddle redesign increased the efficiency from 80.68% to 98.56%, as compared to the original machine configuration. The cost-effective and high-performance pull-type combine harvester proposed in this study has a high potential in solving the bottleneck problem of local production of small grains by small-scale farming operations.

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“…Similar studies were done by Safren et al (2007), who used multi-spectral images of the apple trees. Annamalai and Lee (2003) created an algorithm for image analysis to estimate citrus distribution in an orchard. The main aim of the work was to count the citrus fruit captured by a digital image.…”

Section: Introductionmentioning

confidence: 99%

In-season prediction of yield variability in an apple orchard

Liakos¹,

Tagarakis²,

Aggelopoulou³

et al. 2017

Europ.J.Hortic.Sci

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Prediction of the spatial variability of apple yield as early as possible in the season is very important for farm managers. Many researchers used colour images of the apple trees and algorithms in order to develop prediction models of apple yield. The objective of this research was to study the spatial variability in an apple orchard and to develop methods for predicting yield variability within the growing period and as early as possible to permit management decisions. A commercial point-and-shoot and a multi-spectral camera were utilised to obtain images of the apple trees during the flowering period under daylight in a cv. Fuji apple orchard in Greece. The images were taken in April 2010 and 2011. Fruit yield was recorded at harvest each year. Supervised classification was used to isolate and calculate the flowers' pixel density of the whole image. For both years of the study, the results showed that, with both cameras, the estimated distribution of the flowers was correlated with the final yield distribution; however, for the second year, the correlation was slightly lower, probably due to adverse climatic conditions during and after the pollination period, which resulted in low yield. Multi-spectral images gave the best results in both years (r = 0.859 in 2010 and r = 0.827 in 2011). Keywords apples, flower distribution, multi-spectral images, spectral analysis, yield variability ternative way is to find characteristics of the crop within the growing season that can safely predict yield variability and permit site-specific management. Tanaka et al. ( 2004) used a CCD camera to take pictures of whole trees individually. By using the NDVI values of the trunk, the leaves and the stems, they segmented them. Except from identification of green objects, NDVI was used to separate the dry soil from the wet soil, and the muddy water from the clear water (Oklahoma State University, 2013). The successful identification of nongreen objects by using NDVI was done because each object has different spectral signatures at different wavelengths.In orchards, Bulanon et al. (2002) developed a fruit detection system for robotic harvest of cv. Fuji apples based on a model which used the red colour to segment the apples from the background of the images. Similar work was done by Wachs et al. (2010), who tried to detect green apples by using thermal infrared and colour images, while Zhou et al. (2012) analyzed images of 'Gala' apple trees by using an algorithm with colour difference red minus blue and green minus red to define the fruits on the trees. Five years later,

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Citrus Yield Mapping System Using Machine Vision

Cited by 9 publications

References 17 publications

A Machine Vision System for Quantification of Citrus Fruit Dropped on the Ground under the Canopy

A Machine Vision System for Quantification of Citrus Fruit Dropped on the Ground under the Canopy

Retrofitting and Testing of a Pull-Type Small-Grain Combine Harvester

In-season prediction of yield variability in an apple orchard

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