Classification Of Plant Leaf Diseases Research Articles

A Systematic Analysis of Machine Learning and Deep Learning Based Approaches for Plant Leaf Disease Classification: A Review

Crops’ production and quality of yields are heavily affected by crop diseases which cause adverse impacts on food security as well as economic losses. In India, agriculture is a prime source of income in most rural areas. Hence, there is an intense need to employ novel and accurate computer vision-based techniques for automatic crop disease detection and their classification so that prophylactic actions can be recommended in a timely manner. In literature, numerous computer vision-based techniques by utilizing divergent combinations of machine learning, deep learning, CNN, and various image-processing techniques along with their associated merits and demerits have already been discussed. In this study, we systematically reviewed recent research studies undertaken by a variety of scholars and researchers of fungal and bacterial plant disease detection and classification and summarized them based on vital parameters like type of crop utilized, deep learning/machine learning architecture used, dataset utilized for experiments, performance matrices, types of disease detected and classified, and highest accuracy achieved by the model. As per the analysis carried out, in the category of machine learning-based approaches, 70% of studies utilized real-field plant leaf images and 30% utilized laboratory condition plant leaf images for disease classification while in the case of deep learning-based approaches, 55% studied employed laboratory-conditioned images from the PlantVillage dataset, 25% utilized real-field images, and 20% utilized open image datasets. The average accuracy attained with deep learning-based approaches is quite higher at 98.8% as compared to machine learning-based approaches at 92.2%. In the case of deep learning-based methods, we also analyzed the performances of pretrained and training from scratch models that have been utilized in various studies for plant leaf disease classification. Pretrained models perform better with 99.64% classification accuracy compared to training from scratch models which achieved 98.64% average accuracy. We also highlighted some major issues encountered in the computer vision-based disease detection and classification approach used in literature and provided recommendations that will help and guide researchers to explore new dimensions in crop disease recognition.

Plant leaf disease classification and damage detection system using deep learning models

Plant leaf disease classification and damage detection system using deep learning models

Feature Extraction and Classification of Plant Leaf Diseases Using Deep Learning Techniques

In India’s economy, agriculture has been the most significant contributor. Despite the fact that agriculture’s contribution is decreasing as the world’s population grows, it continues to be the most important source of employment with a little margin of difference. As a result, there is a pressing need to pick up the pace in order to achieve competitive, productive, diverse, and long-term agriculture. Plant disease misinterpretations can result in the incorrect application of pesticides, causing crop harm. As a result, early detection of infections is critical as well as cost-effective for farmers. To diagnose the disease at an earlier stage, appropriate segmentation of the diseased component from the leaf in an accurate manner is critical. However, due to the existence of noise in the digitally captured image, as well as variations in backdrop, shape, and brightness in sick photographs, effective recognition has become a difficult task. Leaf smut, Bacterial blight and Brown spot diseases are segmented and classified using diseased Apple (20), Cercospora (60), Rice (100), Grape (140), and wheat (180) leaf photos in the suggested work. In addition, a superior segmentation technique for the ROI from sick leaves with living backdrop is presented here. Textural features of the segmented ROI, such as 1st and 2nd order WPCA Features, are discovered after segmentation. This comprises 1st order textural features like kurtosis, skewness, mean and variance as well as 2nd procedure textural features like smoothness, energy, correlation, homogeneity, contrast, and entropy. Finally, the segmented region of interest’s textural features is fed into four different classifiers, with the Enhanced Deep Convolutional Neural Network proving to be the most precise, with a 96.1% accuracy.

