Plant Diseases Concept in Smart Agriculture Using Deep Learning

2021 ◽  
pp. 139-153
Author(s):  
Prachi Chauhan ◽  
Hardwari Lal Mandoria ◽  
Alok Negi ◽  
R. S. Rajput
Keyword(s):  
Deep Learning ◽  
Agricultural Sector ◽  
Plant Diseases ◽  
Public Image ◽  
Economic Losses ◽  
Treatment Technique ◽  
Convolutional Networks ◽  
Novel Approach ◽  
Technological Advances ◽  
Smart Agriculture

In the agricultural sector, plant leaf diseases and harmful insects represent a major challenge. Faster and more reliable prediction of leaf diseases in crops may help develop an early treatment technique while reducing economic losses considerably. Current technological advances in deep learning have made it possible for researchers to improve the performance and accuracy of object detection and recognition systems significantly. In this chapter, using images of plant leaves, the authors introduced a deep-learning method with different datasets for detecting leaf diseases in different plants and concerned with a novel approach to plant disease recognition model, based on the classification of the leaf image, by the use of deep convolutional networks. Ultimately, the approach of developing deep learning methods on increasingly large and accessible to the public image datasets provides a viable path towards massive global diagnosis of smartphone-assisted crop disease.

scholarly journals A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 81
Author(s):  
Jianbin Xiong ◽  
Dezheng Yu ◽  
Shuangyin Liu ◽  
Lei Shu ◽  
Xiaochan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Plant Species ◽  
Image Recognition ◽  
Recurrent Neural Network ◽  
Plant Diseases ◽  
Learning Methods ◽  
Smart Agriculture ◽  
Important Branch

Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed.

scholarly journals LATE BLIGHT SEGMENTATION IN TOMATOES USING SPATIAL DOMAIN METHODS AND COLOUR ANALYSIS

2020 ◽  
pp. 1-4
Author(s):  
I. Galvan- Torres ◽  
A.S. CortésGonzález ◽  
C.N. López- Mejía ◽  
B. Luna- Benoso ◽  
J.C. MartínezPerales
Keyword(s):  
Late Blight ◽  
Agricultural Sector ◽  
Computer Systems ◽  
Plant Diseases ◽  
Economic Losses ◽  
Computer Assisted ◽  
Color Analysis ◽  
Hsv Color Model ◽  
High Incidence ◽  
Assisted Diagnosis

Agricultural productivity is an important factor in a country's economic development. Therefore, the diagnosis of plant diseases is a field of research of great importance for the agricultural sector since it allows us to help recommend strategies to prevent the spread of diseases, thus reducing economic losses. Currently, with the rise of computer systems, computer systems have been developed that allow computer assisted diagnosis in different fields of research, including the agricultural sector. Since late blight is one of the main diseases due to its high incidence and severity, this paper proposes a methodology to obtain late blight segmentation in tomato leaf images through image analysis and color analysis using the HSV color model. The proposed methodology was applied to a set of publicly available PlantVillage images, to which late blight segmentation was obtained.

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.

scholarly journals Using Deep Learning for Image-Based Potato Tuber Disease Detection

2019 ◽  
Vol 109 (6) ◽  
pp. 1083-1087 ◽  
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.

scholarly journals Detection of Diseases in Tomato Leaves by Color Analysis

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1055
Author(s):  
Benjamín Luna-Benoso ◽  
José Cruz Martínez-Perales ◽  
Jorge Cortés-Galicia ◽  
Rolando Flores-Carapia ◽  
Víctor Manuel Silva-García
Keyword(s):  
Agricultural Sector ◽  
Computer Systems ◽  
Plant Diseases ◽  
Tomato Mosaic Virus ◽  
Economic Losses ◽  
Support Vector ◽  
Computer Assisted ◽  
Final Decision ◽  
Color Analysis ◽  
K Nearest Neighbors

