scholarly journals Using Multioutput Learning to Diagnose Plant Disease and Stress Severity

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Gianni Fenu ◽  
Francesca Maridina Malloci
Keyword(s):  
Neural Networks ◽  
Precision Agriculture ◽  
Environmental Sustainability ◽  
Plant Disease ◽  
Plant Diseases ◽  
Economic Losses ◽  
Learning Approach ◽  
Proposed Model ◽  
Severity Levels ◽  
Leaf Symptoms

Early diagnosis of leaf diseases is a fundamental tool in precision agriculture, thanks to its high correlation with food safety and environmental sustainability. It is proven that plant diseases are responsible for serious economic losses every year. The aim of this work is to study an efficient network capable of assisting farmers in recognizing pear leaf symptoms and providing targeted information for rational use of pesticides. The proposed model consists of a multioutput system based on convolutional neural networks. The deep learning approach considers five pretrained CNN architectures, namely, VGG-16, VGG-19, ResNet50, InceptionV3, MobileNetV2, and EfficientNetB0, as feature extractors to classify three diseases and six severity levels. Computational experiments are conducted to evaluate the model on the DiaMOS Plant dataset, a self-collected dataset in the field. The results obtained confirm the robustness of the proposed model in automatically extracting the discriminating features of diseased leaves by adopting the multitasking learning paradigm.

scholarly journals Extension Plant Pathology: Strengthening Resources to Continue Serving the Public Interest

2012 ◽  
Vol 102 (7) ◽  
pp. 652-655 ◽  
Author(s):  
K. L. Everts ◽  
L. Osborne ◽  
A. J. Gevens ◽  
S. J. Vasquez ◽  
B. K. Gugino ◽  
...  
Keyword(s):  
Plant Pathology ◽  
Plant Disease ◽  
Crop Yields ◽  
Economic Value ◽  
The United States ◽  
Emerging Diseases ◽  
Plant Diseases ◽  
Economic Losses ◽  
Global Competitiveness ◽  
Horticultural Crops

Extension plant pathologists deliver science-based information that protects the economic value of agricultural and horticultural crops in the United States by educating growers and the general public about plant diseases. Extension plant pathologists diagnose plant diseases and disorders, provide advice, and conduct applied research on local and regional plant disease problems. During the last century, extension plant pathology programs have adjusted to demographic shifts in the U.S. population and to changes in program funding. Extension programs are now more collaborative and more specialized in response to a highly educated clientele. Changes in federal and state budgets and policies have also reduced funding and shifted the source of funding of extension plant pathologists from formula funds towards specialized competitive grants. These competitive grants often favor national over local and regional plant disease issues and typically require a long lead time to secure funding. These changes coupled with a reduction in personnel pose a threat to extension plant pathology programs. Increasing demand for high-quality, unbiased information and the continued reduction in local, state, and federal funds is unsustainable and, if not abated, will lead to a delay in response to emerging diseases, reduce crop yields, increase economic losses, and place U.S. agriculture at a global competitive disadvantage. In this letter, we outline four recommendations to strengthen the role and resources of extension plant pathologists as they guide our nation's food, feed, fuel, fiber, and ornamental producers into an era of increasing technological complexity and global competitiveness.

scholarly journals A Review on Advances in Automated Plant Disease Detection

2021 ◽  
Vol 11 (4) ◽  
pp. 251-264
Author(s):  
Radhika Bhagwat ◽  
Yogesh Dandawate
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Plant Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Detection Methods ◽  
Economic Losses ◽  
Disease Detection ◽  
Range Images

Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images.

Classification of plant diseases using machine and deep learning

2021 ◽  
Vol 11 (1) ◽  
pp. 491-508
Author(s):  
Monika Lamba ◽  
Yogita Gigras ◽  
Anuradha Dhull
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Plant Disease ◽  
Nearest Neighbor ◽  
Early Recognition ◽  
Activation Function ◽  
Plant Diseases ◽  
Support Vector ◽  
Specificity And Sensitivity ◽  
Proposed Model

