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

Cheng, Hong; Damerow, Lutz; Sun, Yifei; Blanke, Michael

doi:10.3390/jimaging3010006

Cited by 101 publications

(41 citation statements)

References 16 publications

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“…This method is simple but requires the reference object to be placed on the same plane as the target, which makes it impractical. For example, Cheng et al [ 21 ] utilised 50 mm diameter white and red spheres placed on trees in the estimation of apple sizes in an orchard, with accurate estimation of size only for the fruit close to the reference objects. Ultrasonics: Murali and Won Suk [ 22 ] used ultrasonic sensors and machine vision techniques to estimate citrus fruit size on trees.…”

Section: Introductionmentioning

confidence: 99%

On-Tree Mango Fruit Size Estimation Using RGB-D Images

Wang

Walsh

Verma

2017

Sensors

139

100

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In-field mango fruit sizing is useful for estimation of fruit maturation and size distribution, informing the decision to harvest, harvest resourcing (e.g., tray insert sizes), and marketing. In-field machine vision imaging has been used for fruit count, but assessment of fruit size from images also requires estimation of camera-to-fruit distance. Low cost examples of three technologies for assessment of camera to fruit distance were assessed: a RGB-D (depth) camera, a stereo vision camera and a Time of Flight (ToF) laser rangefinder. The RGB-D camera was recommended on cost and performance, although it functioned poorly in direct sunlight. The RGB-D camera was calibrated, and depth information matched to the RGB image. To detect fruit, a cascade detection with histogram of oriented gradients (HOG) feature was used, then Otsu’s method, followed by color thresholding was applied in the CIE L*a*b* color space to remove background objects (leaves, branches etc.). A one-dimensional (1D) filter was developed to remove the fruit pedicles, and an ellipse fitting method employed to identify well-separated fruit. Finally, fruit lineal dimensions were calculated using the RGB-D depth information, fruit image size and the thin lens formula. A Root Mean Square Error (RMSE) = 4.9 and 4.3 mm was achieved for estimated fruit length and width, respectively, relative to manual measurement, for which repeated human measures were characterized by a standard deviation of 1.2 mm. In conclusion, the RGB-D method for rapid in-field mango fruit size estimation is practical in terms of cost and ease of use, but cannot be used in direct intense sunshine. We believe this work represents the first practical implementation of machine vision fruit sizing in field, with practicality gauged in terms of cost and simplicity of operation.

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Section: Introductionmentioning

confidence: 99%

On-Tree Mango Fruit Size Estimation Using RGB-D Images

Wang

Walsh

Verma

2017

Sensors

139

100

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“…Bargoti and colleagues in [4] built on [27] to propose an approach that considers pixel positions, orchard row numbers and the position of the sun relative to the camera. Similarly, Cheng et al [9] proposed the use of information such as fruit number, fruit area, area of apple clusters and foliage area to improve accuracy of early yield prediction, especially in scenarios with significant occlusion. However, the inclusion of metadata is highly prone to overfitting, particularly when limited training data is available and the variability of the training set is hence low [4].…”

Section: Flower and Fruits Quantificationmentioning

confidence: 99%

Apple flower detection using deep convolutional networks

Dias

Tabb²,

Medeiros

2018

Computers in Industry

208

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To optimize fruit production, a portion of the flowers and fruitlets of apple trees must be removed early in the growing season. The proportion to be removed is determined by the bloom intensity, i.e., the number of flowers present in the orchard. Several automated computer vision systems have been proposed to estimate bloom intensity, but their overall performance is still far from satisfactory even in relatively controlled environments. With the goal of devising a technique for flower identification which is robust to clutter and to changes in illumination, this paper presents a method in which a pre-trained convolutional neural network is finetuned to become specially sensitive to flowers. Experimental results on a challenging dataset demonstrate that our method significantly outperforms three approaches that represent the state of the art in flower detection, with recall and precision rates higher than 90%. Moreover, a performance assessment on three additional datasets previously unseen by the network, which consist of different flower species and were acquired under different conditions, reveals that the proposed method highly surpasses baseline approaches in terms of generalization capability. 1 , 2 1 The citation information for this article is: P. A. Dias, A. Tabb, and H. Medeiros, Apple flower detection using deep convolutional networks,

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“…Diaz et al (2018) combined Neural Networks (NN) and a Support Vector Machine (SVM), which is a supervised classifier, to identify individual apple flowers. NN have also been used with promising results to estimate yield from mature apples (Bargoti and Underwood, 2017) and fruitlets (Cheng et al, 2017). And multispectral images including the Near InfraRed (NIR) band were used by Liakos et al (2017) and Xiao et al (2014) to derive the Normalised Difference Vegetative Index (NDVI), which was used for the detection of apple flowers.…”

Section: Introductionmentioning

confidence: 99%

Automatic Apple Tree Blossom Estimation From Uav RGB Imagery

Comas

Valente

Kooistra

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Apple trees often produce high amount of fruits, which results in small, low quality fruits. Thinning in apple orchards is used to improve the quality of the apples by reducing the number of flowers or fruits the tree is producing. The current method used to estimate how much thinning is necessary is to measure flowering intensity, currently done by human visual inspection of trees in the orchard. The use of images of apple trees from ground-level to measure flowering intensity and its spatial variation through orchards has been researched with promising results. This research explores the potential of UAV RGB high-resolution imagery to measure flowering intensity. Image segmentation techniques have been used to segment the white pixels, which correspond to the apple flowers, of the orthophoto and the single photos. Single trees have been cropped from the single photos and from the orthophoto, and correlation has been measured between percentage of white pixels per tree and flowering intensity and between percentage of white pixels per tree and flower clusters. The resulting correlation is low, with a maximum of 0.54 for the correlation between white pixels per tree and flower clusters when using the ortophoto. Those results show the complexity of working with drone images, but there are still alternative approaches that have to investigated before discarding the use of UAV RGB imagery for estimation of flowering intensity.

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

Cited by 101 publications

References 16 publications

On-Tree Mango Fruit Size Estimation Using RGB-D Images

On-Tree Mango Fruit Size Estimation Using RGB-D Images

Apple flower detection using deep convolutional networks

Automatic Apple Tree Blossom Estimation From Uav RGB Imagery

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