Optimization of Deep Learning Model for Plant Disease Detection Using Particle Swarm Optimizer

Plant diseases are a major impendence to food security, and due to a lack of key infrastructure in many regions of the world, quick identification is still challenging. Harvest losses owing to illnesses are a severe problem for both large farming structures and rural communities, motivating our mission. Because of the large range of diseases, identifying and classifying diseases with human eyes is not only time-consuming and labor intensive, but also prone to being mistaken with a high error rate. Deep learning-enabled breakthroughs in computer vision have cleared the road for smartphone-assisted plant disease and diagnosis. The proposed work describes a deep learning approach for detection plant disease. Therefore, we proposed a deep learning model strategy for detecting plant disease and classification of plant leaf diseases. In our research, we focused on detecting plant diseases in five crops divided into 25 different types of classes (wheat, cotton, grape, corn, and cucumbers). In this task, we used a public image database of healthy and diseased plant leaves acquired under realistic conditions. For our work, a deep convolutional neural model AlexNet and Particle Swarm optimization was trained for this task we found that the metrics (accuracy, specificity, Sensitivity, precision, and F-score) of the tested deep learning networks achieves an accuracy of 98.83%, specificity of 98.56%, Sensitivity of 98.78%, precision of 98.67%, and F-score of 98.47%, demonstrating the feasibility of this approach.

Plant leaf disease classification using deep neural network

Agriculture is the backbone of Indian economy. Most of the people living in rural areas depend on agriculture for their livelihood. Nevertheless, the farmers are facing a lot of difficulties in crop production due to climatic change. In addition, diseases in plants affect the production of crops drastically. Presently, the farmers identify the plant diseases by visual inspection which, in turn, requires an expert's help and it is a time consuming task. Hence, in this paper, deep learning networks are used to identify different types of diseases in the leaves of different plants. The model is trained with 45,562 images and validated with 8,049 images belonging to 17 categories of diseases. It is fine-tuned with the hyperparameters such as learning rate, epochs, batch size and input image size and then tested with 9,469 images which yield a total classification accuracy of 96.8%.

Lightweight Few Shot Model for Plant Leaf Disease Classification Using an Aggregated Loss Function

Lightweight Few Shot Model for Plant Leaf Disease Classification Using an Aggregated Loss Function

RIC-Net: A plant disease classification model based on the fusion of Inception and residual structure and embedded attention mechanism

In this paper, we proposed a convolutional neural network based on Inception and residual structure with an embedded modified convolutional block attention module (CBAM), aiming to improve the classification of plant leaf diseases. Corn, potatoes and tomatoes are the most cultivated grains in southern China. The leaves of the three crops are very fragile and sensitive and are susceptible to leaf diseases, such as leaf blight of corn, late blight of potato and mosaic virus of tomato. These diseases cannot be identified at early stages. Therefore, an efficient solution is proposed by deep learning techniques to detect the disease categories of crops, which can effectively prevent the spread of diseases and ensure the normal growth of plants. In this experiment, our model achieved an overall accuracy of 99.55% for the identification of the three diseases of corn, potato and tomato. In addition, we tested the three plants individually. The classification accuracy of our model on corn, potato and tomato was 98.44%, 99.43% and 95.20%, respectively. We have also developed a web-based real-time plant disease classification system and deployed our model. The system had good performance in time and accuracy evaluation metrics. The results of this study showed that our model had fewer parameters, shorter training time, and higher recognition accuracy compared to existing image classification models.

Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification

Crop production can be greatly reduced due to various diseases, which seriously endangers food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional classification methods, such as naked-eye observation and laboratory tests, have many limitations, such as being time consuming and subjective. Currently, deep learning (DL) methods, especially those based on convolutional neural network (CNN), have gained widespread application in plant disease classification. They have solved or partially solved the problems of traditional classification methods and represent state-of-the-art technology in this field. In this work, we reviewed the latest CNN networks pertinent to plant leaf disease classification. We summarized DL principles involved in plant disease classification. Additionally, we summarized the main problems and corresponding solutions of CNN used for plant disease classification. Furthermore, we discussed the future development direction in plant disease classification.

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases.

In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.

