A Novel Framework Based on Deep Learning and Unmanned Aerial Vehicles to Assess the Quality of Rice Fields

Tri, Nguyen Cao; Hoài, Trần Văn; Duong, Hieu N.; Trong, Nguyen Thanh; Vinh, Vo Van; Snášel, Václav

doi:10.1007/978-3-319-49073-1_11

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Besides traditional fruit or crop classification, some tasks can also be transformed into classification tasks in analyzing dense scenes in agriculture, such as quality assessment, fine-grained classification and so on. In the study of [85], the evaluation of paddy field quality was transformed into the classification of paddy field density, including sparse density and normal density. Different from traditional disease classification, there are larger intra-class similarities and smaller inter-class variance [86].…”

Section: Recognition and Classificationmentioning

confidence: 99%

Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Zhang

Liu

Gong

et al. 2020

Sensors

126

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Deep Learning (DL) is the state-of-the-art machine learning technology, which shows superior performance in computer vision, bioinformatics, natural language processing, and other areas. Especially as a modern image processing technology, DL has been successfully applied in various tasks, such as object detection, semantic segmentation, and scene analysis. However, with the increase of dense scenes in reality, due to severe occlusions, and small size of objects, the analysis of dense scenes becomes particularly challenging. To overcome these problems, DL recently has been increasingly applied to dense scenes and has begun to be used in dense agricultural scenes. The purpose of this review is to explore the applications of DL for dense scenes analysis in agriculture. In order to better elaborate the topic, we first describe the types of dense scenes in agriculture, as well as the challenges. Next, we introduce various popular deep neural networks used in these dense scenes. Then, the applications of these structures in various agricultural tasks are comprehensively introduced in this review, including recognition and classification, detection, counting and yield estimation. Finally, the surveyed DL applications, limitations and the future work for analysis of dense images in agriculture are summarized.

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Section: Recognition and Classificationmentioning

confidence: 99%

Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Zhang

Liu

Gong

et al. 2020

Sensors

126

View full text Add to dashboard Cite

show abstract

“…Additionally, UAVs allow to have a full view of the field quickly, speeding up the process of measurement and analysis of the crop variables. In the work presented in [12], authors present a method to classify rice quality, based on high resolution images taken by a UAV flying at low altitudes.…”

Section: Related Workmentioning

confidence: 99%

Towards Image Mosaicking with Aerial Images for Monitoring Rice Crops

Rojas

Martínez

Mondragón

et al. 2017

Advances in Automation and Robotics Research in Latin America

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Around 8 to 10 million Ton of rice are required in the following years to be able to supply the demand of the overall population. Analysis and monitoring of rice crops becomes nowadays very important issue for farmers, for ensuring a rice production level to cope this demand. This paper presents simulation results of an algorithm that allows to plan and create 2D maps using the technique of image mosaicking with multiple geo-referenced aerial images (multispectral images in the scope of the project). The planning algorithm is called Image Capture algorithm. It takes into account the area the UAV has to cover, the camera configuration, and the state of the UAV in order to define where to take the pictures to build the mosaic. The algorithm presented in this paper was developed in ROS (Indigo) and simulated in Gazebo. The results of this first approach to the 2D mapping of a rice crop allows to see that using the proposed algorithm, it is possible to automate the process of acquiring the pictures for creating the mosaic, ensuring that all the area of interest is covered. By using this algorithm, pictures will be acquired only in specific areas. Therefore, keeping the storage capacity on-board, under control.

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Fruits diseases classification: exploiting a hierarchical framework for deep features fusion and selection

Khan

Akram

Sharif

et al. 2020

Multimed Tools Appl

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A Novel Framework Based on Deep Learning and Unmanned Aerial Vehicles to Assess the Quality of Rice Fields

Cited by 7 publications

References 10 publications

Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Towards Image Mosaicking with Aerial Images for Monitoring Rice Crops

Fruits diseases classification: exploiting a hierarchical framework for deep features fusion and selection

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