Plant disease prediction using convolutional neural network

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

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Classification of plant diseases using machine and deep learning

Open Computer Science ◽

10.1515/comp-2020-0122 ◽

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.

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Using Deep Learning for Image-Based Potato Tuber Disease Detection

Phytopathology ◽

10.1094/phyto-08-18-0288-r ◽

2019 ◽

Vol 109 (6) ◽

pp. 1083-1087 ◽

Cited By ~ 11

Author(s):

Dor Oppenheim ◽

Guy Shani ◽

Orly Erlich ◽

Leah Tsror

Keyword(s):

Deep Learning ◽

Low Cost ◽

Image Data ◽

Object Identification ◽

Plant Diseases ◽

Disease Classification ◽

Successful Implementation ◽

Potato Tubers ◽

Data Set ◽

Convolutional Networks

Many plant diseases have distinct visual symptoms, which can be used to identify and classify them correctly. This article presents a potato disease classification algorithm that leverages these distinct appearances and advances in computer vision made possible by deep learning. The algorithm uses a deep convolutional neural network, training it to classify the tubers into five classes: namely, four disease classes and a healthy potato class. The database of images used in this study, containing potato tubers of different cultivars, sizes, and diseases, was acquired, classified, and labeled manually by experts. The models were trained over different train-test splits to better understand the amount of image data needed to apply deep learning for such classification tasks. The models were tested over a data set of images taken using standard low-cost RGB (red, green, and blue) sensors and were tagged by experts, demonstrating high classification accuracy. This is the first article to report the successful implementation of deep convolutional networks, popular in object identification, to the task of disease identification in potato tubers, showing the potential of deep learning techniques in agricultural tasks.

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Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection

Symmetry ◽

10.3390/sym11070939 ◽

2019 ◽

Vol 11 (7) ◽

pp. 939 ◽

Cited By ~ 28

Author(s):

Marko Arsenovic ◽

Mirjana Karanovic ◽

Srdjan Sladojevic ◽

Andras Anderla ◽

Darko Stefanovic

Keyword(s):

Deep Learning ◽

Plant Disease ◽

Real Life ◽

Weather Conditions ◽

Plant Diseases ◽

Disease Classification ◽

Generative Adversarial Networks ◽

Disease Detection ◽

Single Leaf ◽

The Impact

Plant diseases cause great damage in agriculture, resulting in significant yield losses. The recent expansion of deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. The current limitations and shortcomings of existing plant disease detection models are presented and discussed in this paper. Furthermore, a new dataset containing 79,265 images was introduced with the aim to become the largest dataset containing leaf images. Images were taken in various weather conditions, at different angles, and daylight hours with an inconsistent background mimicking practical situations. Two approaches were used to augment the number of images in the dataset: traditional augmentation methods and state-of-the-art style generative adversarial networks. Several experiments were conducted to test the impact of training in a controlled environment and usage in real-life situations to accurately identify plant diseases in a complex background and in various conditions including the detection of multiple diseases in a single leaf. Finally, a novel two-stage architecture of a neural network was proposed for plant disease classification focused on a real environment. The trained model achieved an accuracy of 93.67%.

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Improving Image-Based Plant Disease Classification With Generative Adversarial Network Under Limited Training Set

Frontiers in Plant Science ◽

10.3389/fpls.2020.583438 ◽

2020 ◽

Vol 11 ◽

Author(s):

Luning Bi ◽

Guiping Hu

Keyword(s):

Classification Accuracy ◽

Plant Disease ◽

Data Augmentation ◽

Plant Diseases ◽

Disease Classification ◽

Training Data ◽

Training Dataset ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Overfitting Problem

Traditionally, plant disease recognition has mainly been done visually by human. It is often biased, time-consuming, and laborious. Machine learning methods based on plant leave images have been proposed to improve the disease recognition process. Convolutional neural networks (CNNs) have been adopted and proven to be very effective. Despite the good classification accuracy achieved by CNNs, the issue of limited training data remains. In most cases, the training dataset is often small due to significant effort in data collection and annotation. In this case, CNN methods tend to have the overfitting problem. In this paper, Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is combined with label smoothing regularization (LSR) to improve the prediction accuracy and address the overfitting problem under limited training data. Experiments show that the proposed WGAN-GP enhanced classification method can improve the overall classification accuracy of plant diseases by 24.4% as compared to 20.2% using classic data augmentation and 22% using synthetic samples without LSR.

