Identification Of Plant Diseases Research Articles

Resnet-based modified red deer optimization with DLCNN classifier for plant disease identification and classification

The manual inspections of plant diseases resulted in low accuracy with high time consumption and unable to predict the multiple diseases of plants. To address these difficulties, it is necessary to develop automated systems that are capable of effectively classifying. Therefore, this article presents a customized PDICNet model for plant leaf disease identification and classification. Initially, ResNet-50 is used to extract multiple features from plant leaf images with colour and texture properties. In addition, the modified Red Deer optimization algorithm (MRDOA) is implemented as an optimal feature selection algorithm to obtain optimized and salient features with a reduced size of the MRDOA. Further, a deep learning convolutional neural network (DLCNN) classifier model is utilized to achieve enhanced classification performance. Obtained simulation outcome discloses the superiority of proposed PDICNet model with an accuracy and F1-score of 99.73%, and 99.78%, respectively for PlantVillage dataset and 99.68%, and 99.71% for Rice Plant dataset.

Smart farming for detection and identification of tomato plant diseases using light weight deep neural network

Smart farming for detection and identification of tomato plant diseases using light weight deep neural network

An AI and Cloud Based Collaborative Platform for PlantDisease Identification, Tracking and Forecasting for Farmers

Plant diseases are a major threat to farmers, consumers, environment and the global economy. In India alone, 35% of field crops are lost to pathogens and pests causing lossesto farmers. Indiscriminate use of pesticides is also a serious health concern as many are toxic and biomagnified. These adverse effects can be avoided by early disease detection, crop surveillance and targeted treatments. Most diseases are diagnosed by agricultural experts by examining external symptoms. However, farmers have limited access to experts. Our project is the first integrated and collaborative platform for automated disease diagnosis, tracking and forecasting. Farmers can instantly and accurately identify diseases and get solutions with a mobile app by photographing affected plant parts. Real- time diagnosis is enabled using the latest Artificial Intelligence (AI) algorithms for Cloud-based image processing. The AI model continuously learns from user uploaded images and expert suggestions to enhance its accuracy. Farmers can also interact with local expertsthrough the platform. For preventive measures, disease density maps with spread forecasting are rendered from a Cloud based repository of geo-tagged images and micro-climactic factors. A web interface allows experts to perform disease analytics with geographical visualizations. In our experiments, the AI model (CNN) was trained with large disease datasets, created with plant images self-collected from many farms over 7 months. Test images were diagnosed using the automated CNN model and the results were validated by plant pathologists. Over 95% disease identification accuracy was achieved. Our solution is a novel, scalable and accessible tool for disease management of diverse agricultural crop plants and can be deployed as a Cloud based service for farmers and experts for ecologically sustainable crop production.

Identification of plant leaf diseases by deep learning based on channel attention and channel pruning.

Plant diseases cause significant economic losses and food security in agriculture each year, with the critical path to reducing losses being accurate identification and timely diagnosis of plant diseases. Currently, deep neural networks have been extensively applied in plant disease identification, but such approaches still suffer from low identification accuracy and numerous parameters. Hence, this paper proposes a model combining channel attention and channel pruning called CACPNET, suitable for disease identification of common species. The channel attention mechanism adopts a local cross-channel strategy without dimensionality reduction, which is inserted into a ResNet-18-based model that combines global average pooling with global max pooling to effectively improve the features' extracting ability of plant leaf diseases. Based on the model's optimum feature extraction condition, unimportant channels are removed to reduce the model's parameters and complexity via the L1-norm channel weight and local compression ratio. The accuracy of CACPNET on the public dataset PlantVillage reaches 99.7% and achieves 97.7% on the local peanut leaf disease dataset. Compared with the base ResNet-18 model, the floating point operations (FLOPs) decreased by 30.35%, the parameters by 57.97%, the model size by 57.85%, and the GPU RAM requirements by 8.3%. Additionally, CACPNET outperforms current models considering inference time and throughput, reaching 22.8 ms/frame and 75.5 frames/s, respectively. The results outline that CACPNET is appealing for deployment on edge devices to improve the efficiency of precision agriculture in plant disease detection.

