Automatic fruit recognition and counting from multiple images

Shen, Yu; Glasbey, C. A.; Horgan, Graham; Polder, G.; Dieleman, J.A.; Heijden, G.W.A.M. van der

doi:10.1016/j.biosystemseng.2013.12.008

Cited by 152 publications

(77 citation statements)

References 17 publications

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“…This information can be obtained using different means ranging from human visual inspection to satellite images and locally taken pictures, for example, images taken from the crop and then used for plantation production forecasting. Indeed, several techniques such as pictures acquired by digital cameras and multispectral images have already been used for this purpose [4][5][6][7][8][9][10]. For instance, [11] has used red/green/blue (RGB) digital cameras and machine learning techniques to identify tomato fruits in their several development stages.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis between LDR and HDR Images for Automatic Fruit Recognition and Counting

et al. 2017

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Precision agriculture is gaining an increasing interest in the current farming paradigm. This new production concept relies on the use of information technology (IT) to provide a control and supervising structure that can lead to better management policies. In this framework, imaging techniques that provide visual information over the farming area play an important role in production status monitoring. As such, accurate representation of the gathered production images is a major concern, especially if those images are used in detection and classification tasks. Real scenes, observed in natural environment, present high dynamic ranges that cannot be represented by the common LDR (Low Dynamic Range) devices. However, this issue can be handled by High Dynamic Range (HDR) images since they have the ability to store luminance information similarly to the human visual system. In order to prove their advantage in image processing, a comparative analysis between LDR and HDR images, for fruits detection and counting, was carried out. The obtained results show that the use of HDR images improves the detection performance to more than 30% when compared to LDR.

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Section: Introductionmentioning

confidence: 99%

Comparative Analysis between LDR and HDR Images for Automatic Fruit Recognition and Counting

et al. 2017

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“…The apple harvesting robot has also reached a high stage of development [1][2][3]. In China, the apple planting acreage is large, the yield is high and the rural labour force is decreasing, so the harvesting schedule is tight.…”

Section: Introductionmentioning

confidence: 99%

Night Vision Image De-noising of Apple Harvesting Robots Based on the Wavelet Fuzzy Threshold

Ruan

Zhao

Jia

et al. 2015

Int. j. adv. robot. syst.

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In this paper, the de-noising problem of night vision images is studied for apple harvesting robots working at night. The wavelet threshold method is applied to the de-noising of night vision images. Due to the fact that the choice of wavelet threshold function restricts the effect of the wavelet threshold method, the fuzzy theory is introduced to construct the fuzzy threshold function. We then propose the de-noising algorithm based on the wavelet fuzzy threshold. This new method can reduce image noise interferences, which is conducive to further image segmentation and recognition. To demonstrate the performance of the proposed method, we conducted simulation experiments and compared the median filtering and the wavelet soft threshold de-noising methods. It is shown that this new method can achieve the highest relative PSNR. Compared with the original images, the median filtering de-noising method and the classical wavelet threshold de-noising method, the relative PSNR increases 24.86%, 13.95%, and 11.38% respectively. We carry out comparisons from various aspects, such as intuitive visual evaluation, objective data evaluation, edge evaluation and artificial light evaluation. The experimental results show that the proposed method has unique advantages for the denoising of night vision images, which lay the foundation for apple harvesting robots working at night.

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“…Y. Song et al [1] have presented automatic fruit recognition and counting from multiple fruit images. In this case feature extraction using (MSCR, BOW model) in frequency domain and SVM classifier is the major image processing method.…”

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confidence: 99%

On Tree Detection, Counting & Post- Harvest grading of fruits Based on Image Processing and Machine Learning Approach-A Review

Sethy¹,

Panda²,

Behera³

et al. 2017

IJET

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Abstract-This paper reports involvement of image processing and machine vision technique to detect and count of fruits on-tree, in field condition, have been reviewed. In addition, this paper also associated with the grading of fruits in post-harvesting. Different types of algorithms are available for counting and to extract the feature of fruit characters by capturing the on-tree fruit image by any conventional RGB camera. With the help of this counting algorithm and feature extraction technique, fruit is detected and counted. This work also surveys grading method applied to the post-harvest fruits. Grading method involves: identification of mature & immature fruits, intact & diseased fruits and also predict the weight of the fruit from its shape. The grading of fruit can be done by using different types of the classifier. The main features, drawback and future prospective of previous work in this area are summarized.

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Automatic fruit recognition and counting from multiple images

Cited by 152 publications

References 17 publications

Comparative Analysis between LDR and HDR Images for Automatic Fruit Recognition and Counting

Comparative Analysis between LDR and HDR Images for Automatic Fruit Recognition and Counting

Night Vision Image De-noising of Apple Harvesting Robots Based on the Wavelet Fuzzy Threshold

On Tree Detection, Counting & Post- Harvest grading of fruits Based on Image Processing and Machine Learning Approach-A Review

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