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RICE PLANT DISEASE CLASSIFICATION USING TRANSFER LEARNING OF DEEP CONVOLUTION NEURAL NETWORK

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w6-631-2019 ◽

2019 ◽

Vol XLII-3/W6 ◽

pp. 631-635 ◽

Cited By ~ 7

Author(s):

V. K. Shrivastava ◽

M. K. Pradhan ◽

S. Minz ◽

M. P. Thakur

Keyword(s):

Neural Network ◽

Rice Plant ◽

Plant Disease ◽

Crop Protection ◽

Visual Observation ◽

Plant Diseases ◽

Disease Classification ◽

Real Field ◽

Support Vector ◽

Identification System

Abstract. Early and accurate diagnosis of plant diseases is a vital step in the crop protection system. In traditional practices, identification is performed either by visual observation or by testing in laboratory. The visual observation requires expertise and it may vary subject to an individual which may lead to an error while the laboratory test is time consuming and may not be able to provide the results in time. To overcome these issues, image based machine learning approach to detect and classify plant diseases has been presented in literature. We have focused specifically on rice plant (Oryza sativa) disease in this paper. The images of the diseased symptoms in leaves and stems have been captured from the rice field. We have collected a total of 619 rice plant diseased images from the real field condition belong to four classes:(a) Rice Blast (RB), (b) Bacterial Leaf Blight (BLB), (c) Sheat Blight (SB) and (d) Healthy Leave (HL). We have used a pre-trained deep convolutional neural network(CNN) as a feature extractor and Support Vector Machine (SVM) as a classifier. We have obtained encouraging results. The early identification of rice diseases by this approach could be used as a preventive measure well as an early warning system. Further, it could be extended to develop a rice plant disease identification system on real agriculture field.

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Potato Leaf Disease Classification using Transfer Learning based Modified Xception Model

10.46532/978-81-950008-1-4_096 ◽

2020 ◽

pp. 438-442

Author(s):

Rajasekaran Thangaraj ◽

Pandiyan P ◽

Vishnu Kumar Kaliappan ◽

Anandamurugan S ◽

Indupriya P

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Potato Plant ◽

Plant Disease ◽

Learning Algorithm ◽

Selection Criterion ◽

Plant Diseases ◽

Disease Classification ◽

Agricultural Field ◽

Potato Leaf

Plant diseases are the essential thing which decreases the quantity as well quality in agricultural field. As a result, the identification and analysis of the diseases are important. The proper classification with least data in deep learning is the most challenging task. In addition, it is tough to label the data manually depending upon the selection criterion. Transfer learning algorithm helps in resolving this kind of problem by means of learning the previous task and then applying capabilities and knowledge to the new task. This work presents the convolution neural network-based model to predict and analysis the potato plant disease using plant village datasets with deep learning algorithms. Transfer learning with feature extraction model is employed to detect the potato plant disease. The results show that improved performance with an accuracy of 98.16%, precision of 98.18%, the recall value of 98.17% and the F1 score value of 98.169 %.

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Plant Disease Classification using Deep Learning Google Net Model

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i1051.0789s19 ◽

2019 ◽

Vol 8 (9S) ◽

pp. 319-322

Keyword(s):

Deep Learning ◽

Food Production ◽

Plant Disease ◽

Plant Diseases ◽

Disease Classification ◽

Disease Detection ◽

Crop Varieties ◽

Growing Age ◽

Crop Types ◽

Harvesting Stage

Plant diseases have been a major crisis that is disturbing the food production. So there is a need to provide proper procedures for plant disease detection at its growing age and also during harvesting stage. Timely disease detection can help the user to respond instantly and sketch for some defensive actions. This detection can be carried out without human intervention by using plant leaf images. Deep learning is progressively best for image detection and classification. In this effort, a deep learning based GoogleNet architecture is used for plant diseases detection. The model is trained using public database of 54,306 images of 14 crop varieties and their respective diseases. It achieves 97.82% accuracy for 14 crop types making it capable of further deployment in a crop detection and protection application.

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Detection and Classification of Leaf Disease Using Deep Neural Network

10.4018/978-1-7998-8161-2.ch004 ◽

2022 ◽

pp. 51-77

Author(s):

Meeradevi ◽

Monica R. Mundada ◽

Shilpa M.

Keyword(s):

Neural Network ◽

Plant Disease ◽

Early Stage ◽

Crop Productivity ◽

Attention Mechanism ◽

Disease Classification ◽

Production Plant ◽

Leaf Disease ◽

Proposed Model

Modern technologies have improved their application in field of agriculture in order to improve production. Plant diseases are harmful to plant growth, which leads to reduced quality and quantity of crop. Early identification of plant disease will reduce the loss of the crop productivity. So, it is necessary to identify and diagnose the disease at an early stage before it spreads to the entire field. In this chapter, the proposed model uses VGG16 with attention mechanism for leaf disease classification. This model makes use of convolution neural network which consist of convolution block, max pool layer, and fully connected layer with softmax as an activation function. The proposed approach integrates CNN with attention mechanism to focus more on the diseased part of leaf and increase the classification accuracy. The proposed model design is a novel deep learning model to perform the fine tuning in the classification of nine different type of tomato plant disease.

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

AI ◽

10.3390/ai2030026 ◽

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.

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A Convolutional Neural Network based System to Detect Plant Disease

Webology ◽

10.14704/web/v18si04/web18175 ◽

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.

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