Plant Leaf Disease Detection and Classification using Optimized CNN Model

Our economy depends on productivity in agriculture. The quantity and quality of the yield is greatly affected by various hazardous diseases. Early-stage detection of plant disease will be very helpful to prevent severe damage. Automatic systems to detect the changes in the plants by monitoring the abnormal symptoms in its growth will be more beneficial for the farmers. This paper presents a system for automatic prediction and classification of plant leaf diseases. The survey on various diseases classification techniques that can be used for plant leaf disease detection are also discussed. The proposed system will define the cropped image of a plant through image processing and feature extraction algorithms. Enhanced CNN model is designed and applied for about 20,600 images are collected as a dataset. Optimization is done to enhance the accuracy in the system prediction and to show the improvement in the true positive samples classification. The proposed system shows the improvement in the accuracy of prediction as 93.18% for three different species with twelve different diseases.

AUTONOMOUS VEHICLE OF WEEDING ROBOT FOR MORE ECOLOGICAL AND ECONOMICAL WEEDING

Agricultural productivity is something on which our economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field. Insect pests are dangerous for the overall growth of the crops. Several Integrated Pest Management strategies have been tailored for the effective management of insect pests under filed condition. But, a prime method to protect the crop may be the regular monitoring of the crop for the occurrence of the insect pests to understand the health of the crop. Early detection of insect pest infestation to evolve suitable management techniques, therefore, would be an important step towards successful implementation of pest control strategies at appropriate time targeting the most vulnerable stage of the insect pest. Further, if the insect pests are detected at their early stage, appropriate measures can be taken to protect the crop from future damage that can be caused by the insect thereby immense crop loss can be minimized. This paper presents an algorithm for image segmentation technique which is used for automatic detection and classification of plant leaf diseases. It also covers survey on different diseases classification techniques that can be used for plant leaf disease detection. The diagnosis of plant disease by computer vision using image processing methodology is a key for timely intervention and treatment of healthy agricultural procedure and to increase the yield by natural means. Digital image processing has three basic steps: processing, analysis and understanding. Image processing contains the pre-processing of the plant leaf as segmentation, color extraction, diseases specific data extraction and filtration of images. Image analysis generally deals with the classification of diseases

A novel pixel replacement-based segmentation and double feature extraction techniques for efficient classification of plant leaf diseases

Diseases in plants accounts to major crop losses which affects the economy of farmers and in turn the country. Recent advancements in technologies like Internet of Things and Machine learning has led to the development of automated systems for crucial applications. This paper focuses on developing such a system for agricultural application mainly for early detection of diseases in plant leaves and provides solutions to the farmers. The main contribution is the novel pixel replacement-based segmentation and double feature extraction techniques for enhancing classification process. Classification is done using Support vector machine classifier and the developed system was validated using pomegranate leaves. The system is evaluated using performance metrics such as detection accuracy and classification accuracy.

Convolutional Neural Networks for Texture Feature Extraction. Applications to Leaf Disease Classification in Precision Agriculture

This paper studies the use of deep-learning models (AlexNet, VggNet, ResNet) pre-trained on object categories (ImageNet) in applied texture classification problems such as plant disease detection tasks. Research related to precision agriculture is of high relevance due to its potential economic impact on agricultural productivity and quality. Within this context, we propose a deep learning-based feature extraction method for the identification of plant species and the classification of plant leaf diseases. We focus on results relevant to real-time processing scenarios that can be easily transferred to manned/unmanned agricultural smart machinery (e.g. tractors, drones, robots, IoT smart sensor networks, etc.) by reconsidering the common processing pipeline. In our approach, texture features are extracted from different layers of pre-trained Convolutional Neural Network models and are later applied to a machine-learning classifier. For the experimental evaluation, we used publicly available datasets consisting of RGB textured images and datasets containing images of healthy and non-healthy plant leaves of different species. We compared our method to feature vectors derived from traditional handcrafted feature extraction descriptors computed for the same images and end-to-end deep-learning approaches. The proposed method proves to be significantly more efficient in terms of processing times and discriminative power, being able to surpass traditional and end-to-end CNN-based methods and provide a solution also to the problem of the reduced datasets available for precision agriculture.