An Empirical Survey of Machine Learning Based Plant Disease Prediction Models

The occurrence of diseases in plants badly impacts the agricultural production, which increases the food insecurity when the diseases are left undetected. Particularly important for ensuring the availability of production of agricultural and food are the major crops, such as maize, rice, and others. Effective control and prevention of diseases in plants are based on disease forecasting and early warning, which is essential for managing and making decisions regarding agricultural productivity. In rural parts of developing nations, observations by knowledgeable providers remain the main method for plant disease identification as of yet. This draws researchers in for ongoing experienced monitoring, which may be cost-prohibitive on large farms. Besides, in some remote areas, farmers require the assistance of the agricultural experts, which is the expensive and time-consuming process. Hence, automatic disease identification for plants is important to promote the monitoring of large crop fields, which encourages the contribution of the accurate, less-expensive, automatic, and fast technique to perform the detection of diseases in plants. In this survey, the automatic detection methods used for the plant disease detection based on the deep learning methods are discussed. The importance of the deep learning methods for the detection of disease is demonstrated through the schematic sketch on the other basic machine learning techniques in agricultural applications.

A weight optimization-based transfer learning approach for plant disease detection of New Zealand vegetables.

Deep learning (DL) is an effective approach to identifying plant diseases. Among several DL-based techniques, transfer learning (TL) produces significant results in terms of improved accuracy. However, the usefulness of TL has not yet been explored using weights optimized from agricultural datasets. Furthermore, the detection of plant diseases in different organs of various vegetables has not yet been performed using a trained/optimized DL model. Moreover, the presence/detection of multiple diseases in vegetable organs has not yet been investigated. To address these research gaps, a new dataset named NZDLPlantDisease-v2 has been collected for New Zealand vegetables. The dataset includes 28 healthy and defective organs of beans, broccoli, cabbage, cauliflower, kumara, peas, potato, and tomato. This paper presents a transfer learning method that optimizes weights obtained through agricultural datasets for better outcomes in plant disease identification. First, several DL architectures are compared to obtain the best-suited model, and then, data augmentation techniques are applied. The Faster Region-based Convolutional Neural Network (RCNN) Inception ResNet-v2 attained the highest mean average precision (mAP) compared to the other DL models including different versions of Faster RCNN, Single-Shot Multibox Detector (SSD), Region-based Fully Convolutional Networks (RFCN), RetinaNet, and EfficientDet. Next, weight optimization is performed on datasets including PlantVillage, NZDLPlantDisease-v1, and DeepWeeds using image resizers, interpolators, initializers, batch normalization, and DL optimizers. Updated/optimized weights are then used to retrain the Faster RCNN Inception ResNet-v2 model on the proposed dataset. Finally, the results are compared with the model trained/optimized using a large dataset, such as Common Objects in Context (COCO). The final mAP improves by 9.25% and is found to be 91.33%. Moreover, the robustness of the methodology is demonstrated by testing the final model on an external dataset and using the stratified k-fold cross-validation method.

Deep neural network based interactive fuzzy Bayesian search algorithm for low‐cost smart farming automation model

