A Plant Leaf Geometric Parameter Measurement System Based on the Android Platform

Liu, Haiyun; Xu, Ma; Tao, Ming; Deng, Ruoling; Bangura, Kemoh; Deng, Xiangwu; Liu, Chuang; Qi, Long

doi:10.3390/s19081872

Cited by 23 publications

(21 citation statements)

References 26 publications

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“…e technical system of the application program is basically composed of two parts. One is the underlying part composed of the TensorFlow Lite development interface, and the other is the Android application layer composed of the Android native development interface (API) [41,42]. TensorFlow Lite can deploy the CNN model trained under the TensorFlow framework to Android smartphones.…”

Section: Software Performance and Technical Overviewmentioning

confidence: 99%

A Deep Learning Model for Quick and Accurate Rock Recognition with Smartphones

Fan

Chen

et al. 2020

Mobile Information Systems

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In the geological survey, the recognition and classification of rock lithology are an important content. The recognition method based on rock thin section leads to long recognition period and high recognition cost, and the recognition accuracy cannot be guaranteed. Moreover, the above method cannot provide an effective solution in the field. As a communication device with multiple sensors, smartphones are carried by most geological survey workers. In this paper, a smartphone application based on the convolutional neural network is developed. In this application, the phone’s camera can be used to take photos of rocks. And the types and lithology of rocks can be quickly and accurately identified in a very short time. This paper proposed a method for quickly and accurately recognizing rock lithology in the field. Based on ShuffleNet, a lightweight convolutional neural network used in deep learning, combined with the transfer learning method, the recognition model of the rock image was established. The trained model was then deployed to the smartphone. A smartphone application for identifying rock lithology was designed and developed to verify its usability and accuracy. The research results showed that the accuracy of the recognition model in this paper was 97.65% on the verification data set of the PC. The accuracy of recognition on the test data set of the smartphone was 95.30%, among which the average recognition time of the single sheet was 786 milliseconds, the maximum value was 1,045 milliseconds, and the minimum value was 452 milliseconds. And the single-image accuracy above 96% accounted for 95% of the test data set. This paper presented a new solution for the rapid and accurate recognition of rock lithology in field geological surveys, which met the needs of geological survey personnel to quickly and accurately identify rock lithology in field operations.

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Section: Software Performance and Technical Overviewmentioning

confidence: 99%

A Deep Learning Model for Quick and Accurate Rock Recognition with Smartphones

Fan

Chen

et al. 2020

Mobile Information Systems

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“…Over the past decades, researchers have used computer vision technology in agriculture for estimating crop yields (Gong et al, 2013 ; Deng et al, 2020 ), detecting crop nutritional deficiencies (Xu et al, 2011 ; Baresel et al, 2017 ; Tao et al, 2020 ), estimating geometric sizes of crop (Liu et al, 2019 ), and recognizing weeds (Jiang et al, 2020 ). Several different approaches of computer vision have also been used for the diagnosis of crop diseases, such as image processing, pattern recognition, support vector machine, and hyperspectral detection (Ngugi et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Automatic Diagnosis of Rice Diseases Using Deep Learning

et al. 2021

Self Cite

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Rice disease has serious negative effects on crop yield, and the correct diagnosis of rice diseases is the key to avoid these effects. However, the existing disease diagnosis methods for rice are neither accurate nor efficient, and special equipment is often required. In this study, an automatic diagnosis method was developed and implemented in a smartphone app. The method was developed using deep learning based on a large dataset that contained 33,026 images of six types of rice diseases: leaf blast, false smut, neck blast, sheath blight, bacterial stripe disease, and brown spot. The core of the method was the Ensemble Model in which submodels were integrated. Finally, the Ensemble Model was validated using a separate set of images. Results showed that the three best submodels were DenseNet-121, SE-ResNet-50, and ResNeSt-50, in terms of several attributes, such as, learning rate, precision, recall, and disease recognition accuracy. Therefore, these three submodels were selected and integrated in the Ensemble Model. The Ensemble Model minimized confusion among the different types of disease, reducing misdiagnosis of the disease. Using the Ensemble Model to diagnose six types of rice diseases, an overall accuracy of 91% was achieved, which is considered to be reasonably good, considering the appearance similarities in some types of rice disease. The smartphone app allowed the client to use the Ensemble Model on the web server through a network, which was convenient and efficient for the field diagnosis of rice leaf blast, false smut, neck blast, sheath blight, bacterial stripe disease, and brown spot.

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“…e hardware equipment is low in cost and easy to promote, but it is easily affected by environmental factors. For example, a method of using a mobile phone to measure leaf area [10,11] can obtain leaf area in real time in the field, but the whole system does not carry out high-precision calibration, resulting in large distortion and easy introduction of measurement error. In literature [12,13], Hough transform was used to carry out geometric correction on the image and improve the measurement accuracy, but the elimination of ambient light interference was not carried out.…”

Section: Introductionmentioning

confidence: 99%

2D In Situ Method for Measuring Plant Leaf Area with Camera Correction and Background Color Calibration

Peng

et al. 2021

Scientific Programming

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In order to measure the plant leaf area conveniently and quickly in an indoor laboratory and outdoor field, a set of scaffold leaf area measurement systems was designed and manufactured. A 2D in situ method for measuring plant leaf area with camera correction and background color calibration was proposed. The method integrates three subalgorithms: fast calibration and distortion correction algorithm, background color calibration algorithm, and edge error correction algorithm. At the same time, the Visual Studio 2015 and OpenCV 3.4.0 were used to develop and test the algorithm. In order to verify the measurement speed and environmental adaptability of the system, the test was carried out in the complex light disturbance outdoors, and the results were consistent with those in the room. In order to verify the measurement accuracy of the system, this method was used to measure the standard rectangular gauge block of known area and the real leaf area, respectively, and its data were compared with the data measured by Wanshen LA-S plant image analyzer. The results show that both methods have a good stability, and the algorithm proposed in this paper performs better in measurement accuracy and environmental adaptability.

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A Plant Leaf Geometric Parameter Measurement System Based on the Android Platform

Cited by 23 publications

References 26 publications

A Deep Learning Model for Quick and Accurate Rock Recognition with Smartphones

A Deep Learning Model for Quick and Accurate Rock Recognition with Smartphones

Automatic Diagnosis of Rice Diseases Using Deep Learning

2D In Situ Method for Measuring Plant Leaf Area with Camera Correction and Background Color Calibration

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