Banana detection based on color and texture features in the natural environment

Fu, Lanhui; Duan, Jieli; Zou, Xiangjun; Lin, Guimin; Song, Shuaishuai; Ji, Bang; Yang, Zhou

doi:10.1016/j.compag.2019.105057

Cited by 53 publications

(34 citation statements)

References 49 publications

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“…Due to the large size of bananas and the distance between banana plants, it is very rare for more than three hands of bananas to appear in the same image. It's easy to see that each banana in the images was accurately detected under different illumination conditions, which was different from the detection result in reference [60]. This is because the machine learning algorithm is easily affected by the illumination, while the deep learning algorithm has stronger robustness to the environmental conditions.…”

Section: B Detection Resultsmentioning

confidence: 78%

“…In our early work [60], we demonstrated that using traditional machine learning algorithm SVM classifier with color and texture features can achieve impressive results in banana detection. However, early work focused on detecting orchard bananas of the same variety for CPU processing.…”

Section: Research Progress Of Our Topicmentioning

confidence: 99%

“…We compared the banana detection results of YOLOv4, YOLOv3, and HOG+LBP+SVM algorithms. The detection results of the three algorithms in different conditions have been described in detail in the above and literature [60], The problems encountered in the machine learning algorithm are described below. In literature [60], when the key parts of the banana are covered, the banana was mistaken as two hands of bananas.…”

Section: B Comparison Between Deep Learning Algorithm and Machine Lementioning

confidence: 99%

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Fast and Accurate Detection of Banana Fruits in Complex Background Orchards

Duan

Zou

et al. 2020

IEEE Access

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The detection of banana fruits is an important part of intelligent management in the banana plantation. To detect the banana fruit quickly and accurately in the complex orchard environment, this paper proposes a method based on the latest deep learning algorithm to detect the banana fruit. Using a monocular camera, we applied the YOLOv4 neural network algorithm to extract the deep features of banana fruits, realizing accurate detection of different banana sizes. The detection algorithm achieved a 99.29% detection rate, the average execution time was 0.171s, the shortest execution time was 0.135s, and the AP was 0.9995. Moreover, the detection results were discussed with the YOLOv3 algorithm and the machine learning algorithm. Compared with the machine learning algorithm, deep learning algorithm was superior to both detection accuracy and detection time. YOLOv4 had higher detection confidence and higher detection rate than YOLOv3. The results show that the proposed method could realize the fast detection of different varieties and different maturity in banana plantations, under different illumination and occlusion conditions, and provide information for banana picking, maturity and yield estimation. INDEX TERMS Banana detection, orchard environment, deep learning, green fruit, YOLOv4. L.Fu et al.: Fast and Accurate Detection of Banana Fruits in Complex Background Orchards L.Fu et al.: Fast and Accurate Detection of Banana Fruits in Complex Background Orchards

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Section: B Detection Resultsmentioning

confidence: 78%

Section: Research Progress Of Our Topicmentioning

confidence: 99%

Section: B Comparison Between Deep Learning Algorithm and Machine Lementioning

confidence: 99%

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Fast and Accurate Detection of Banana Fruits in Complex Background Orchards

Duan

Zou

et al. 2020

IEEE Access

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“…HMI segmentation from a 2D RGB camera image is simplified by the fact that the left and right HMIs have distinctive colours (it additionally greatly simplifies the correct determination of the side of the hand at different view angles). Colour-based segmentation is used as a simple alternative to the complex task of segmenting a 3D object in space, which otherwise would require the application of machine learning approaches such as SVM [ 28 ], deep learning-based object recognition [ 29 ], and image segmentation [ 30 ]. An extensive review of object localisation methods is demonstrated in the work of Y. Tang et al [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

Improved Mutual Understanding for Human-Robot Collaboration: Combining Human-Aware Motion Planning with Haptic Feedback Devices for Communicating Planned Trajectory

Grushko

Vysocký

Oščádal

et al. 2021

Sensors

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In a collaborative scenario, the communication between humans and robots is a fundamental aspect to achieve good efficiency and ergonomics in the task execution. A lot of research has been made related to enabling a robot system to understand and predict human behaviour, allowing the robot to adapt its motion to avoid collisions with human workers. Assuming the production task has a high degree of variability, the robot’s movements can be difficult to predict, leading to a feeling of anxiety in the worker when the robot changes its trajectory and approaches since the worker has no information about the planned movement of the robot. Additionally, without information about the robot’s movement, the human worker cannot effectively plan own activity without forcing the robot to constantly replan its movement. We propose a novel approach to communicating the robot’s intentions to a human worker. The improvement to the collaboration is presented by introducing haptic feedback devices, whose task is to notify the human worker about the currently planned robot’s trajectory and changes in its status. In order to verify the effectiveness of the developed human-machine interface in the conditions of a shared collaborative workspace, a user study was designed and conducted among 16 participants, whose objective was to accurately recognise the goal position of the robot during its movement. Data collected during the experiment included both objective and subjective parameters. Statistically significant results of the experiment indicated that all the participants could improve their task completion time by over 45% and generally were more subjectively satisfied when completing the task with equipped haptic feedback devices. The results also suggest the usefulness of the developed notification system since it improved users’ awareness about the motion plan of the robot.

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“…Electronic Eyes have proven advantageous in various foodstuff areas, such as process monitoring, quality control, freshness assessment, shelf life investigation, and authenticity assessment. Over the last few years, it has been possible to find applications related to the evaluation of the quality in alcoholic beverages [12,13], fruit ripening analysis [14][15][16], vegetables [17,18], cereals [19,20], meat products [21,22], fish and seafood [23][24][25], coffee [26,27], tea [28,29], olive oil [30,31], and others [32,33].…”

Section: Introductionmentioning

confidence: 99%

Electronic Eye for Identification of Tequila Samples

Gómez

Bueno

Gutiérrez

2020

The 1st International Electronic Conference on Biosensors

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The present work reports the potential of a bio-inspired system based on spectrometry, also known as Electronic Eye (EE), capable of detecting different Tequila samples. The reported system analyzes small volumes of Tequila Reposado and Blanco by calculating samples’ absorbances, using a low cost and portable instrumentation employing a CCD camera. The absorbance imaging method consisted of exciting samples with light passes through an 8MP camera connected to a Raspberry Pi Card. The camera’s image data are analyzed using MATLAB 2018b to be represented in Red, Green and Blue (RGB) components for each pixel, in order to get an approximation of the absorbance and the Surface Color Index (Isc) associated with sample concentration. Using the developed EE, it was possible to identify seven different kinds and brands of Tequila. From the obtained results, it was observed that the average absorbance of the Tequila Reposado was greater than the absorbance of the Tequila Blanco. Otherwise, with the Isc, the Tequila Blanco color index is lower concerning the Tequila Reposado’s. Finally, the EE allowed the identification of Tequila samples with reproducibility and repeatability.

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Banana detection based on color and texture features in the natural environment

Cited by 53 publications

References 49 publications

Fast and Accurate Detection of Banana Fruits in Complex Background Orchards

Fast and Accurate Detection of Banana Fruits in Complex Background Orchards

Improved Mutual Understanding for Human-Robot Collaboration: Combining Human-Aware Motion Planning with Haptic Feedback Devices for Communicating Planned Trajectory

Electronic Eye for Identification of Tequila Samples

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