SummaryOne of the most significant factors that influence the globalized economy is agriculture. In order to address the requirement of increasing populations in terms of food necessities, modernizations and technological progressions in agriculture, it is necessary to implement a smart agricultural system. Various traditional techniques are still utilized by the farmers and their intuition in agriculture is not enough to furnish and deliver various issues namely soil management, plant disease identification, weed management, irrigation management, and so forth. Therefore, in this paper, a low‐cost smart farming automation system is evaluated and presented. Since the development of a smart farming automation system minimizes the labor cost and enhances agricultural production level, this paper proposes a deep neural network based Interactive fuzzy Bayesian search (DNN‐IFBS) algorithm for a low‐cost smart farming automation system. In addition to this, the crop water stress index (CWSI) is computed to determine the water status of the plant by employing solar irradiation, canopy temperature, and so forth. The data for analysis is collected simultaneously from three cameras containing image resolutions of about 650 × 460 pixels each. Two different plants namely the melon and sesame are utilized as datasets for experimentation. Finally, the performances of the proposed approach are determined according to the statistical performance measures namely accuracy, specificity as well as F‐measure. The comparative analysis is carried out to evaluate the effectiveness of the proposed system. From the evaluation results, the accuracy rate obtained for the proposed approach is 98.7%.

An optimal hybrid multiclass SVM for plant leaf disease detection using spatial Fuzzy C-Means model

In recent times, plant disease detection and diagnosing measures become a major concern in the agriculture field. Earlier identification of plant disease aids the farmers to take precautionary measures thereby preventing the spread of disease to other parts of plants. Based on the severity of the diseases, the plant may undergo an attack from mild to complete destruction. So, to avoid such destruction, plant diseases should be detected at the initial stage. Therefore, this paper proposes a novel hybrid random forest Multiclass SVM (HRF-MCSVM) design for plant foliar disease detection. To improve the computation accuracy, the image features are preprocessed and segmented using Spatial Fuzzy C-Means prior to the classification process. The Plant Village dataset used consists of a total of 54,303 healthy and diseased leaf images. Finally, the performance metrics like accuracy, F-measure, specificity, sensitivity, and recall value were evaluated to determine the effectiveness of the system. The proposed HRF-MCSVM method is compared with a few existing techniques to determine its efficiency.

Deep Convolutional Neural Networks for image based tomato leaf disease detection

Plant leaf diseases are a major significant risk to food security. In many situation the agriculture production may be reduced, which consequently reduces the nation’s economy, if the crops get affected due to diseases. Generally, diseases affect the leaves of the crops which should be identified in the early stage so that the quality and quantity of the produce may be increased. To detect the leaf diseases at an early stage and taking proper remedial actions will be more helpful for the farmers. So there is a need for an automatic system for leaf disease recognition that identifies and classifies the leaf diseases at an early stage. The highlights of the objective work focus on the DCNN models of leaf images used and overall performance according to the performance metrics that are been applied for plant disease identification. During the past decade many researchers are focusing on leaf disease recognition by proposing various methods and techniques using traditional image processing and machine learning techniques. This motivation of the proposed DCNN work is suited for increasing performance accuracy and minimizing response time in the identification of tomato leaf diseases. In this paper we have proposed an automatic system for leaf disease identification in tomato leaves using Deep Convolutional Neural Network (CNN) since, DCNN focuses on agriculture areas during recent years. In the proposed work we have used 18160 images of tomato leaf diseases which are collected from plant village data set. We have split the dataset that contains 60% of images from the dataset for training and 40% of images for testing. With our proposed DCNN model we have obtained 98.40% of accuracy for the testing set.

Rice plant disease classification using dilated convolutional neural network with global average pooling