Plant leaf disease classification using deep Convolutional neural network with Bayesian learning

In the present situation, the world's population continues to rise rapidly, while the cultivated land suitable for cultivation stays the same. It forces farmers to demonstrate innovative techniques that improve crop yields to sustain the increasing population. Preserving the health of crops is quite critical in this respect. Therefore, early detection of disease in crops seems to be very crucial to monitor disease and increase crop yield. In this paper, a deep Convolutional Neural Network (DCNN) model is proposed using Bayesian learning process for classification of the diseases in plants. Material used for this work consist of 20,639 images taken from plantVillage having 15 distinct classes of healthy and infected leaf images of potato, tomato and pepper bell. The key objective of this research is the introduction of Bayesian process at the top of residual network for efficient feature learning. An investigation was performed for comparison of proposed DCNN with the traditional classifiers in terms of accuracy, precision, recall and F-score and result indicated that the proposed model is an efficient tool for classification of diseases. Further, the prominent physical or chemical properties of nanomaterials have enabled their use as innovative and high-performance diagnostic toolsfor variedplant pathogens and other important disease biomarkers. Taking these as a pre processing step while acquiring the data set and combining it with proposed work enhances the performance. The proposed approach utilizes the features developed by CNN mostly in different hierarchies to use Bayesian framework and obtains the accuracy rate of 98.9% with no sign of overfitting.

Plant leaf disease classification using deep neural network

Plant leaf disease classification using deep neural network

The Effect of Adaptive Learning Rate on the Accuracy of Neural Networks

Learning rates in gradient descent algorithms have significant effects especially on the accuracy of a Capsule Neural Network (CNN). Choosing an appropriate learning rate is still an issue to date. Many developers still have a problem in selecting a learning rate for CNN leading to low accuracies in classification. This gap motivated this study to assess the effect of learning rate on the accuracy of a developed (CNN). There are no predefined learning rates in CNN and therefore it is hard for researchers to know what learning rate will give good results. This work, therefore, focused on assessing the effect of learning rate on the accuracy of a CNN by using different learning rates and observing the best performance. The contribution of this work is to give an appropriate learning rate for CNNs to improve accuracy during classification. This work has assessed the effect of different learning rates and came up with the most appropriate learning rate for CNN plant leaf disease classification. Part of the images used in this work was from the PlantVillage dataset while others were from the Nepal database. The images were pre-processed then subjected to the original CNN model for classification. When the learning rate was 0.0001, the best performance was 99.4% on testing and 100% on training. When the learning rate was 0.00001, the highest performance was 97% on testing and 99.9% on training. The lowest performance observed was 81% accuracy on testing and 99% on training when the learning rate was 0.001. This work observed that CNN was able to achieve the highest accuracy with a learning rate of 0.0001. The best Convolutional Neural Network accuracy observed was 98% on testing and 100% on training when the learning rate was 0.0001.

Real-time plant health assessment via implementing cloud-based scalable transfer learning on AWS DeepLens.

The control of plant leaf diseases is crucial as it affects the quality and production of plant species with an effect on the economy of any country. Automated identification and classification of plant leaf diseases is, therefore, essential for the reduction of economic losses and the conservation of specific species. Various Machine Learning (ML) models have previously been proposed to detect and identify plant leaf disease; however, they lack usability due to hardware sophistication, limited scalability and realistic use inefficiency. By implementing automatic detection and classification of leaf diseases in fruit trees (apple, grape, peach and strawberry) and vegetable plants (potato and tomato) through scalable transfer learning on Amazon Web Services (AWS) SageMaker and importing it into AWS DeepLens for real-time functional usability, our proposed DeepLens Classification and Detection Model (DCDM) addresses such limitations. Scalability and ubiquitous access to our approach is provided by cloud integration. Our experiments on an extensive image data set of healthy and unhealthy fruit trees and vegetable plant leaves showed 98.78% accuracy with a real-time diagnosis of diseases of plant leaves. To train DCDM deep learning model, we used forty thousand images and then evaluated it on ten thousand images. It takes an average of 0.349s to test an image for disease diagnosis and classification using AWS DeepLens, providing the consumer with disease information in less than a second.