Abstract Detection of plant disease has a crucial role in better understanding the economy of India in terms of agricultural productivity. Early recognition and categorization of diseases in plants are very crucial as it can adversely affect the growth and development of species. Numerous machine learning methods like SVM (support vector machine), random forest, KNN (k-nearest neighbor), Naïve Bayes, decision tree, etc., have been exploited for recognition, discovery, and categorization of plant diseases; however, the advancement of machine learning by DL (deep learning) is supposed to possess tremendous potential in enhancing the accuracy. This paper proposed a model comprising of Auto-Color Correlogram as image filter and DL as classifiers with different activation functions for plant disease. This proposed model is implemented on four different datasets to solve binary and multiclass subcategories of plant diseases. Using the proposed model, results achieved are better, obtaining 99.4% accuracy and 99.9% sensitivity for binary class and 99.2% accuracy for multiclass. It is proven that the proposed model outperforms other approaches, namely LibSVM, SMO (sequential minimal optimization), and DL with activation function softmax and softsign in terms of F-measure, recall, MCC (Matthews correlation coefficient), specificity and sensitivity.

scholarly journals Identification of Plant disease in leaves, using Deep Neural Networks

2020 ◽  
Vol 8 (5S) ◽  
pp. 18-21
Keyword(s):  
Neural Networks ◽  
Efficient Algorithm ◽  
Plant Disease ◽  
Deep Neural Networks ◽  
Research Topic ◽  
Plant Diseases ◽  
Plant Leaves ◽  
Crop Fields ◽  
Large Crop

Plant diseases have become a concern as they can lead to a significant reduction in both the quality and quantity of agricultural products.Immediate identification of plant diseases is a key research topic as it can prove useful in the monitoring of large crop fields and thus automatically identify the signs of pathogens as soon as they appear on plant leaves. The proposed efficient algorithm could successfully identify and recognize the diseases under investigation and model could achieve an accuracy of 95.18.

scholarly journals Deep Learning and Autoregressive Approach for Prediction of Time Series Data

2021 ◽  
Vol 3 (2) ◽  
pp. 1
Author(s):  
Akhter Mohiuddin Rather
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Statistical Models ◽  
Time Complexity ◽  
Time Series Data ◽  
Series Data ◽  
Learning Approach ◽  
Proposed Model ◽  
Artificial Neural

Fractional This paper proposes a deep learning approach for prediction of nonstationary data. A new regression scheme has been used in the proposed model. Any non-stationary data can be used to test the efficiency of the proposed model, however in this work stock data has been used due to the fact that stock data has a property of being nonlinear or non-stationary in nature. Beside using proposed model, predictions were also obtained using some statistical models and artificial neural networks. Traditional statistical models did not yield any expected results; artificial neural networks resulted into high time complexity. Therefore, deep learning approach seemed to be the best method as of today in dealing with such problems wherein time complexity and excellent predictions are of concern.

scholarly journals Plant disease prediction using convolutional neural network

2021 ◽  
Vol 9 (2) ◽  
pp. 283-293
Author(s):  
Hema M S ◽  
†, Niteesha Sharma ◽  
Y Sowjanya ◽  
Ch. Santoshini ◽  
R Sri Durga ◽  
...  
Keyword(s):  
Plant Disease ◽  
Plant Diseases ◽  
Disease Classification ◽  
Dense Layer ◽  
Convolutional Networks ◽  
Soil Features ◽  
Proposed Model ◽  
Plant Image ◽  
Manual Examination ◽  
Processing Steps

Every year India losses the significant amount of annual crop yield due to unidentified plant diseases. The traditional method of disease detection is manual examination by either farmers or experts, which may be time-consuming and inaccurate. It is proving infeasible for many small and medium-sized farms around the world. To mitigate this issue, computer aided disease recognition model is proposed. It uses leaf image classification with the help of deep convolutional networks. In this paper, VGG16 and Resnet34 CNN was proposed to detect the plant disease. It has three processing steps namely feature extraction, downsizing image and classification. In CNN, the convolutional layer extracts the feature from plant image. The pooling layer downsizing the image. The disease classification was done in dense layer. The proposed model can recognize 38 differing types of plant diseases out of 14 different plants with the power to differentiate plant leaves from their surroundings. The performance of VGG16 and Resnet34 was compared.  The accuracy, sensitivity and specificity was taken as performance Metrix. It helps to give personalized recommendations to the farmers based on soil features, temperature and humidity

Use of Convolutional Neural Networks in the Diagnosis of Corn Diseases

2021 ◽  
Author(s):  
Paulo Victor Cunha Lima ◽  
Edson Magalhães Costa ◽  
Maria Eliana da Silva Holanda ◽  
Dhian Kelson Leite Oliveira ◽  
Esley Teixeira Espírito Santo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Visual Observation ◽  
Plant Diseases ◽  
Subjective Perception ◽  
Maize Crop ◽  
Production Chain ◽  
Average Accuracy ◽  
Proposed Model ◽  
Field Practice

