Performance Analysis of Fruits Classification System Using Deep Learning Techniques

Rajasekar, L.; Sharmila, D.; RameshKumar, M.R.; Yuwaraj, Balram Singh

doi:10.1007/978-981-15-5243-4_26

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…In their research, SVM (Support Vector Machine), a machine learning technique, gave the highest accuracy, but ANFIS (Adaptive Neuro Fuzzy Interference System) showed the lowest accuracy rate. Still, it is easy to implement [4].…”

Section: Introductionmentioning

confidence: 99%

Aerial object detection analysis: Challenges and preliminary results

Polina,

Suparwito,

Kumalasanti

2024

E3S Web Conf.

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Computer vision allows computers to retrieve information from images, videos, and other visual inputs. Unmanned Aerial Vehicle (UAV) technology is also used to assist computer vision in collecting image data from the air. This paper aims to perform tree object detection using UAVs by capturing images perpendicularly from above the object. Image data was collected from around Sleman Yogyakarta using DJI Pro 3 from 5 to 12 July 2023. A total of 162 images were used as a dataset. The YOLOv8n model was implemented to 162 images as the training and validation data. Next, 12 other images were used as testing data. The results showed that YOLOv8n could detect trees well from above. The confidence value of the testing dataset with the appropriate image capture is more than 80%. As a deep learning algorithm for object detection, the YOLO model can perform object detection quickly and accurately. The subsequent research will focus on analyzing the implementation of object detection using the YOLO algorithm to measure open green areas.

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Section: Introductionmentioning

confidence: 99%

Aerial object detection analysis: Challenges and preliminary results

Polina,

Suparwito,

Kumalasanti

2024

E3S Web Conf.

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“…Palleja et al [4] developed a spraying robot for vineyards and apple orchards, which formed an array by combining multiple ultrasonic sensors, and realized real-time estimation of fruit tree canopy density facing the canopy plane, providing data support for the spraying system; Dairath [5] and others have developed a robot picking and grading prototype system which integrates robot picking and grading process based on fruit quality identification. Rajasekar [6] and others use deep learning technology and OpenCV library to realize real-time flow detection of fruits; Diego [7] and others used the Open CV library in Python and Otsu technology to extract the pixel area of the fruit image to identify the mature state of passion fruit. Therefore, we use the HSV method to study how to recognize citrus objects in complex situations.…”

Section: Introductionmentioning

confidence: 99%

Citrus objects recognition in orchard scene using HSV methods

Hua,

Wu,

Liang

et al. 2024

International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024)

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There is a problem that it is always difficult for picking and spraying robots to identify the objects for such factors such as leaf occlusion, similar color and others. In this paper, based on the citrus image data set collected from the actual orchard scene, image features are extracted and a mathematical model is constructed to identify and count the number of citruses in each image, and the distribution of apples in the whole data set is displayed. A model is constructed to be used to identify and count citrus via these images with diversity of citrus colors and the complexity of the background. Firstly, the yellow mature citruses are focused on using the HSV (Hue, Saturation, Value) method for the data sets. Secondly, the empirical HSV range for two citrus fruits, cyan and yellow, was defined, and contour processing is introduced to optimize the recognition model to process more citrus image datasets. Finally, such three types of objects as immature citrus, leaves with similar colors, and citrus covered by leaves are distinguish. The research results indicate that we adopt more detailed color threshold settings and morphological operations with the eccentricity of the contour is less than 0.85, and can distinguish citrus and leaves through image segmentation,and the recognition effect on citrus objects is improved.

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Performance Analysis of Fruits Classification System Using Deep Learning Techniques

Cited by 2 publications

References 12 publications

Aerial object detection analysis: Challenges and preliminary results

Aerial object detection analysis: Challenges and preliminary results

Citrus objects recognition in orchard scene using HSV methods

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