Delta tributary network—An efficient alternate approach for bottleneck layers in CNN for plant disease classification

Gunasekaran, Suresh; Gunavathi, K.

doi:10.1049/ipr2.12065

Cited by 2 publications

(4 citation statements)

References 40 publications

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“…We have taken 80 research papers that used various machine learning techniques for the comparison study. Articles (Bisen D., Shrestha G., 2020;Gunasekaran S., 2021) have taken many varieties of plant leaves as the input data (Table1). The performances of these models are studied with the other papers that are focusing on some particular plants and diseases.…”

Section: Resultsmentioning

confidence: 99%

“…Table 1 (continuation) [26] Deep Siamese CNN Grape 90% [27] Delta Tributary Network many varieties 96% [39] PCA and Machine Learning Pumpkin 97.30% [20] KNN, SVM Custard 99.50% [30] VGG16, Faster Region based CNN Tea leaf 95.74%…”

Section: Resultsmentioning

confidence: 99%

“…The classification process of the diseases is done using SVM. Gunasekaran S. et al, (2021), introduced delta tributary network, a novel deep learning technique that can replace the bottleneck DCNN architectures for plant disease detection and classification. Nihar V. et al, (2021) proposed a novel neural network ModCNN that produces an accuracy of 97.69% in finding the common diseases affecting the plant leaves.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Techniques in Plant Disease Detection and Classification – A State of the Art

John

Arul

2021

INMATEH

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As we belong to a developing country, the agricultural importance is a known criterion. Majority of the Indians depend on agriculture for their basic living. It also serves as the backbone of the Indian economy. Therefore this sector should be considered important and taken care of. Diseases affecting the plants and pest are the two major threats of agriculture production. Naked eye observation followed by the addition of chemical fertilizers is the traditional method adopted by most of the farmers to avoid plant diseases. But the main limitation to this method is that it works only in the case of small scale farming. In order to tackle this issue many automatic plant disease detection systems have been developed from the early 70s. This paper is intended to survey some of the existing works in plant disease recognition that include various procedures, materials and approaches. They use different machine learning algorithms, image processing techniques and deep learning methods for disease detection. This paper also compares and suggests novel methods to recognize and classify the various kinds of infections affecting agricultural plants.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning Techniques in Plant Disease Detection and Classification – A State of the Art

John

Arul

2021

INMATEH

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“…Convolutional neural networks (CNN) is a feedforward neural network that uses convolutional operations for deep learning. In recent years, it has been widely applied in biomedics, [25][26][27][28] engineering technology, [29][30][31] agriculture, [32][33][34] and other related fields. This technology has made breakthrough achievements in image recognition, [35][36][37] target monitoring, [38][39][40] natural speech processing, 41 and other aspects.…”

Section: Introductionmentioning

confidence: 99%

Improved deep learning based multi‐index comprehensive evaluation system for the research on accuracy and real‐time performance of coal gangue particles recognition

Zhang,

Yang,

Zeng

2023

Energy Science & Engineering

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The problems of insufficient recognition accuracy, poor real‐time performance and lack of consideration of actual working conditions in the process of intelligent construction of coal mines make this technology still in the research stage and not applied in practical engineering. The purpose of this paper is to establish an accurate and real‐time recognition model, which can quickly distinguish the vibration acceleration signals of coal and gangue under the influence of external factors such as impact position, velocity, and direction by using the different physical properties of coal and gangue particles. Therefore, the accuracy and real‐time of coal gangue recognition model established by different convolutional neural networks (CNN) structures and different position signal input are studied. First, to meet the real‐time requirements, an original CNN recognition model composed of single convolution layer and single pooling layer is established, and the data collected by seven sensors are input in the form of two‐dimensional matrix. However, the stability of the training and test results is insufficient. To solve this problem, once improved CNN (OI‐CNN) recognition model with multiconvolution layers and multipooling layers is built by deepening the network. The experimental results show that the stability and accuracy are improved, but the real‐time performance is poor. Furthermore, through parameter adjustment, the OI‐CNN is changed to the twice improved CNN (TI‐CNN), and the sensor data at different positions are input in the form of one‐dimensional vectors. The results show that the accuracy and real‐time performance of the TI‐CNN coal gangue recognition model are further improved. Finally, according to the research purpose of this paper, the weights of CNN indexes are given, and a multi‐index comprehensive evaluation system (MICES) is established. With the original CNN recognition model as the control, the OI‐CNN recognition model and the TI‐CNN recognition model at different positions are quantitatively compared to obtain the comprehensive evaluation scores of each model. The results show that the MICES of the coal gangue recognition model established based on the TI‐CNN structure and the data input of a single position sensor is the highest, while the sensor position has little effect on the recognition results.

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Delta tributary network—An efficient alternate approach for bottleneck layers in CNN for plant disease classification

Cited by 2 publications

References 40 publications

Machine Learning Techniques in Plant Disease Detection and Classification – A State of the Art

Machine Learning Techniques in Plant Disease Detection and Classification – A State of the Art

Improved deep learning based multi‐index comprehensive evaluation system for the research on accuracy and real‐time performance of coal gangue particles recognition

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