scholarly journals High-Accuracy Detection of Maize Leaf Diseases CNN Based on Multi-Pathway Activation Function Module

2021 ◽  
Vol 13 (21) ◽  
pp. 4218
Author(s):  
Yan Zhang ◽  
Shiyun Wa ◽  
Yutong Liu ◽  
Xiaoya Zhou ◽  
Pengshuo Sun ◽  
...  
Keyword(s):  
Agricultural Production ◽  
Activation Function ◽  
High Accuracy ◽  
Warm Up ◽  
Maize Leaf ◽  
Leaf Disease ◽  
Pathway Activation ◽  
Baseline Test ◽  
Validation Set ◽  
Function Module

Maize leaf disease detection is an essential project in the maize planting stage. This paper proposes the convolutional neural network optimized by a Multi-Activation Function (MAF) module to detect maize leaf disease, aiming to increase the accuracy of traditional artificial intelligence methods. Since the disease dataset was insufficient, this paper adopts image pre-processing methods to extend and augment the disease samples. This paper uses transfer learning and warm-up method to accelerate the training. As a result, three kinds of maize diseases, including maculopathy, rust, and blight, could be detected efficiently and accurately. The accuracy of the proposed method in the validation set reached 97.41%. This paper carried out a baseline test to verify the effectiveness of the proposed method. First, three groups of CNNs with the best performance were selected. Then, ablation experiments were conducted on five CNNs. The results indicated that the performances of CNNs have been improved by adding the MAF module. In addition, the combination of Sigmoid, ReLU, and Mish showed the best performance on ResNet50. The accuracy can be improved by 2.33%, proving that the model proposed in this paper can be well applied to agricultural production.

scholarly journals Emotion Recognition in Horses with Convolutional Neural Networks

2021 ◽  
Vol 13 (10) ◽  
pp. 250
Author(s):  
Luis A. Corujo ◽  
Emily Kieson ◽  
Timo Schloesser ◽  
Peter A. Gloor
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Intelligent Systems ◽  
High Accuracy ◽  
Eye Position ◽  
Proof Of Concept ◽  
Concept System ◽  
Validation Set ◽  
Fast Region ◽  
Head Neck

Creating intelligent systems capable of recognizing emotions is a difficult task, especially when looking at emotions in animals. This paper describes the process of designing a “proof of concept” system to recognize emotions in horses. This system is formed by two elements, a detector and a model. The detector is a fast region-based convolutional neural network that detects horses in an image. The model is a convolutional neural network that predicts the emotions of those horses. These two elements were trained with multiple images of horses until they achieved high accuracy in their tasks. In total, 400 images of horses were collected and labeled to train both the detector and the model while 40 were used to test the system. Once the two components were validated, they were combined into a testable system that would detect equine emotions based on established behavioral ethograms indicating emotional affect through the head, neck, ear, muzzle, and eye position. The system showed an accuracy of 80% on the validation set and 65% on the test set, demonstrating that it is possible to predict emotions in animals using autonomous intelligent systems. Such a system has multiple applications including further studies in the growing field of animal emotions as well as in the veterinary field to determine the physical welfare of horses or other livestock.

A Novel Method of Maize Leaf Disease Image Identification Based on a Multichannel Convolutional Neural Network

2018 ◽  
Vol 61 (5) ◽  
pp. 1461-1474 ◽  
Author(s):  
Zhongqi Lin ◽  
Shaomin Mu ◽  
Aiju Shi ◽  
Chao Pang ◽  
Xiaoxiao Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Graphics Processing Units ◽  
High Efficiency ◽  
Saliency Detection ◽  
Machine Learning Algorithms ◽  
Brown Spot ◽  
Maize Leaf ◽  
Leaf Disease ◽  
Fully Connected

Abstract. Traditional methods for detecting maize leaf diseases (such as leaf blight, sooty blotch, brown spot, rust, and purple leaf sheaf) are typically labor-intensive and strongly subjective. With the aim of achieving high accuracy and efficiency in the identification of maize leaf diseases from digital imagery, this article proposes a novel multichannel convolutional neural network (MCNN). The MCNN is composed of an input layer, five convolutional layers, three subsampling layers, three fully connected layers, and an output layer. Using a method that imitates human visual behavior in video saliency detection, the first and second subsampling layers are connected directly with the first fully connected layer. In addition, the mixed modes of pooling and normalization methods, rectified linear units (ReLU), and dropout are introduced to prevent overfitting and gradient diffusion. The learning process corresponding to the network structure is also illustrated. At present, there are no large-scale images of maize leaf disease for use as experimental samples. To test the proposed MCNN, 10,820 RGB images containing five types of disease were collected from maize planting areas in Shandong Province, China. The original images could not be used directly in identification experiments because of noise and irrelevant regions. They were therefore denoised and segmented by homomorphic filtering and region of interest (ROI) segmentation to construct a standard database. A series of experiments on 8 GB graphics processing units (GPUs) showed that the MCNN could achieve an average accuracy of 92.31% and a high efficiency in the identification of maize leaf diseases. The multichannel design and the integration of different innovations proved to be helpful methods for boosting performance. Keywords: Artificial intelligence, Convolutional neural network, Deep learning, Image classification, Machine learning algorithms, Maize leaf disease.

