Automatic Identification of Plant Physiological Disorders in Plant Factories with Artificial Light Using Convolutional Neural Networks

2019 ◽  
Vol 9 (1) ◽  
pp. 25-30
Author(s):  
Shigeharu Shimamura ◽  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automatic Identification ◽  
Artificial Light ◽  
Physiological Disorders

scholarly journals Plant Diseases Identification through a Discount Momentum Optimizer in Deep Learning

2021 ◽  
Vol 11 (20) ◽  
pp. 9468
Author(s):  
Yunyun Sun ◽  
Yutong Liu ◽  
Haocheng Zhou ◽  
Huijuan Hu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Adaptive Learning ◽  
Learning Rate ◽  
Plant Diseases ◽  
Stochastic Gradient Descent ◽  
Automatic Identification ◽  
Deep Convolutional Neural Networks ◽  
Adaptive Learning Rate

Deep learning proves its promising results in various domains. The automatic identification of plant diseases with deep convolutional neural networks attracts a lot of attention at present. This article extends stochastic gradient descent momentum optimizer and presents a discount momentum (DM) deep learning optimizer for plant diseases identification. To examine the recognition and generalization capability of the DM optimizer, we discuss the hyper-parameter tuning and convolutional neural networks models across the plantvillage dataset. We further conduct comparison experiments on popular non-adaptive learning rate methods. The proposed approach achieves an average validation accuracy of no less than 97% for plant diseases prediction on several state-of-the-art deep learning models and holds a low sensitivity to hyper-parameter settings. Experimental results demonstrate that the DM method can bring a higher identification performance, while still maintaining a competitive performance over other non-adaptive learning rate methods in terms of both training speed and generalization.

scholarly journals Use of Images of Leaves and Fruits of Apple Trees for Automatic Identification of Symptoms of Diseases and Nutritional Disorders

2017 ◽  
Vol 5 (2) ◽  
pp. 1-14
Author(s):  
Lucas Garcia Nachtigall ◽  
Ricardo Matsumura Araujo ◽  
Gilmar Ribeiro Nachtigall
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Rapid Diagnosis ◽  
Nutritional Deficiencies ◽  
Automatic Identification ◽  
Apple Trees ◽  
Data Set ◽  
Apple Fruits

Rapid diagnosis of symptoms caused by pest attack, diseases and nutritional or physiological disorders in apple orchards is essential to avoid greater losses. This paper aimed to evaluate the efficiency of Convolutional Neural Networks (CNN) to automatically detect and classify symptoms of diseases, nutritional deficiencies and damage caused by herbicides in apple trees from images of their leaves and fruits. A novel data set was developed containing labeled examples consisting of approximately 10,000 images of leaves and apple fruits divided into 12 classes, which were classified by algorithms of machine learning, with emphasis on models of deep learning. The results showed trained CNNs can overcome the performance of experts and other algorithms of machine learning in the classification of symptoms in apple trees from leaves images, with an accuracy of 97.3% and obtain 91.1% accuracy with fruit images. In this way, the use of Convolutional Neural Networks may enable the diagnosis of symptoms in apple trees in a fast, precise and usual way.

scholarly journals A Pointer Type Instrument Intelligent Reading System Design Based on Convolutional Neural Networks

2020 ◽  
Vol 8 ◽  
Author(s):  
Yue Lin ◽  
Qinghua Zhong ◽  
Hailing Sun
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Feature Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Operating Conditions ◽  
Automatic Identification ◽  
Online Reading ◽  
Reading System

The pointer instrument has the advantages of being simple, reliable, stable, easy to maintain, having strong anti-interference properties, and so on, which has long occupied the main position of electrical and electric instruments. Though the pointer instrument structure is simple, it is not convenient for real-time reading of measurements. In this paper, a RK3399 microcomputer was used for real-time intelligent reading of a pointer instrument using a camera. Firstly, a histogram normalization transform algorithm was used to optimize the brightness and enhance the contrast of images; then, the feature recognition algorithm You Only Look Once 3rd (YOLOv3) was used to detect and capture the panel area in images; and Convolutional Neural Networks were used to read and predict the characteristic images. Finally, predicted results were uploaded to a server. The system realized automatic identification, numerical reading, an intelligent online reading of pointer data, which has high feasibility and practical value. The experimental results show that the recognition rate of this system was 98.71% and the reading accuracy was 97.42%. What is more, the system can accurately locate the pointer-type instrument area and read corresponding values with simple operating conditions. This achievement meets the demand of real-time readings for analog instruments.

scholarly journals Automatic Identification of Harmful Algae Based On Multiple Convolutional Neural Networks and Transfer Learning

2021 ◽  
Author(s):  
Mengyu Yang ◽  
Wensi Wang ◽  
Qiang Gao ◽  
Chen Zhao ◽  
Caole Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Recognition Performance ◽  
Harmful Algae ◽  
Fine Tuning ◽  
Automatic Identification ◽  
Red Tides ◽  
Monitoring Methods ◽  
Identification Method