The Indian economy is thought to be primarily dependent on agriculture. In plants with various climatic circumstances, illness is highly prevalent and natural. As a result, the quality of crop deteriorates. Getting the best quality and quantity of harvest is farmers' most challenging task due to recent changes in weather cycles. Crop diseases must be identified and prevented as soon as possible to improve productivity. Deep learning is an artificial intelligence branch. It has been actively discussed in academic and industrial circles in recent days, because of the advantages of autonomous learning and feature extraction. The use of learning-based algorithms in plant leaf disease recognition can help to strengthen the objectivity of plant leaf disease feature extraction, minimize the limitations of intentionally selecting disease spot features, and speed up the study. In this paper, we examine existing approaches to detecting plant leaf disease using deep learning and high-end imaging methods, as well as their challenges. We anticipate that our research will be useful to researchers interested in plant disease identification. The traditional CNN has the issue of using up too much computational power. To address this issue, this research developed a DCNN (Dilated Convolutional Neural Network) model with Global Average Pooling (GAP), which will be constructed by changing regular CNN convolution kernels with dilated convolution kernels and the fully connected layer in traditional CNN replaced by Global Average Pooling. The dilated convolution gives the advantages of less computational cost and reduced memory usage then GAP avoids overfitting. These two new concepts are implemented with CNN and the results of this method is compared with other learning and hybrid learning methods using performance metric such as precision, recall, f1-score and accuracy. The classification includes four classes such as bacterial blight, blast, brown spot and turgo. The performance metrics shows that, in the same experimental setup, the DCNN model with GAP improves training accuracy by 5.49 percent on average, compared to the classic CNN model and the results are compared with other learning and hybrid learning methods.

Deep Learning-Based Leaf Disease Detection in Crops Using Images for Agricultural Applications

The agricultural sector plays a key role in supplying quality food and makes the greatest contribution to growing economies and populations. Plant disease may cause significant losses in food production and eradicate diversity in species. Early diagnosis of plant diseases using accurate or automatic detection techniques can enhance the quality of food production and minimize economic losses. In recent years, deep learning has brought tremendous improvements in the recognition accuracy of image classification and object detection systems. Hence, in this paper, we utilized convolutional neural network (CNN)-based pre-trained models for efficient plant disease identification. We focused on fine tuning the hyperparameters of popular pre-trained models, such as DenseNet-121, ResNet-50, VGG-16, and Inception V4. The experiments were carried out using the popular PlantVillage dataset, which has 54,305 image samples of different plant disease species in 38 classes. The performance of the model was evaluated through classification accuracy, sensitivity, specificity, and F1 score. A comparative analysis was also performed with similar state-of-the-art studies. The experiments proved that DenseNet-121 achieved 99.81% higher classification accuracy, which was superior to state-of-the-art models.

AUTOMATIC AGRICULTURAL CROP MAINTENANCE SYSTEM USINGMACHINE LEARNING

Plant diseases serve as a major threat to food supply. The proposed system helps in identification of plant disease and provides remedies that can be used as a defense mechanism against the disease. The database obtained from the Internet is properly segregated and the different plant species are identified and are renamed to form a proper database then obtain test-database which consists of various plant diseases that are used for checking the accuracy and confidence level of the project. Then using training data we will train our classifier and then output will be predicted with optimum accuracy. We use Convolution Neural Network (CNN) which comprises of different layers which are used for prediction.

Optimized lightweight CA-transformer: Using transformer for fine-grained visual categorization

As a rapidly developing research direction in computer vision (CV), related algorithms such as image classification and object detection have achieved inevitable research progress. Improving the accuracy and efficiency of algorithms for fine-grained identification of plant diseases and birds in agriculture is essential to the dynamic monitoring of agricultural environments. In this study, based on the computer vision detection and classification algorithm, combined with the architecture and ideas of the CNN model, the mainstream Transformer model was optimized, and then the CA-Transformer (Transformer Combined with Channel Attention) model was proposed to improve the ability to identify and classify critical areas. The main work is as follows: (1) The C-Attention mechanism is proposed to strengthen the feature information extraction within the patch and the communication between feature information so that the entire network can be fully attentive while reducing the computational overhead; (2) The weight-sharing method is proposed to transfer parameters between different layers, improve the reusability of model data, and at the same time increase the knowledge distillation link to reduce problems such as excessive parameters and overfitting; (3) Token Labeling is proposed to generate score labels according to the position of each Token, and the total loss function of this study is proposed according to the CA-Transformer model structure. The performance of the CA-Transformer model proposed in this study is compared with the current mainstream models on datasets of different scales, and ablation experiments are performed. The results show that the accuracy and mIoU of the CA-Transformer proposed in this study reach 82.89% and 53.17MS, respectively, and have good transfer learning ability, indicating that the model has good performance in fine-grained visual categorization tasks and can be used in ecological information. In the context of more diverse ecological information, this study can provide reference and inspiration for the practical application of information.

