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 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.

Deep Learning-Based Mobile Application for Plant Disease Diagnosis

2019 ◽  
pp. 242-271 ◽  
Author(s):  
Shradha Verma ◽  
Anuradha Chug ◽  
Amit Prakash Singh ◽  
Shubham Sharma ◽  
Puranjay Rajvanshi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Mobile Application ◽  
Plant Disease ◽  
Prediction Models ◽  
Disease Diagnosis ◽  
Tomato Leaf ◽  
Plant Diseases ◽  
Learning Tools ◽  
Class Labels

With the increasing computational power, areas such as machine learning, image processing, deep learning, etc. have been extensively applied in agriculture. This chapter investigates the applications of the said areas and various prediction models in plant pathology for accurate classification, identification, and quantification of plant diseases. The authors aim to automate the plant disease identification process. To accomplish this objective, CNN has been utilized for image classification. Research shows that deep learning architectures outperform other machine learning tools significantly. To this effect, the authors have implemented and trained five CNN models, namely Inception ResNet v2, VGG16, VGG19, ResNet50, and Xception, on PlantVillage dataset for tomato leaf images. The authors analyzed 18,160 tomato leaf images spread across 10 class labels. After comparing their performance measures, ResNet50 proved to be the most accurate prediction tool. It was employed to create a mobile application to classify and identify tomato plant diseases successfully.

Deep Learning in Engineering Education

2020 ◽  
pp. 187-218
Author(s):  
Deepali R. Vora ◽  
Kamatchi R. Iyer
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Information Systems ◽  
Engineering Education ◽  
Hybrid Model ◽  
Cuckoo Search ◽  
Support Vector ◽  
Proposed Model ◽  
Class Labels

The goodness measure of any institute lies in minimising the dropouts and targeting good placements. So, predicting students' performance is very interesting and an important task for educational information systems. Machine learning and deep learning are the emerging areas that truly entice more research practices. This research focuses on applying the deep learning methods to educational data for classification and prediction. The educational data of students from engineering domain with cognitive and non-cognitive parameters is considered. The hybrid model with support vector machine (SVM) and deep belief network (DBN) is devised. The SVM predicts class labels from preprocessed data. These class labels and actual class labels acts as input to the DBN to perform final classification. The hybrid model is further optimised using cuckoo search with Levy flight. The results clearly show that the proposed model SVM-LCDBN gives better performance as compared to simple hybrid model and hybrid model with traditional cuckoo search.

scholarly journals A stacking ensemble deep learning approach to cancer type classification based on TCGA data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohanad Mohammed ◽  
Henry Mwambi ◽  
Innocent B. Mboya ◽  
Murtada K. Elbashir ◽  
Bernard Omolo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Feature Selection Method ◽  
Morphological Characteristics ◽  
Support Vector ◽  
Cancer Type ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Proposed Model ◽  
Significant Difference

AbstractCancer tumor classification based on morphological characteristics alone has been shown to have serious limitations. Breast, lung, colorectal, thyroid, and ovarian are the most commonly diagnosed cancers among women. Precise classification of cancers into their types is considered a vital problem for cancer diagnosis and therapy. In this paper, we proposed a stacking ensemble deep learning model based on one-dimensional convolutional neural network (1D-CNN) to perform a multi-class classification on the five common cancers among women based on RNASeq data. The RNASeq gene expression data was downloaded from Pan-Cancer Atlas using GDCquery function of the TCGAbiolinks package in the R software. We used least absolute shrinkage and selection operator (LASSO) as feature selection method. We compared the results of the new proposed model with and without LASSO with the results of the single 1D-CNN and machine learning methods which include support vector machines with radial basis function, linear, and polynomial kernels; artificial neural networks; k-nearest neighbors; bagging trees. The results show that the proposed model with and without LASSO has a better performance compared to other classifiers. Also, the results show that the machine learning methods (SVM-R, SVM-L, SVM-P, ANN, KNN, and bagging trees) with under-sampling have better performance than with over-sampling techniques. This is supported by the statistical significance test of accuracy where the p-values for differences between the SVM-R and SVM-P, SVM-R and ANN, SVM-R and KNN are found to be p = 0.003, p =  < 0.001, and p =  < 0.001, respectively. Also, SVM-L had a significant difference compared to ANN p = 0.009. Moreover, SVM-P and ANN, SVM-P and KNN are found to be significantly different with p-values p =  < 0.001 and p =  < 0.001, respectively. In addition, ANN and bagging trees, ANN and KNN were found to be significantly different with p-values p =  < 0.001 and p = 0.004, respectively. Thus, the proposed model can help in the early detection and diagnosis of cancer in women, and hence aid in designing early treatment strategies to improve survival.

Flood prediction based on weather parameters using deep learning

2019 ◽  
Vol 11 (4) ◽  
pp. 1766-1783 ◽  
Author(s):  
Suresh Sankaranarayanan ◽  
Malavika Prabhakar ◽  
Sreesta Satish ◽  
Prerna Jain ◽  
Anjali Ramprasad ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Rainfall Intensity ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Flood Prediction ◽  
Deep Learning Model

Abstract Today, India is one of the worst flood-affected countries in the world, with the recent disaster in Kerala in August 2018 being a prime example. A good amount of work has been carried out by employing Internet of Things (IoT) and machine learning (ML) techniques in the past for flood occurrence based on rainfall, humidity, temperature, water flow, water level etc. However, the challenge is that no one has attempted the possibility of occurrence of flood based on temperature and rainfall intensity. So accordingly Deep Neural Network has been employed for predicting the occurrence of flood based on temperature and rainfall intensity. In addition, a deep learning model is compared with other machine learning models (support vector machine (SVM), K-nearest neighbor (KNN) and Naïve Bayes) in terms of accuracy and error. The results indicate that the deep neural network can be efficiently used for flood forecasting with highest accuracy based on monsoon parameters only before flood occurrence.