Plant Leaf Disease Classification and Detection System Using Machine Learning

In a developing country like India agriculture plays a noteworthy role. Agricultural intervention in the livelihood of rural India indulges by about 58%. Among the agricultural products, tomato is one of the most used crops. Thus, preventing significant loss in quantity and yield of tomato is majorly dependent on recognition and classification of diseases a tomato plant might possess. Latest and fostering technologies like Image processing is used to rectify such issues using different types of techniques and algorithms. Initially, the leaves of a tomato plant get affected, when plant develops a particular type of disease. In this project, four consecutive stages are used to discover the type of disease. The four stages include pre-processing, leaf segmentation, feature extraction and classification. To remove the noise we are doing the pre-processing and to part the affected or damages area of the leaf, image segmentation is used. The k-nearest neighbors (KNN) algorithm, which is a guided, supervised and advance machine learning algorithm, is implemented to find solutions for both the problems related to classification and regression. During the terminal stage, user is recommended with the treatment. Mostly live plants are adversely affected by the diseases. This paper imparts representation of leaf disease detection employing image processing that can identify drawbacks in tomato plant from images, based on color, bound and texture to give the brisk and reliable results to the farmer.

Plant leaf disease classification using EfficientNet deep learning model

Most plant diseases show visible symptoms, and the technique which is accepted today is that an experienced plant pathologist diagnoses the disease through optical observation of infected plant leaves. The fact that the disease diagnosis process is slow to perform manually and another fact that the success of the diagnosis is proportional to the pathologist's capabilities makes this problem an excellent application area for computer-aided diagnostic systems. Instead of classical machine learning methods, in which manual feature extraction should be flawless to achieve successful results, there is a need for a model that does not need pre-processing and can perform a successful classification. In this study, EfficientNet deep learning architecture was proposed in plant leaf disease classification and the performance of this model was compared with other state-of-the-art deep learning models. The PlantVillage dataset was used to train models. All the models were trained with original and augmented datasets having 55,448 and 61,486 images, respectively. EfficientNet architecture and other deep learning models were trained using transfer learning approach. In the transfer learning, all layers of the models were set to be trainable. The results obtained in the test dataset showed that B5 and B4 models of EfficientNet architecture achieved the highest values compared to other deep learning models in original and augmented datasets with 99.91% and 99.97% respectively for accuracy and 98.42% and 99.39% respectively for precision.

Performance Evaluation of Capsule Networks for Classification of Plant Leaf Diseases

Deep Learning (DL) is a high capable machine learning algorithm which composed the advanced image processing as feature learning and supervised learning with detailed models with many hidden layers and neurons. DL demonstrated its efficiency and robustness in many big data problems, computer vision, and more. Whereas it has an increasing popularity day by day, it has still some deficiencies to construe the relationship between learned feature maps and spatial information. Capsule network (CapsNET) is proposed to overcome the shortcoming by excluding the pooling layer from the architecture and transferring spatial information between layers by capsule. In this paper, CapsNET architecture was proposed to evaluate the performance of the model on classification of plant leaf diseases using simple reduced capsules on leaf images. Plant leaf diseases are common and prevalent diseases that disrupt harvesting and yielding for agriculture. CapsNET has capability of detailed analysis for even small stains that may lead seed dressing time and duration. The proposed CapsNET model aimed at assessing the applicability of various feature learning models and enhancing the learning capacity of the DL models for bell pepper plants. The healthy and diseased leaf images were fed into the CapsNET. The proposed CapsNET model reached high classification performance rates of 95.76%, 96.37%, and 97.49% for accuracy, sensitivity, and specificity, respectively.