Estimation of mango crop yield using image analysis – Segmentation method

Payne, A.; Walsh, Kerry B.; Subedi, P.P.; Jarvis, Dennis

doi:10.1016/j.compag.2012.11.009

Cited by 176 publications

(84 citation statements)

References 5 publications

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“…v.75, n.3, p.208-215, May/June 2018 fruits may minimize their visible area and hinder their recognition in the image. Errors resulting from the occlusion of fruits have been addressed in studies that estimate the number of fruits using methodologies based on digital images (Payne et al, 2013;Roscher et al, 2014). In addition, errors in the recognition of deformed fruits may result from the difficulty of identifying the part of the fruit exhibiting the anomaly.…”

Section: Combined Selection and Estimation Of Genetic Gainsmentioning

confidence: 99%

Papaya recombinant inbred lines selection by image-based phenotyping

Cortes

Santa-Catarina

Azevedo

et al. 2018

Sci. agric. (Piracicaba, Braz.)

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ABSTRACT:The selection of superior Carica papaya (L) genotypes depends on the availability of genetic variability and on the favorable and simultaneous response of the genotypes to those traits of most interest. However, manual phenotyping (MP) demands intensive labor, is time-consuming and expensive. The aim of the current study is to access the efficiency of image-based phenotyping (IBP) in estimating genetic parameters and in selecting F 4 recombinant inbred lines.The genetic parameters and values were estimated in accordance with the REML/BLUB procedure and combined selection using the selection index based on standardized genetic values.The majority of traits accessed through IBP showed experimental coefficients of variation similar to those found through MP. Both methodologies showed genetic parameters of similar magnitude, indicating expressive genetic variability between lines in the traits accessed in this study.The same superior lines were indicated in both methodologies and expressive genetic gains obtained through the lines were selected for all traits. IBP performance was similar to that of MP with respect to the estimates of breeding-relevant traits such as commercial fruits and yield.Thus, IBP showed efficient phenotypic assessment, as well as selective accuracy in accessing genetic variability and genetic gains, when it was compared to MP. Since IBP is far less dependent on labor, it is expected to be incorporated into the routine of papaya breeding programs as a way of increasing the number of accessed lines and, consequently, increasing genetic gains.

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Section: Combined Selection and Estimation Of Genetic Gainsmentioning

confidence: 99%

Papaya recombinant inbred lines selection by image-based phenotyping

Cortes

Santa-Catarina

Azevedo

et al. 2018

Sci. agric. (Piracicaba, Braz.)

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“…Whilst these technologies have been found to be highly effective for measuring yield in row crops [16][17][18][19][20], generally attributed to harvest index (HI) (i.e., fraction of biomass allocated to yield components divided by the total above ground biomass) [21][22][23], similar studies in perennial fruit tree crops, such as citrus [24,25], apple [7,26], pear [27], peach [28], olives [28], mango [29], and grapevines [30,31] have produced varying levels of success. For avocado, there has only been limited remote sensing research investigating fruit size and yield mapping as well as tree number auditing [4].…”

Section: Introductionmentioning

confidence: 99%

Using Worldview Satellite Imagery to Map Yield in Avocado (Persea americana): A Case Study in Bundaberg, Australia

2017

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Accurate pre-harvest estimation of avocado (Persea americana cv. Haas) yield offers a range of benefits to industry and growers. Currently there is no commercial yield monitor available for avocado tree crops and the manual count method used for yield forecasting can be highly inaccurate. Remote sensing using satellite imagery offers a potential means to achieve accurate pre-harvest yield forecasting. This study evaluated the accuracies of high resolution WorldView (WV) 2 and 3 satellite imagery and targeted field sampling for the pre-harvest prediction of total fruit weight (kg·tree −1 ) and average fruit size (g) and for mapping the spatial distribution of these yield parameters across the orchard block. WV 2 satellite imagery was acquired over two avocado orchards during 2014, and WV3 imagery was acquired in 2016 and 2017 over these same two orchards plus an additional three orchards. Sample trees representing high, medium and low vigour zones were selected from normalised difference vegetation index (NDVI) derived from the WV images and sampled for total fruit weight (kg·tree −1 ) and average fruit size (g) per tree. For each sample tree, spectral reflectance data was extracted from the eight band multispectral WV imagery and 18 vegetation indices (VIs) derived. Principal component analysis (PCA) and non-linear regression analysis was applied to each of the derived VIs to determine the index with the strongest relationship to the measured total fruit weight and average fruit size. For all trees measured over the three year period (2014, 2016, and 2017) a consistent positive relationship was identified between the VI using near infrared band one and the red edge band (RENDVI1) to both total fruit weight (kg·tree −1 ) (R 2 = 0.45, 0.28, and 0.29 respectively) and average fruit size (g) (R 2 = 0.56, 0.37, and 0.29 respectively) across all orchard blocks. Separate analysis of each orchard block produced higher R 2 values as well as identifying different optimal VIs for each orchard block and year. This suggests orchard location and growing season are influencing the relationship of spectral reflectance to total fruit weight and average fruit size. Classified maps of avocado yield (kg·tree −1 ) and average fruit size per tree (g) were produced using the relationships developed for each orchard block. Using the relationships derived between the measured yield parameters and the optimal VIs, total fruit yield (kg) was calculated for each of the five sampled blocks for the 2016 and 2017 seasons and compared to actual yield at time of harvest and pre-season grower estimates. Prediction accuracies achieved for each block far exceeded those provided by the grower estimates.

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“…Thus, this step provided final count of the mangoes within the image [47]. Another method to predict the yield of Gala apples used the RGB model and threshold method for segmentation.…”

Section: The Segmentation Methodsmentioning

confidence: 99%

Colour Feature Extraction Techniques of Fruits: A Survey

Vyas¹,

Talati²,

Naik³

2013

IJCA

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There is a great significance of agriculture in India. Fruit grading can improve the revenue of fruits. In the automated fruit grading system the most important feature is its colour. So for any automated fruit grading system one should have the idea of colour space and segmentation needs to be performed. As colour feature extraction has a vital role in grading, here a review of various colour feature extraction techniques in detail is provided. General TermsFeature Extraction, Colour Feature.

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Estimation of mango crop yield using image analysis – Segmentation method

Cited by 176 publications

References 5 publications

Papaya recombinant inbred lines selection by image-based phenotyping

Papaya recombinant inbred lines selection by image-based phenotyping

Using Worldview Satellite Imagery to Map Yield in Avocado (Persea americana): A Case Study in Bundaberg, Australia

Colour Feature Extraction Techniques of Fruits: A Survey

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