ApLeaf: An efficient android-based plant leaf identification system

Zhao, Zhuo; Lin, Hai; Cheung, Yiu-ming; Wu, Xindong; Tang, Yuanyan; Chen, C. L. Philip

doi:10.1016/j.neucom.2014.02.077

Cited by 70 publications

(38 citation statements)

References 14 publications

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“…At the same time, it is necessary to combine DL algorithms with modern technologies such as meteorological information and geographic information to form a comprehensive service platform. By considering the portability of mobile platforms such as Android or ios [122], it can be applied in smart mobile terminals to provide higher practical and promotional value [63].…”

Section: (4) Robot Systemmentioning

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: (4) Robot Systemmentioning

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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“…Zhao et al [11] employ the same general approach as pictures have to be taken on a bright background. For classification a variation of the established HOG (Histogram of Oriented Gradient) features is combined with colour features using the HSV picture representation and wavelet features.…”

Section: Related Workmentioning

confidence: 99%

“…Many systems were proposed, mainly to test and compare different approaches, feature classes and classifiers [1][2][3][4][5][6][7][8][9]. Furthermore, several mobile applications like Leafsnap [10] for iOS or ApLeaf [11] for Android were developed. They allow quick classification by taking a photo of a leaf on a bright background like a piece of paper.…”

Section: Introductionmentioning

confidence: 99%

Cross Dataset Evaluation of Feature Extraction Techniques for Leaf Classification

Reul¹,

Toepfer²,

Puppe³

2016

IJAIA

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“…The Android mobile phone platform provides third-party developers with a wide ranging, free environment, allowing users to capture and process images wherever necessary. A number of applications on mobile devices have been developed for agricultural applications, such as grain phenotyping [18], estimating citrus yield [19], identifying plant species [20], weed identification [21], estimating soil-color [22] and so on.…”

Section: Introductionmentioning

confidence: 99%

GainTKW: A Measurement System of Thousand Kernel Weight Based on the Android Platform

Zhou

Chen

et al. 2018

Agronomy

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Thousand kernel weight (TKW) is an important parameter for the evaluation of grain yield. The traditional measurement method relies on manual steps: weighing and counting. In this paper, we developed a system for the automated evaluation of thousand kernel weight that combines a weighing module and Android devices, called “gainTKW”. The system is able to collect the weight information from the weighing module through a serial port using the RS232-micro USB cable. In the imaging process, we adopt a k-means clustering segmentation algorithm to solve the problem of uneven lighting. We used the marker-controlled watershed algorithm and area threshold method to count the number of kernels that are touching one another. These algorithms were implemented based on the OpenCV (Open Source Computer Vision) libraries. The system tested kernel images of six species taken with the Android device under different lighting conditions. The algorithms in this study can solve the segmentation problems caused by shadows, as well. The appropriate numbers of kernels, of different species, are counted with an error ratio upper limit of 3%. The application is convenient and easy to operate. For the experiments, we can prove the efficiency and accuracy of the developed system by comparing the results between the manual method and the proposed application.

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ApLeaf: An efficient android-based plant leaf identification system

Cited by 70 publications

References 14 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

Cross Dataset Evaluation of Feature Extraction Techniques for Leaf Classification

GainTKW: A Measurement System of Thousand Kernel Weight Based on the Android Platform

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