A fruit detection algorithm based on R-FCN in natural scene

Liu, Jian; Mingrui, Zhao; Guo, Xifeng

doi:10.1109/ccdc49329.2020.9163826

Cited by 9 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The input consists of authentic data provided by fruit producers, which includes a total of 700 photographs of mangoes and 700 photographs of pitaya fruits. In their publication, Jian et al [20] introduced an algorithm for the optical detecting system used in agricultural robots for fruit harvesting. This algorithm is capable of identifying and locating different types of fruits in diverse environments.…”

Section: Related Workmentioning

confidence: 99%

A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector

Che Ani,

Hamdani Mohammad Farid,

Firdhaus Kamaruzaman

et al. 2024

Bulletin EEI

View full text Add to dashboard Cite

The image datasets that are most widely used for training deep learning models are specifically developed for applications. This study introduces a novel dataset aimed at augmenting the existing data for the identification of figs in their natural habitats, specifically in the wilderness. In the present study, researchers have generated numerous image datasets specifically for object detection focus on applications in agriculture. Regrettably, it is exceedingly difficult for us to obtain a specialized dataset specifically designed for detecting figs. To tackle this issue, a grand total of 462 photographs of fig fruits were gathered. The augmentation technique was utilized to substantially increase the size of the dataset. Ultimately, we conduct an examination of the dataset by doing a baseline performance study for bounding-box detection using established object detection methods, specifically you only look once (YOLO) version 3 and YOLOv4. The performance obtained on the test photos of our dataset is satisfactory. For farmers, the capacity to identify and oversee fig fruits in their natural or developed environments can be highly advantageous. The detecting device offers instantaneous data regarding the quantity of mature figs, facilitating decision-making procedures.

show abstract

Section: Related Workmentioning

confidence: 99%

A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector

Che Ani,

Hamdani Mohammad Farid,

Firdhaus Kamaruzaman

et al. 2024

Bulletin EEI

View full text Add to dashboard Cite

show abstract

“…There were 14 studies based on tables that used public datasets. Typically, public datasets may be found in the MS COCO dataset, which contains about 330 thousand photos and 80 object categories [13], [56], ImageNet is a database of over a million photos and one thousand different kinds of objects [43] and the Kaggle dataset [57], which has been collected manually by the researcher and published on it. Besides, Roboflow was also one of the public datasets that new researchers or other users could access.…”

Section: Dataset Preparationmentioning

confidence: 99%

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

et al. 2023

View full text Add to dashboard Cite

Deep learning has shown much promise in target identification in recent years, and it's becoming more popular in agriculture, where fig fruit detection and counting have become important. In this study, a systematic literature review (SLR) is utilised to evaluate a deep learning algorithm for detecting and counting fig fruits. The SLR is based on the widely used 'Reporting Standards for Systematic Evidence Synthetics' (ROSES) review process. The study starts by formulating the research questions, and the proposed SLR approach is critically discussed until the data abstraction and analysis process is completed. Following that, 33 relevant research involving the agriculture sector, fruit, were selected from many studies. IEEE, Scopus, and Web of Sciences are three databases to investigate. Due to the lack of fig fruit research, fruit and vegetable studies have been included because they use similar methods and processes. The SLR found that various deep learning algorithms can count fig fruit in the field. Furthermore, as most approaches obtained acceptable results, deep learning's performance is acceptable in F1-score and average precision (AP), higher than 80%. Moreover, improvements can be produced by enhancing the existing deep learning model with the personal dataset.

show abstract

“…It can automatically learn multi-dimensional feature information with a distinguishing degree between crops and weeds from input images. Owing to its advantages, CNNs are also widely used in the agricultural field to solve various practical problems including weed identification [30], [31], [32], [33]. Zou et al [34] proposed a U-Net variant network for segmenting wheat and weeds on digital images.…”

Section: Introductionmentioning

confidence: 99%

An Improved Transformer Network With Multi-Scale Convolution for Weed Identification in Sugarcane Field

Sun,

Zhang,

Zhou

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Automated weeding equipment is urgently needed to deal with weeds in farmlands in the context of the rapid development of Intelligent Agriculture. A system for accurately identifying crops and weeds in images is crucial component of automated weeding equipment. However, in the field environment, crops and weeds grow intertwined, and weeds are very similar to sugarcane leaves, making it difficult to accurately segment crops, weeds, and their boundaries from images. In this paper, we proposed a novel network that fully utilizes low-level semantic information to accurately segment crops and weeds in images, improving the accuracy of crop and weed segmentation while reducing the need for training weight parameters and improving speed in the prediction stage. Specifically, we made three important modifications for crop and weed identification. First, a Multi-scale Feature Extraction and Fusion module (MFEF) was designed to capture abundant low-level semantic feature information. Afterward, we introduce a Global Response Normalization (GRN) block to select more useful feature information. Finally, a series of residual attention transformer layers are designed to transmit the long-range dependency information extracted between layers. Numerous experimental results confirmed that our proposed network achieved excellent performance in segmenting sugarcane and weed images. Specifically, (1) the mean accuracy and, Mean Intersection of Union (MIoU) reached 96.97% and 94.13%, respectively, (2) the training parameters of the model have been reduced by more than 25%, improving the Frames Per Second (FPS) value of the prediction process, and (3) is also effective on the publicly available BoniRob Dataset, indicating that the proposed model has considerable generalization ability. This study provides an accurate weed identification map and has reference significance for subsequent systems as mechanical weeding equipment.

show abstract

A fruit detection algorithm based on R-FCN in natural scene

Cited by 9 publications

References 12 publications

A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector

A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

An Improved Transformer Network With Multi-Scale Convolution for Weed Identification in Sugarcane Field

Contact Info

Product

Resources

About