A vision-based robust grape berry counting algorithm for fast calibration-free bunch weight estimation in the field

Liu, Scarlett; Zeng, Xiangdong; Whitty, Mark

doi:10.1016/j.compag.2020.105360

Cited by 37 publications

(40 citation statements)

References 16 publications

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“…To evaluate the GBCNet model performance we adopt the most common metrics employed in both agricultural yield estimation and crowd-counting domains [13,17,44,45], i.e., Mean Absolute Error (MAE) and Mean Squared Error (MSE). These are defined as follows:…”

Section: Performance Metricsmentioning

confidence: 99%

“…An even better and more appealing opportunity for farmers is to employ the smartphone [11][12][13][14] they already have and use in their daily activities. This simplified approach can overcome the current procedure based on destructive sampling (cutting off and weighting a collection of grape bunches) to obtain a yield estimate, as proposed in a rich line of research initiated by Nuske and colleagues in [15,16], that can help in increasing their productivity, even if sometimes specific setups are required [17]. Such gain is boosted by the coupling of the hardware technological advancement with the simultaneous scientific leap in mathematics and computer sciences.…”

Section: Introductionmentioning

confidence: 99%

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GBCNet: In-Field Grape Berries Counting for Yield Estimation by Dilated CNNs

et al. 2020

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We introduce here the Grape Berries Counting Net (GBCNet), a tool for accurate fruit yield estimation from smartphone cameras, by adapting Deep Learning algorithms originally developed for crowd counting. We test GBCNet using cross-validation procedure on two original datasets CR1 and CR2 of grape pictures taken in-field before veraison. A total of 35,668 berries have been manually annotated for the task. GBCNet achieves good performances on both the seven grape varieties dataset CR1, although with a different accuracy level depending on the variety, and on the single variety dataset CR2: in particular Mean Average Error (MAE) ranges from 0.85% for Pinot Gris to 11.73% for Marzemino on CR1 and reaches 7.24% on the Teroldego CR2 dataset.

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Section: Performance Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GBCNet: In-Field Grape Berries Counting for Yield Estimation by Dilated CNNs

et al. 2020

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“…Inspired by the work mentioned in paper [32], the proposed algorithm first localizes the bunch as the initial Region of Interest (ROI) after the manual crop operation. The reason we set this manual cropping is that it is difficult to get a clean image which only contains one bunch with a backing board; specifically the problem of identifying the peduncle is challenging when there are multiple sub-bunches, tendrils or wings.…”

Section: Internal Image Processing Algorithm Of 3dbunchmentioning

confidence: 99%

“…Once the image is correctly acquired, 3DBunch applies the proposed algorithm that is improved from the method presented by Liu et al [32] to estimate berries by adapting it with iOS system. The internal algorithm can be divided into four major steps from the perspective of image analysis and the parts amended from the original work are explained as follows:…”

Section: Internal Image Processing Algorithm Of 3dbunchmentioning

confidence: 99%

“…The performance and computational efficiency of this app is tested and reported regarding estimating the number of both green and purple grape berries per bunch from single images. The number of berries in a bunch image is strongly correlated to the actual number in the real bunch [32], and the radii distribution of grape berries and other information related to each sampled bunch can be logged in a cloud server by 3DBunch.…”

Section: Introductionmentioning

confidence: 99%

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3DBunch: A Novel iOS-Smartphone Application to Evaluate the Number of Grape Berries per Bunch Using Image Analysis Techniques

2020

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Evaluating the number of berries per bunch is a vital step of grape yield estimation in viticulture but is a labour intensive task for traditional manual measurement. Therefore, this paper develops a novel smartphone application for counting berries automatically from a single image. The application, called 3DBunch, acquires images from the camera or the album on a smartphone, and then estimates the number of berries by a reconstructed 3D bunch model based on the proposed image analysis techniques that are embedded in the developed iOS app. It also presents features of visualising the statistics of the reconstructed bunch, which including of the distribution of detected berry size in pixels and the total number of berries. It also has the capability of presenting sampling related information, which includes the person who conducted the sampling, the location of samples, the dates of sampling, the variety, farm, vineyards etc. The application was evaluated both on a simulator in a commercial computer and an iPad mini 4. By analysing 291 bunch images from two varieties the app achieved an accuracy of 91% regarding berry counting per bunch. Additionally, the computational time consumed to process 100 images on iPad mini 4 was studied and returned an average of 7.51 seconds per image. Getting these results with only a smartphone and a small backing board for capturing a photo with a single bunch, 3DBunch provides an efficient way for farmers to count berries in-vivo and it is available on iOS App Store now. INDEX TERMS berry counting, bunch analysis, image analysis, iOS application, yield estimation

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