Abstract The monitoring of harmful algae is very important for the maintenance of the aquatic ecological environment. Traditional algae monitoring methods require professionals with substantial experience in algae species, which are time-consuming, expensive and limited in practice. The automatic classification of algae cell images and the identification of harmful algae images were realized by the combination of multiple Convolutional Neural Networks (CNNs) and deep learning techniques based on transfer learning in this work. 11 common harmful and 31 harmless algae genera were collected as input samples, the five CNNs classification models of AlexNet, VGG16, GoogLeNet, ResNet50, and MobileNetV2 were fine-tuned to automatically classify algae images, and the average accuracy was improved 11.9% when compared to models without fine-tuning. In order to monitor harmful algae which can cause red tides or produce toxins severely polluting drinking water, a new identification method of harmful algae which combines the recognition results of five CNN models was proposed, and the recall rate reached 98.0%. The experimental results validate that the recognition performance of harmful algae could be significantly improved by transfer learning, and the proposed identification method is effective in the preliminary screening of harmful algae and greatly reduces the workload of professional personnel.

scholarly journals Automatic Identification of Center Pivot Irrigation Systems from Landsat Images Using Convolutional Neural Networks

Agriculture ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 147 ◽  
Author(s):  
Chenxiao Zhang ◽  
Peng Yue ◽  
Liping Di ◽  
Zhaoyan Wu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Water Resource Management ◽  
Data Augmentation ◽  
The United States ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Automatic Identification ◽  
Irrigation Systems ◽  
Center Pivot

Being hailed as the greatest mechanical innovation in agriculture since the replacement of draft animals by the tractor, center pivot irrigation systems irrigate crops with a significant reduction in both labor and water needs compared to traditional irrigation methods, such as flood irrigation. In the last few decades, the deployment of center pivot irrigation systems has increased dramatically throughout the United States. Monitoring the installment and operation of the center pivot systems can help: (i) Water resource management agencies to objectively assess water consumption and properly allocate water resources, (ii) Agro-businesses to locate potential customers, and (iii) Researchers to investigate land use change. However, few studies have been carried out on the automatic identification and location of center pivot irrigation systems from satellite images. Growing rapidly in recent years, machine learning techniques have been widely applied on image recognition, and they provide a possible solution for identification of center pivot systems. In this study, a Convolutional Neural Networks (CNNs) approach was proposed for identification of center pivot irrigation systems. CNNs with different structures were constructed and compared for the task. A sampling approach was presented for training data augmentation. The CNN with the best performance and less training time was used in the testing area. A variance-based approach was proposed to further locate the center of each center pivot system. The experiment was applied to a 30-m resolution Landsat image, covering an area of 20,000 km2 in North Colorado. A precision of 95.85% and a recall of 93.33% of the identification results indicated that the proposed approach performed well in the center pivot irrigation systems identification task.

Segmentation of wood cell in cross-section using deep convolutional neural networks

2021 ◽  
pp. 1-10
Author(s):  
Halime Ergun
Keyword(s):  
Neural Networks ◽  
Cross Section ◽  
Convolutional Neural Networks ◽  
Optimization Algorithm ◽  
Cell Types ◽  
Anatomical Structure ◽  
Automatic Identification ◽  
Segmentation Method ◽  
Deep Convolutional Neural Networks ◽  
Anatomical Features

Fiber and vessel structures located in the cross-section are anatomical features that play an important role in identifying tree species. In order to determine the microscopic anatomical structure of these cell types, each cell must be accurately segmented. In this study, a segmentation method is proposed for wood cell images based on deep convolutional neural networks. The network, which was developed by combining two-stage CNN structures, was trained using the Adam optimization algorithm. For evaluation, the method was compared with SegNet and U-Net architectures, trained with the same dataset. The losses in these models trained were compared using IoU (Intersection over Union), accuracy, and BF-score measurements on the test data. The automatic identification of the cells in the wood images obtained using a microscope will provide a fast, inexpensive, and reliable tool for those working in this field.

scholarly journals Convolutional Neural Networks for the Automatic Identification of Plant Diseases

2019 ◽  
Vol 10 ◽  
Author(s):  
Justine Boulent ◽  
Samuel Foucher ◽  
Jérôme Théau ◽  
Pierre-Luc St-Charles
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Plant Diseases ◽  
Automatic Identification

scholarly journals Automatic Identification of Local Features Representing Image Content with the Use of Convolutional Neural Networks

2020 ◽  
Vol 10 (15) ◽  
pp. 5186
Author(s):  
Paweł Tarasiuk ◽  
Arkadiusz Tomczyk ◽  
Bartłomiej Stasiak
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Sparse Matrices ◽  
Local Features ◽  
Automatic Identification ◽  
Image Content ◽  
Data Set ◽  
Practical Applications ◽  
A Value ◽  
Working Principle

Image analysis has many practical applications and proper representation of image content is its crucial element. In this work, a novel type of representation is proposed where an image is reduced to a set of highly sparse matrices. Equivalently, it can be viewed as a set of local features of different types, as precise coordinates of detected keypoints are given. Additionally, every keypoint has a value expressing feature intensity at a given location. These features are extracted from a dedicated convolutional neural network autoencoder. This kind of representation has many advantages. First of all, local features are not manually designed but are automatically trained for a given class of images. Second, as they are trained in a network that restores its input on the output, they may be expected to minimize information loss. Consequently, they can be used to solve similar tasks replacing original images; such an ability was illustrated with image classification task. Third, the generated features, although automatically synthesized, are relatively easy to interpret. Taking a decoder part of our network, one can easily generate a visual building block connected with a specific feature. As the proposed method is entirely new, a detailed analysis of its properties for a relatively simple data set was conducted and is described in this work. Moreover, to present the quality of trained features, it is compared with results of convolutional neural networks having a similar working principle (sparse coding).

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