Fast and Accurate Detection of Banana Fruits in Complex Background Orchards

Fu, Lanhui; Duan, Jieli; Zou, Xiangjun; Lin, Jiaquan; Zhao, Lei; Li, Jinhui; Yang, Zhou

doi:10.1109/access.2020.3029215

Cited by 48 publications

(37 citation statements)

References 52 publications

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“…Recent years have seen an unprecedented rise in the cost of human labor, with the increase reaching up to 12–15% in 2019 ( Fu et al, 2020 ). At present, banana buds are generally cut and picked manually, and the labor cost accounts for approximately 34–40% of the total cost of banana production.…”

Section: Introductionmentioning

confidence: 99%

“…A multi-view 3D perception of the center of the fruit axis of bananas in a complex orchard environment was conducted ( Chen et al, 2020 ). A YOLOv4 neural network was then used to extract deep-level features from the banana fruits, thus realizing the accurate detection of bananas of varying sizes ( Fu et al, 2020 ). Determining the cutting points in the banana flower buds and inflorescence axes is a necessary prerequisite for decision-making in bud-cutting robots.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Target Recognition of Bananas and Automatic Positioning for the Inflorescence Axis Cutting Point

Duan

Chen

et al. 2021

Front. Plant Sci.

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Multi-target recognition and positioning using robots in orchards is a challenging task in modern precision agriculture owing to the presence of complex noise disturbance, including wind disturbance, changing illumination, and branch and leaf shading. To obtain the target information for a bud-cutting robotic operation, we employed a modified deep learning algorithm for the fast and precise recognition of banana fruits, inflorescence axes, and flower buds. Thus, the cutting point on the inflorescence axis was identified using an edge detection algorithm and geometric calculation. We proposed a modified YOLOv3 model based on clustering optimization and clarified the influence of front-lighting and backlighting on the model. Image segmentation and denoising were performed to obtain the edge images of the flower buds and inflorescence axes. The spatial geometry model was constructed on this basis. The center of symmetry and centroid were calculated for the edges of the flower buds. The equation for the position of the inflorescence axis was established, and the cutting point was determined. Experimental results showed that the modified YOLOv3 model based on clustering optimization showed excellent performance with good balance between speed and precision both under front-lighting and backlighting conditions. The total pixel positioning error between the calculated and manually determined optimal cutting point in the flower bud was 4 and 5 pixels under the front-lighting and backlighting conditions, respectively. The percentage of images that met the positioning requirements was 93 and 90%, respectively. The results indicate that the new method can satisfy the real-time operating requirements for the banana bud-cutting robot.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Target Recognition of Bananas and Automatic Positioning for the Inflorescence Axis Cutting Point

Duan

Chen

et al. 2021

Front. Plant Sci.

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“…). Fu et al [89] utilized the YOLO V4 model to quickly and accurately recognize banana fruits in large backdrop orchards.…”

Section: You Look Only Once (Yolo V4mentioning

confidence: 99%

Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques

Ukwuoma

Qin

Heyat

et al. 2022

Mathematical Problems in Engineering

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Recent advances in computer vision have allowed broad applications in every area of life, and agriculture is not left out. For the agri-food industry, the use of advanced technology is essential. Owing to deep learning’s capability to learn robust features from images, it has witnessed enormous application in several fields. Fruit detection and classification remains challenging due to the form, color, and texture of different fruit species. While studying the impact of computer vision on fruit detection and classification, we pointed out that till 2018 many conventional machine learning methods were utilized while a few methods exploited the application of deep learning methods for fruit detection and classification. This has prompted us to pursue an extensive study on surveying and implementing deep learning models for fruit detection and classification. In this article, we intensively discussed the datasets used by many scholars, the practical descriptors, the model’s implementation, and the challenges of using deep learning to detect and categorize fruits. Lastly, we summarized the results of different deep learning methods applied in previous studies for the purpose of fruit detection and classification. This review covers the study of recently published articles that utilized deep learning models for fruit identification and classification. Additionally, we also implemented from scratch a deep learning model for fruit classification using the popular dataset “Fruit 360” to make it easier for beginner researchers in the field of agriculture to understand the role of deep learning in the agriculture domain.

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“…Their system had an AP of 96.28 % and a detection speed of 106 FPS on the high-powered Tesla V100 GPU. Other studies also evaluated the performance of YOLO-based models on detecting other fruits such as apple, lemon, banana and cherry [ 8 , 11 , 12 , 13 , 15 , 16 , 32 ].…”

Section: Related Workmentioning

confidence: 99%

“…Several studies have utilized YOLO-based models for fruit detection and have demonstrated that YOLO models have a huge potential in accurate real time detection of fruits in an orchard [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. However, there were some concerns found among these studies.…”

Section: Introductionmentioning

confidence: 99%

Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT

Parico

Ahamed

2021

Sensors

119

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This study aimed to produce a robust real-time pear fruit counter for mobile applications using only RGB data, the variants of the state-of-the-art object detection model YOLOv4, and the multiple object-tracking algorithm Deep SORT. This study also provided a systematic and pragmatic methodology for choosing the most suitable model for a desired application in agricultural sciences. In terms of accuracy, YOLOv4-CSP was observed as the optimal model, with an AP@0.50 of 98%. In terms of speed and computational cost, YOLOv4-tiny was found to be the ideal model, with a speed of more than 50 FPS and FLOPS of 6.8–14.5. If considering the balance in terms of accuracy, speed and computational cost, YOLOv4 was found to be most suitable and had the highest accuracy metrics while satisfying a real time speed of greater than or equal to 24 FPS. Between the two methods of counting with Deep SORT, the unique ID method was found to be more reliable, with an F1count of 87.85%. This was because YOLOv4 had a very low false negative in detecting pear fruits. The ROI line is more reliable because of its more restrictive nature, but due to flickering in detection it was not able to count some pears despite their being detected.

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Fast and Accurate Detection of Banana Fruits in Complex Background Orchards

Cited by 48 publications

References 52 publications

Multi-Target Recognition of Bananas and Automatic Positioning for the Inflorescence Axis Cutting Point

Multi-Target Recognition of Bananas and Automatic Positioning for the Inflorescence Axis Cutting Point

Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques

Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT

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