Immature green citrus fruit detection and counting based on fast normalized cross correlation (FNCC) using natural outdoor colour images

Han, Ling; Lee, Won Suk; Ku, Wang

doi:10.1007/s11119-016-9443-z

Cited by 47 publications

(15 citation statements)

References 18 publications

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“…Patel et al [15] developed an algorithm for fruit detection based on multiple features; different weights were assigned to different image features such as intensity, color, orientation and edge. Li et al [16] developed a method for detecting and counting immature green citrus fruits using outdoor color images as part of the development of an early yield mapping system; multiple features, including color, shape and texture, were combined to remove false positives. Seng and Mirisaee [17] proposed a recognition approach that combined color-based, shape-based and size-based methods and used a nearest neighbor model to classify fruit pixels.…”

Section: Introduction a Backgroundmentioning

confidence: 99%

Automatic Detection of Single Ripe Tomato on Plant Combining Faster R-CNN and Intuitionistic Fuzzy Set

Liu

Zhou

et al. 2019

IEEE Access

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Fast and accurate detection of ripe tomatoes on plant, which replaces manual labor with a robotic vision-based harvesting system, is a challenging task. Tomatoes in adjacent positions are easily mistaken as a single tomato by image recognition methods. In this study, a ripe tomato detection method that combines deep learning with edge contour detection is proposed. Our approach efficiently separates target tomatoes from overlapping tomatoes to detect individual fruits. This approach yields several improvements. First, deep learning requires less time and extracts deeper features than traditional methods for assessing candidate ripe tomato regions. Second, we use Gaussian density function of H and S in the HSV color space to help segment tomato regions from the background, followed by erosion and dilation on the tomato body to separate adjacent tomatoes and remove peripheral subpixels from all detected ripe tomatoes. Third, an adaptive threshold intuitionistic fuzzy set (IFS) method was developed to identify the tomato's edge, and it performs well in detecting blurred edges in overlapping regions. To improve the efficiency and stability of edge detection under natural conditions, we adopted an illumination adjustment algorithm for the tomato image before edge detection. As samples, we collected images showing tomatoes that were separated, adjacent, overlapped, and even shaded by leaves. The widths and heights of these tomato samples were calculated and analyzed to evaluate the detection performance of the proposed method. The root mean square error (RMSE) results for tomato width and height using the proposed method are 2.996 pixels and 3.306 pixels, respectively. The mean relative error percent (MRE%) values for horizontal and vertical center position shift are 0.261% and 1.179%, respectively. These results demonstrate that the proposed method improves tomato detection accuracy and that it can be further applied in the harvesting process of agricultural robots. INDEX TERMS Tomato detection, deep learning, background subtraction, intuitionistic fuzzy set theory (IFS), contour segmentation. XUAN LIU received the B.S. degree in electrical engineering and automation, Nanjing Forestry University, China, in 2018, where he is currently pursuing the M.S. degree in instrument engineering. His research interests include computer vision and machine learning. ZHOU PAN received the B.S. degree in electrical engineering and automation from the Huaian Institute of Technology, China, in 2016. She is currently pursuing the M.S. degree in control theory and control engineering with Nanjing Forestry University, China. Her research interests include computer vision and machine learning.

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Section: Introduction a Backgroundmentioning

confidence: 99%

Automatic Detection of Single Ripe Tomato on Plant Combining Faster R-CNN and Intuitionistic Fuzzy Set

Liu

Zhou

et al. 2019

IEEE Access

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“…In computer vision, the image histogram can be epitomized graphically with pixels plotted through tonal variations for images to analyze the peaks and valleys and consequently to uncover the threshold value. The histograms applied for color spaces assist in background removal to improve the efficiency as well as accuracy [69]. The optimal threshold value can be resolved automatically with the unimodal attributes generated by the grey-level histogram of the luminance designed for natural images.…”

Section: Histogram Equalizationmentioning

confidence: 99%

“…Considering only the color features may lead to many false positives due to the similarity of the green colors of fruits and leaves. A circular Hough transform can identify the circular citrus fruits by merging multiple detections along with the histograms of H, R, B components [69]. The calibration measurements with destructive hand samples by the time of imaging provide accurate prediction of vine yields [83].…”

Section: Segmentation Based On Shapesmentioning

confidence: 99%

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

et al. 2021

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Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.

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“…Furthermore, some special algorithms for fast NCC have been presented [12,13]. The fast cross-correlation of binary sequences can be extended to other types of NCC sequences [14].…”

Section: Introductionmentioning

confidence: 99%

The Algorithm and Structure for Digital Normalized Cross-Correlation by Using First-Order Moment

Pan

Xia

et al. 2020

Sensors

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Normalized cross-correlation is an important mathematical tool in digital signal processing. This paper presents a new algorithm and its systolic structure for digital normalized cross-correlation, based on the statistical characteristic of inner-product. We first introduce a relationship between the inner-product in cross-correlation and a first-order moment. Then digital normalized cross-correlation is transformed into a new calculation formula that mainly includes a first-order moment. Finally, by using a fast algorithm for first-order moment, we can compute the first-order moment in this new formula rapidly, and thus develop a fast algorithm for normalized cross-correlation, which contributes to that arbitrary-length digital normalized cross-correlation being performed by a simple procedure and less multiplications. Furthermore, as the algorithm for the first-order moment can be implemented by systolic structure, we design a systolic array for normalized cross-correlation with a seldom multiplier, in order for its fast hardware implementation. The proposed algorithm and systolic array are also improved for reducing their addition complexity. The comparisons with some algorithms and structures have shown the performance of the proposed method.

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Immature green citrus fruit detection and counting based on fast normalized cross correlation (FNCC) using natural outdoor colour images

Cited by 47 publications

References 18 publications

Automatic Detection of Single Ripe Tomato on Plant Combining Faster R-CNN and Intuitionistic Fuzzy Set

Automatic Detection of Single Ripe Tomato on Plant Combining Faster R-CNN and Intuitionistic Fuzzy Set

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

The Algorithm and Structure for Digital Normalized Cross-Correlation by Using First-Order Moment

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