The detection of corn (maize) crop diseases is traditionally carried out by farmers, based on their experience accumulated over a period of field practice. However, the visual observation may represent a risk of error due to subjective perception. This article presents an approach based on Deep Learning to identify diseases that affect corn crops. A public database with 3,852 images of maize plant leaves was used, dividedinto four classes: healthy corn, exserohilun leaf spot (northern leaf blight), common corn rust (common rust) and cercosporiosis (cercospora leaf/gray leaf). The proposed model used Convolutional Neural Networks (CNN) techniques for image classification. The four experiments indicated results with an average accuracy above 94.5%. These results in the identification and diagnosis of plant diseases can contribute significantly as atool to the improvement of the production chain that affect corn crops. All data are available at https://github.com/npcaufra/classificacao-doencas-milho .

scholarly journals Diagnosis of Plant Diseases using Artificial Neural Network

2019 ◽  
Vol 8 (11) ◽  
pp. 1464-1467
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Plant Diseases ◽  
Economic Losses ◽  
Fruit Crops ◽  
Agricultural Business ◽  
Complex Foundation

Pomegranate is one of India's most commonly cultivated fruit crops. manual expert observations are being used to detect leaf diseases that take longer time for further prevention. Fruit diseases are causing devastating disadvantages in worldwide agricultural business economic losses in production .in this journal, the answer is proposed and valid by experiment for the identification and classification of fruit disorders. The objective of proposed work is to analyze the illness utilizing picture preparing and artificial intelligence techniques on pictures of pomegranate plant leaf. In the proposed framework, pomegranate leaf picture with complex foundation is taken as input. Then pomegranate leaf ailment division is finished utilizing K-means clustering. The infected segment from portioned pictures is recognized. Best results have been seen when neural networks with a RBFN is used for a classification.

scholarly journals How Convolutional Neural Networks Diagnose Plant Disease

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yosuke Toda ◽  
Fumio Okura
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Plant Disease ◽  
Disease Diagnosis ◽  
Black Box ◽  
Plant Diseases ◽  
Training Dataset ◽  
Great Success ◽  
Human Decision

Deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various plant diseases. However, a limited number of studies have elucidated the process of inference, leaving it as an untouchable black box. Revealing the CNN to extract the learned feature as an interpretable form not only ensures its reliability but also enables the validation of the model authenticity and the training dataset by human intervention. In this study, a variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available plant disease image dataset. We showed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis, which resembles human decision-making. While several visualization methods were used as they are, others had to be optimized to target a specific layer that fully captures the features to generate consequential outputs. Moreover, by interpreting the generated attention maps, we identified several layers that were not contributing to inference and removed such layers inside the network, decreasing the number of parameters by 75% without affecting the classification accuracy. The results provide an impetus for the CNN black box users in the field of plant science to better understand the diagnosis process and lead to further efficient use of deep learning for plant disease diagnosis.

scholarly journals Meta-Learning for Few-Shot Plant Disease Detection

Foods ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2441
Author(s):  
Liangzhe Chen ◽  
Xiaohui Cui ◽  
Wei Li
Keyword(s):  
Neural Networks ◽  
Crop Production ◽  
Plant Disease ◽  
Plant Pathogens ◽  
Feature Matching ◽  
Apple Scab ◽  
Plant Diseases ◽  
Deep Impact ◽  
Meta Learning ◽  
Adaptive Processes

Plant diseases can harm crop growth, and the crop production has a deep impact on food. Although the existing works adopt Convolutional Neural Networks (CNNs) to detect plant diseases such as Apple Scab and Squash Powdery mildew, those methods have limitations as they rely on a large amount of manually labeled data. Collecting enough labeled data is not often the case in practice because: plant pathogens are variable and farm environments make collecting data difficulty. Methods based on deep learning suffer from low accuracy and confidence when facing few-shot samples. In this paper, we propose local feature matching conditional neural adaptive processes (LFM-CNAPS) based on meta-learning that aims at detecting plant diseases of unseen categories with only a few annotated examples, and visualize input regions that are ‘important’ for predictions. To train our network, we contribute Miniplantdisease-Dataset that contains 26 plant species and 60 plant diseases. Comprehensive experiments demonstrate that our proposed LFM-CNAPS method outperforms the existing methods.

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