Identification of Plant Diseases Using Multi-Level Classification Deep Model

As plants are exposed to the various environmental elements, they are highly prone to many diseases which affect the crop yield and result in low productivity. Due to the lack of proper care and regular checking for any diseases in plants, severe consequences may be seen in a long-term basis on plants and environments. Agricultural productivity is one of the important factors on which the economy highly depends. Plant pathologists require a reliable and effective method to diagnose the disease effectively. Several physical methods and techniques have been applied to better predict and classify the plant disease. However, we need an automated method to identify and produce as accurate result as possible with minimum time. Previously, many machine learning models were developed, producing limited accuracy. But, using deep learning, improved performance can be achieved for classification of plant diseases. The authors propose the multi-level classification model for plant diseases detection. The accuracy achieved by the proposed model is 96.70%, which is higher than the other models.

Identifying tomato leaf diseases under real field conditions using convolutional neural networks and a chatbot

Tomatoes are an essential crop in Taiwan and in many other countries worldwide. Disease is a major threat to tomato production, and disease identification is the first step to limiting production loss. Conventionally, plant disease identification has relied on naked-eye examination in fields by experienced farmers and culture and microscopic examination in laboratories by plant pathologists. However, as of 2021, the tomato industry in Taiwan is facing a labor shortage, and experienced farmers or pathologists are not always available. This study developed a method for the automatic identification of eight tomato disease and pest categories using images of tomato leaves, convolutional neural networks (CNNs), and a chatbot controller. Approximately 9,000 images of tomato leaves affected by 11 diseases and pests were collected in fields or greenhouses. The images and the respective lesions on the leaves were sorted into eight categories according to the appearance of lesions. Three CNNs—an anomaly detection model (ADM), a disease identification model (DIM), and a leaf mold/powdery mildew II distinguishing model (LPDM)—were respectively trained to detect anomalies (i.e., non-leaf images), to sort the images into the eight categories, and to distinguish between leaf mold and powdery mildew II. The three CNNs were hosted on a cloud service. The chatbot controller was programmed to manage the communication between the CNNs and the users through LINE, a mobile instant messaging application. The trained ADM achieved an accuracy of 97.40% in the detection of anomalous images. The trained DIM achieved an accuracy of 93.63% in the categorization of images into the eight tomato disease and pest categories. The trained LPDM achieved an accuracy of 98.70% in the distinction between leaf mold and powdery mildew II. The proposed system can assist farmers in timely identification of tomato leaf diseases and provide simple suggestions for the treatment and prevention of diseases in the identified category.

A multi-scale cucumber disease detection method in natural scenes based on YOLOv5

Plant diseases are the main factors affecting the agricultural production. At present, improving the efficiency of plant disease identification in natural scenarios is a crucial issue. Due to this significance, this study aims at providing an efficient detection method, which is applicable to disease detection in natural scenes. The proposed MTC-YOLOv5n method is based on the YOLOv5 model, which integrates the Coordinate Attention (CA) and Transformer in order to reduce invalid information interference in the background, and combines a Multi-scale training strategy (MS) and feature fusion network to improve the small object detection accuracy. MTC-YOLOv5n is trained and validated on a self-built cucumber disease dataset. The model size and FLOPs are respectively 4.7 MB and 6.1 G, achieving 84.9 % mAP and FPS up to 143. Compared with the advanced single-stage detection model, the experimental results show that MTC-YOLOv5n has higher detection accuracy and speed, smaller computation and model size. In addition, the proposed model is tested under the interference of strong noise conditions such as dense fog, drizzle and dark light, which shows that the model has strong robustness. Finally, the comprehensive experimental results demonstrate that MTC-YOLOv5n is lightweight, efficient and suitable for deployment to mobile terminals for disease detection in natural scenarios.