Maize Leaf Disease Detection and Classification Using Deep Learning

2021 ◽  
pp. 87-102
Author(s):  
Phani Kumar Singamsetty ◽  
G. V. N. D. Sai Prasad ◽  
N. V. Swamy Naidu ◽  
R. Suresh Kumar
Keyword(s):  
Deep Learning ◽  
Disease Detection ◽  
Maize Leaf ◽  
Leaf Disease

Research of Maize Leaf Disease Identifying Models Based Image Recognition

2009 ◽  
pp. 317-324 ◽  
Author(s):  
Yu-Xia Zhao ◽  
Ke-Ru Wang ◽  
Zhong-Ying Bai ◽  
Shao-Kun Li ◽  
Rui-Zhi Xie ◽  
...  
Keyword(s):  
Image Recognition ◽  
Maize Leaf ◽  
Leaf Disease

scholarly journals Apple Leaf Disease Identification with a Small and Imbalanced Dataset Based on Lightweight Convolutional Networks

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 173
Author(s):  
Lili Li ◽  
Shujuan Zhang ◽  
Bin Wang
Keyword(s):  
Plant Diseases ◽  
Panonychus Ulmi ◽  
Accurate Identification ◽  
Convolutional Networks ◽  
Leaf Disease ◽  
Ring Rot ◽  
Field Environment ◽  
Average Accuracy ◽  
Validation Set

The intelligent identification and classification of plant diseases is an important research objective in agriculture. In this study, in order to realize the rapid and accurate identification of apple leaf disease, a new lightweight convolutional neural network RegNet was proposed. A series of comparative experiments had been conducted based on 2141 images of 5 apple leaf diseases (rust, scab, ring rot, panonychus ulmi, and healthy leaves) in the field environment. To assess the effectiveness of the RegNet model, a series of comparison experiments were conducted with state-of-the-art convolutional neural networks (CNN) such as ShuffleNet, EfficientNet-B0, MobileNetV3, and Vision Transformer. The results show that RegNet-Adam with a learning rate of 0.0001 obtained an average accuracy of 99.8% on the validation set and an overall accuracy of 99.23% on the test set, outperforming all other pre-trained models. In other words, the proposed method based on transfer learning established in this research can realize the rapid and accurate identification of apple leaf disease.

scholarly journals Leaf Disease Detection

2021 ◽  
Vol 9 (VII) ◽  
pp. 3324-3329
Author(s):  
Shivangi Singh
Keyword(s):  
Developing Countries ◽  
Agricultural Production ◽  
Large Scale ◽  
Plant Diseases ◽  
Technical Knowledge ◽  
Plant Leaves ◽  
Employment Opportunities ◽  
Leaf Disease ◽  
Village People ◽  
The Many

Agriculture is a key source of livelihood. Agriculture provides employment opportunities for village people on a large scale in developing countries like India. India's agriculture consists of the many crops and consistent with survey nearly 70% population is depends on agriculture. Most of Indian farmers are adopting manual cultivation thanks to lagging of technical knowledge. Farmers are unaware of what quite crops that grows well on their land. When plants are suffering from heterogeneous diseases through their leaves which will effect on the production of agriculture and profitable loss, also reduction in both quality and quantity of agricultural production. Leaves are important for fast growing of plant and to extend production of crops. Identifying diseases in plant leaves is challenging for farmers and also for researchers. Currently farmers are spraying pesticides to the plants but it affects humans directly or indirectly by health or also economically. To detect these plant diseases many fast techniques got to be adopt. In this paper, we have done surveys on different leaf diseases and various advanced techniques to detect these diseases. As said by Mahatma Gandhi, "Agriculture is the backbone of the Indian Economy". Hence the detection of leaf diseases is an important aspect in increasing the yield of a crop. By detecting the leaf disease farmer can increase the crop yield which leads in growth of country’s economy.

Maize leaf disease classification using deep convolutional neural networks

2019 ◽  
Vol 31 (12) ◽  
pp. 8887-8895 ◽  
Author(s):  
Ramar Ahila Priyadharshini ◽  
Selvaraj Arivazhagan ◽  
Madakannu Arun ◽  
Annamalai Mirnalini
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Disease Classification ◽  
Deep Convolutional Neural Networks ◽  
Maize Leaf ◽  
Leaf Disease

scholarly journals Application of artificial neural networks in the prediction of sugarcane juice Pol

2019 ◽  
Vol 23 (1) ◽  
pp. 9-15 ◽  
Author(s):  
Anderson P. Coelho ◽  
João V. T. Bettiol ◽  
Alexandre B. Dalri ◽  
João A. Fischer Filho ◽  
Rogério T. de Faria ◽  
...  
Keyword(s):  
Activation Function ◽  
High Accuracy ◽  
Sugarcane Juice ◽  
Mean Square ◽  
Training Algorithm ◽  
Ann Modeling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Mean Square Errors ◽  
Innovative Techniques

ABSTRACT Innovative techniques that seek to minimize the costs of production and the laboriousness of certain operations are one of the great challenges in the sugar-energy sector nowadays. Thus, the objective of the present study was to estimate the Pol values of sugarcane juice as a function of °Brix and wet cake weight (WCW) using artificial neural network (ANN) modeling. A database was organized consisting of 204 technological analyses from a field experiment with 15 treatments and 2 years of evaluation. 75% of the data were used for the calibration of the model and 25% for its validation. Multilayer Perceptron ANNs were used for calibration and validation of the data. Before calibration, the variables were normalized. The training algorithm used was backpropagation and the activation function was the sigmoid. The ANNs were established with two hidden layers and the number of neurons ranging from 4 to 20 in each. The 15 ANNs with the lowest root mean square errors were randomly presented by the software, among which 6 were chosen to verify the accuracy. The ANNs had a high accuracy in the estimation of sugarcane juice Pol, both in the calibration phase (R2 = 0.948, RMSE = 0.36%) and in the validation (R2 = 0.878, RMSE = 0.41%), and can replace the standard method of analysis. Simpler networks can be trained to have the same accuracy as more complex networks.

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