Determination of the number of green apples in RGB images recorded in orchards

Linker, Raphael; Cohen, Oded; Naor, A.

doi:10.1016/j.compag.2011.11.007

Cited by 193 publications

(105 citation statements)

References 6 publications

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“…Note that this measure SFPR is identical to how authors of previous and recent fruit detection literature refer to "falsepositive rate" (Jiménez et al, 2000, Bulanon et al, 2010, Linker et al, 2012. Such a definition of false-positive rate is, however, confusing because in other research disciplines false-positive rate is calculated as FP/(FP+TN) (Mackinnon, 2000, Gu et al, 2009).…”

Section: Fn Tpmentioning

confidence: 91%

“…To compare results of blob analysis to the literature, the full image was classified instead of only labelled regions. Separation of fruit clusters into individual fruits is a challenging task in fruit detection (Linker et al, 2012) and was not performed in this research. To calculate the number of individual fruits detected we manually counted the number of fruits present in a cluster, before and after detection.…”

Section: Green Fruit Detectionmentioning

confidence: 99%

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Pixel classification and post-processing of plant parts using multi-spectral images of sweet-pepper

Bac

Hemming

Henten

2013

IFAC Proceedings Volumes

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Section: Fn Tpmentioning

confidence: 91%

Section: Green Fruit Detectionmentioning

confidence: 99%

Pixel classification and post-processing of plant parts using multi-spectral images of sweet-pepper

Bac

Hemming

Henten

2013

IFAC Proceedings Volumes

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“…Moreover, to predict yield during the early growing period, Linker et al [10] aimed to detect green "Golden Delicious" apple fruit from RGB images on only a part of an apple tree to facilitate the study; the algorithm accurately detected more than 85% of the apples visible in the images under natural illumination. Zhou et al [5] proposed a recognition algorithm based on colour features to estimate the number of young "Gala" apples after June drop with a close correlation coefficient of R 2 of 0.80 between apples detected by the fruit counting algorithm and those manually counted and R 2 of 0.57 between apples detected by the fruit counting algorithm and actual harvested yield.…”

Section: Introductionmentioning

confidence: 99%

Early Yield Prediction Using Image Analysis of Apple Fruit and Tree Canopy Features with Neural Networks

et al. 2017

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(1) Background: Since early yield prediction is relevant for resource requirements of harvesting and marketing in the whole fruit industry, this paper presents a new approach of using image analysis and tree canopy features to predict early yield with artificial neural networks (ANN); (2) Methods: Two back propagation neural network (BPNN) models were developed for the early period after natural fruit drop in June and the ripening period, respectively. Within the same periods, images of apple cv. "Gala" trees were captured from an orchard near Bonn, Germany. Two sample sets were developed to train and test models; each set included 150 samples from the 2009 and 2010 growing season. For each sample (each canopy image), pixels were segmented into fruit, foliage, and background using image segmentation. The four features extracted from the data set for the canopy were: total cross-sectional area of fruits, fruit number, total cross-section area of small fruits, and cross-sectional area of foliage, and were used as inputs. With the actual weighted yield per tree as a target, BPNN was employed to learn their mutual relationship as a prerequisite to develop the prediction; (3) Results: For the developed BPNN model of the early period after June drop, correlation coefficients (R 2 ) between the estimated and the actual weighted yield, mean forecast error (MFE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were 0.81, −0.05, 10.7%, 2.34 kg/tree, respectively. For the model of the ripening period, these measures were 0.83, −0.03, 8.9%, 2.3 kg/tree, respectively. In 2011, the two previously developed models were used to predict apple yield. The RMSE and R 2 values between the estimated and harvested apple yield were 2.6 kg/tree and 0.62 for the early period (small, green fruit) and improved near harvest (red, large fruit) to 2.5 kg/tree and 0.75 for a tree with ca. 18 kg yield per tree. For further method verification, the cv. "Pinova" apple trees were used as another variety in 2012 to develop the BPNN prediction model for the early period after June drop. The model was used in 2013, which gave similar results as those found with cv. "Gala"; (4) Conclusion: Overall, the results showed in this research that the proposed estimation models performed accurately using canopy and fruit features using image analysis algorithms.

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“…However, developments in image processing and machine vision technologies have been proposed as a solution to these problems. Most existing studies on fruit detection have determined the area with the pixel-based image processing method and counted the number of detected areas [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

A Novel Red Apple Detection Algorithm Based on AdaBoost Learning

Kim¹,

Choi²,

Choi³

et al. 2015

IEIE Transactions on Smart Processing and Computing

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Abstract:This study proposes an algorithm for recognizing apple trees in images and detecting apples to measure the number of apples on the trees. The proposed algorithm explores whether there are apple trees or not based on the number of image block-unit edges, and then it detects apple areas. In order to extract colors appropriate for apple areas, the CIE L*a*b* color space is used. In order to extract apple characteristics strong against illumination changes, modified census transform (MCT) is used. Then, using the AdaBoost learning algorithm, characteristics data on the apples are learned and generated. With the generated data, the detection of apple areas is made. The proposed algorithm has a higher detection rate than existing pixel-based image processing algorithms and minimizes false detection.

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Determination of the number of green apples in RGB images recorded in orchards

Cited by 193 publications

References 6 publications

Pixel classification and post-processing of plant parts using multi-spectral images of sweet-pepper

Pixel classification and post-processing of plant parts using multi-spectral images of sweet-pepper

Early Yield Prediction Using Image Analysis of Apple Fruit and Tree Canopy Features with Neural Networks

A Novel Red Apple Detection Algorithm Based on AdaBoost Learning

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