Deep Learning in Plant Diseases Detection for Agricultural Crops

2020 ◽  
Vol 11 (2) ◽  
pp. 41-58 ◽  
Author(s):  
Mohamed Loey ◽  
Ahmed ElSawy ◽  
Mohamed Afify
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Plant Disease ◽  
Plant Diseases ◽  
Food Plants ◽  
Disease Detection ◽  
Human Society ◽  
Research Papers ◽  
Limited Scope

Deep learning has brought a huge improvement in the area of machine learning in general and most particularly in computer vision. The advancements of deep learning have been applied to various domains leading to tremendous achievements in the areas of machine learning and computer vision. Only recent works have introduced applying deep learning to the field of using computers in agriculture. The need for food production and food plants is of utmost importance for human society to meet the growing demands of an increased population. Automatic plant disease detection using plant images was originally tackled using traditional machine learning and image processing approaches resulting in limited accuracy results and a limited scope. Using deep learning in plant disease detection made it possible to produce higher prediction accuracies as well as broadened the scope of detected diseases and plant species considered. This article presents a survey of research papers that presented the use of deep learning in plant disease detection, and analyzes them in terms of the dataset used, models employed, and overall performance achieved.

scholarly journals Intelligent Plant Disease Identification System Using Machine Learning

2020 ◽  
Vol 2 (1) ◽  
pp. 49
Author(s):  
Paramasivam Alagumariappan ◽  
Najumnissa Jamal Dewan ◽  
Gughan Narasimhan Muthukrishnan ◽  
Bhaskar K. Bojji Raju ◽  
Ramzan Ali Arshad Bilal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real Time ◽  
Extreme Learning Machine ◽  
Plant Disease ◽  
Agricultural Sector ◽  
Plant Diseases ◽  
Support Vector ◽  
Identification System ◽  
Learning Machine

Agriculture is the backbone of every country in the world. In India, most of the rural population still depends on agriculture. The agricultural sector provides major employment in rural areas. Furthermore, it contributes a significant amount to India’s gross domestic product (GDP). Therefore, protecting and enhancing the agricultural sector helps in the development of India’s economy. In this work, a real-time decision support system integrated with a camera sensor module was designed and developed for identification of plant disease. Furthermore, the performance of three machine learning algorithms, such as Extreme Learning Machine (ELM) and Support Vector Machine (SVM) with linear and polynomial kernels was analyzed. Results demonstrate that the performance of the extreme learning machine is better when compared to the adopted support vector machine classifier. It is also observed that the sensitivity of the support vector machine with a polynomial kernel is better when compared to the other classifiers. This work appears to be of high social relevance, because the developed real-time hardware is capable of detecting different plant diseases.

scholarly journals A Deep Learning Enabled Multi-Class Plant Disease Detection Model Based on Computer Vision

AI ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 413-428
Author(s):  
Arunabha M. Roy ◽  
Jayabrata Bhaduri
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Large Scale ◽  
Plant Disease ◽  
State Of The Art ◽  
Plant Diseases ◽  
Disease Detection ◽  
Detection Model ◽  
Proposed Model ◽  
Apple Plant

In this paper, a deep learning enabled object detection model for multi-class plant disease has been proposed based on a state-of-the-art computer vision algorithm. While most existing models are limited to disease detection on a large scale, the current model addresses the accurate detection of fine-grained, multi-scale early disease detection. The proposed model has been improved to optimize for both detection speed and accuracy and applied to multi-class apple plant disease detection in the real environment. The mean average precision (mAP) and F1-score of the detection model reached up to 91.2% and 95.9%, respectively, at a detection rate of 56.9 FPS. The overall detection result demonstrates that the current algorithm significantly outperforms the state-of-the-art detection model with a 9.05% increase in precision and 7.6% increase in F1-score. The proposed model can be employed as an effective and efficient method to detect different apple plant diseases under complex orchard scenarios.

scholarly journals A Convolutional Neural Network based System to Detect Plant Disease

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 944-962
Author(s):  
K. Niha ◽  
Dr.S. Amutha ◽  
Dr. Aisha Banu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Neural Network Model ◽  
Plant Disease ◽  
Performance Metrics ◽  
Plant Diseases ◽  
Detection Accuracy ◽  
Learning Models ◽  
Proposed Model

Disease in plants are a great challenge in the advancement of agriculture which affects farmers yield and the plants. In this modern research deep learning models got a spot light by increasing plant detection accuracy and classification. The proposed CNN (Convolutional Neural Network) model detect seven plant diseases out of healthy leaf, where the dataset considered in this work contain 8685 leaf images from Plant Village Dataset. The proposed modals performance are evaluated with respect to the performance metrics (F1 score, Precision and Recall) and are compared with SVM and ANN. Where the proposed CNN model outperforms the rest with the accuracy of 96.2% and the F1 score greater than 95%. The feasibility of the proposed model in plant detection and classification may provide a solution to the problem faced by farmers.

scholarly journals Plant Disease Detection and Classification by Deep Learning

Plants ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 468 ◽  
Author(s):  
Saleem ◽  
Potgieter ◽  
Mahmood Arif
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Early Identification ◽  
Plant Disease ◽  
Performance Metrics ◽  
Plant Diseases ◽  
Research Gaps ◽  
Visualization Techniques ◽  
Respective Species

Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly.

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