Agricultural productivity is an important factor for the economic development of a country. Therefore, the diagnosis of plant diseases is a field of research of utmost importance for the agricultural sector as it allows us to help recommend strategies to avoid the spread of diseases, thus reducing economic losses. Currently, with the rise of computer systems, computer systems have been developed that allow computer-assisted diagnosis in different research fields, including the agricultural sector. This work proposes the development of a methodology that allows the detection of three types of diseases in tomato leaves (late blight, tomato mosaic virus and Septoria leaf spot) by image analysis and pattern recognition. The methodology is divided into three stages: (1) segmentation of the leaf and of the lesion, (2) feature extraction using color moments and Gray Level Co-occurrence Matrix (GLCM) and (3) classification. For the segmentation process, it is proposed to use a range of pixel colors that represent healthy and diseased areas in tomato leaves using values proposed by an expert in the area of phytopathology. For the classification it is proposed to use a decision rule in which if two of the Support Vector Machines (SVM) classifiers, K Nearest Neighbors (K-NN) and Multilayer Perceptron (MLP) give the same result, then this is taken for the final decision. The result of the methodology is compared with other classifiers using the value of its accuracy and validated with cross validation.

scholarly journals Detection of Plant Diseases Using Convolutional Neural Networks

2021 ◽  
Vol 9 (8) ◽  
pp. 1653-1657
Author(s):  
Krishna Vamsi Kurumaddali
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Agricultural Sector ◽  
Cost Effective ◽  
Plant Diseases ◽  
Crop Disease ◽  
Internal And External Factors ◽  
Technological Improvements

Abstract: Introduction of various technologies like Artificial Intelligence, Image Processing, etc. there has been a significant improvement in the growth of various sectors. They have automated a lot of existing tasks that happen to be difficult to be handled manually thereby reducing the load simultaneously providing precision, efficiency and productivity. This research provides various ways to improve the agricultural sector in terms of productivity and also in terms of efficiency. Image processing of crops for analysis, crop disease detection, etc. are some of the various applications of technology in agriculture. This also provides an effective way of monitoring various internal and external factors like soil fertility, water logging capacity, temperature, etc. Providing a much more cost-effective way of increasing agricultural output and improved efficiency, the implementation of modern technologies improves agricultural sector in various ways. Technological improvements provide the farmers security of their crops getting infected by any pests, being impacted by climate changes, etc. These improvements also reduce the time the farmer needs to spend on the farm by utilizing the concept of deep learning and neural networks. There are various other ways in which technology can benefit the agricultural sectors. Keywords: Agriculture, Artificial Intelligence, Deep Learning, Image Processing, Neural Networks.

scholarly journals Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning

Pathogens ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 131
Author(s):  
Malusi Sibiya ◽  
Mbuyu Sumbwanyambe
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Disease Severity ◽  
Early Stage ◽  
Decision Rules ◽  
Plant Diseases ◽  
Disease Classification ◽  
Rust Disease ◽  
Novel Approach ◽  
The Common

Many applications of plant pathology had been enabled by the evolution of artificial intelligence (AI). For instance, many researchers had used pre-trained convolutional neural networks (CNNs) such as the VGG-16, Inception, and Google Net to mention a few, for the classifications of plant diseases. The trend of using AI for plant disease classification has grown to such an extent that some researchers were able to use artificial intelligence to also detect their severities. The purpose of this study is to introduce a novel approach that is reliable in predicting severities of the maize common rust disease by CNN deep learning models. This was achieved by applying threshold-segmentation on images of diseased maize leaves (Common Rust disease) to extract the percentage of the diseased leaf area which was then used to derive fuzzy decision rules for the assignment of Common Rust images to their severity classes. The four severity classes were then used to train a VGG-16 network in order to automatically classify the test images of the Common Rust disease according to their classes of severity. Trained with images developed by using this proposed approach, the VGG-16 network achieved a validation accuracy of 95.63% and a testing accuracy of 89% when tested on images of the Common Rust disease among four classes of disease severity named Early stage, Middle stage, Late Stage and Healthy stage.

Saliency detection in deep learning era: trends of development

2019 ◽  
pp. 10-36 ◽  
Author(s):  
M. N. Favorskaya ◽  
L. C. Jain
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Event Detection ◽  
Visual Analysis ◽  
Saliency Detection ◽  
Salient Object Detection ◽  
Public Image ◽  
Detection Methods ◽  
Salient Object ◽  
Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

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