Leaf disease image retrieval with object detection and deep metric learning.

Rapid identification of plant diseases is essential for effective mitigation and control of their influence on plants. For plant disease automatic identification, classification of plant leaf images based on deep learning algorithms is currently the most accurate and popular method. Existing methods rely on the collection of large amounts of image annotation data and cannot flexibly adjust recognition categories, whereas we develop a new image retrieval system for automated detection, localization, and identification of individual leaf disease in an open setting, namely, where newly added disease types can be identified without retraining. In this paper, we first optimize the YOLOv5 algorithm, enhancing recognition ability in small objects, which helps to extract leaf objects more accurately; secondly, integrating classification recognition with metric learning, jointly learning categorizing images and similarity measurements, thus, capitalizing on prediction ability of available image classification models; and finally, constructing an efficient and nimble image retrieval system to quickly determine leaf disease type. We demonstrate detailed experimental results on three publicly available leaf disease datasets and prove the effectiveness of our system. This work lays the groundwork for promoting disease surveillance of plants applicable to intelligent agriculture and to crop research such as nutrition diagnosis, health status surveillance, and more.

Image Information Contribution Evaluation for Plant Diseases Classification via Inter-Class Similarity

Combineingplant diseases identification and deep learning algorithm can achieve cost-effective prevention effect, and has been widely used. However, the current field of intelligent plant diseases identification still faces the problems of insufficient data and inaccurate classification. Aiming to resolve these problems, the present research proposes an image information contribution evaluation method based on the analysis of inter-class similarity. Combining this method with the active learning image selection strategy can provide guidance for the collection and annotation of intelligent identification datasets of plant diseases, so as to improve the recognition effect and reduce the cost. The method proposed includes two modules: the inter-classes similarity evaluation module and the image information contribution evaluation module. The images located on the decision boundary between high similarity classes will be analysis as high information contribution images, they will provide more information for plant diseases classification. In order to verify the effectiveness of this method, experiments were carried on the fine-grained classification dataset of tomato diseases. Experimental results confirm the superiority of this method compared with others. This research is in the field of plant disease classification. For the detection and segmentation, further research is advisable.

Design and Development of IoT Based Remote Sensing System for Smart Farming

Abstract: Now a day’s a remote sensing system in IoT based environment widely used in smart farming. This system in smart farming is used to perform various farming task like soil sensing, temperature sensing, weather sensing, light sensing, and moisture sensing smarter and efficient for better yielding and production. The proposed system aims to design and develop a remote sensing tool that senses the current status of plant and disease in the plants in farm field using various sensors like temperature, humidity, pH sensors and water level sensors. Identification of plant disease is done through sensor readings data. The main aim of the research paper is to design and development of an Internet of Things (IoT) based remote sensing system tool to supervise and control the citrus plant in farm field. This proposed system consists of hardware setup with real-time sensors such as soil moisture, sunlight, humidity, temperature, water level, and camera module which are controlled by Raspberry Pi processor with Python and configured essential getaways to sense and stored data associated to parameters for supervising the dynamic plant growth. With the help of such kind of IoT based remote sensing system farmer can observe all the parameters of plant remotely and get real time analysis of data, so that they can increase the productivity.

GLCM and LSTM Recurrent Neural Networks Integrated with Machine Learning Techniques to Identify Plant Disease

Plant diseases are very impactful towards the overall effectiveness and quality management of the agricultural sector. In recent years, deep learning methods have been used as a way to identify these diseases, based on neural networks. The study presents GLCM and LSTM Recurrent Neural Networks Integrated with Machine Learning towards the identification of plant diseases. It has been found that the process is very accurate and can assess diverse plants disease characteristics dataset as well.