Towards Intelligent Detection and Classification of Rice Plant Diseases Based on Leaf Image Dataset

The detection of rice leaf disease is significant because,as an agricultural and rice exporter country,Pakistan needs to advance in production and lower the risk of diseases.In this rapid globalization era,information technology has increased.A sensing system is mandatory to detect rice diseases using Artificial Intelligence(AI).It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases.Deep Neural Network(DNN)is a novel technique that will help detect disease present on a rice leave because DNN is also considered a state-of-the-art solution in image detection using sensing nodes.Further in this paper,the adoption of the mixed-method approach Deep Convolutional Neural Network(Deep CNN)has assisted the research in increasing the effectiveness of the proposed method.Deep CNN is used for image recognition and is a class of deep-learning neural networks.CNN is popular and mostly used in the field of image recognition.A dataset of images with three main leaf diseases is selected for training and testing the proposed model.After the image acquisition and preprocessing process,the Deep CNN model was trained to detect and classify three rice diseases(Brown spot,bacterial blight,and blast disease).The proposed model achieved 98.3%accuracy in comparison with similar state-of-the-art techniques.

Towards Sustainable Agricultural Systems:A Lightweight Deep Learning Model for Plant Disease Detection

A country’s economy heavily depends on agricultural development.However,due to several plant diseases,crop growth rate and quality are highly suffered.Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information.Therefore,the agricultural management system is searching for an automatic early disease detection technique.To this end,an efficient and lightweight Deep Learning(DL)-based framework(E-GreenNet)is proposed to overcome these problems and precisely classify the various diseases.In the end-to-end architecture,a MobileNetV3Smallmodel is utilized as a backbone that generates refined,discriminative,and prominent features.Moreover,the proposed model is trained over the PlantVillage(PV),Data Repository of Leaf Images(DRLI),and a new Plant Composite(PC)dataset individually,and later on test samples,its actual performance is evaluated.After extensive experimental analysis,the proposed model obtained 1.00%,0.96%and 0.99%accuracies on all three included datasets.Moreover,the proposed method achieves better inference speed when compared with other State-Of-The-Art(SOTA)approaches.In addition,a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pretrained models and other Machine Learning(ML)and DL methods.

Plant Disease Detection with Deep Learning and Feature Extraction Using Plant Village

Nowadays, crop diseases are a crucial problem to the world’s food supplies, in a world where the population count is around 7 billion people, with more than 90% not getting access to the use of tools or features that would identify and solve the problem. At present, we live in a world dominated by technology on a significant scale, major network coverage, high-end smart-phones, as long as there are great discoveries and improvements in AI. The combination of high-end smart-phones and computer vision via Deep Learning has made possible what can be defined as “smartphone-assisted disease diagnosis”. In the area of Deep Learning, multiple architecture models have been trained, some achieving performance reaching more than 99.53% [1]. In this study, we evaluate CNN’s architectures applying transfer learning and deep feature extraction. All the features obtained will also be classified by SVM and KNN. Our work is feasible by the use of the open source Plant Village Dataset. The result obtained shows that SVM is the best classifier for leaf’s diseases detection.

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 Fscore)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.

Spectroscopic detection of forest diseases:a review(1970–2020)

Sustainable forest management is essential to confront the detrimental impacts of diseases on forest ecosystems.This review highlights the potential of vegetation spectroscopy in improving the feasibility of assessing forest disturbances induced by diseases in a timely and cost-effective manner.The basic concepts of vegetation spectroscopy and its application in phytopathology are first outlined then the literature on the topic is discussed.Using several optical sensors from leaf to landscape-level,a number of forest diseases characterized by variable pathogenic processes have been detected,identified and quantified in many country sites worldwide.Overall,these reviewed studies have pointed out the green and red regions of the visible spectrum,the red-edge and the early near-infrared as the spectral regions most sensitive to the disease development as they are mostly related to chlorophyll changes and symptom development.Late disease conditions particularly affect the shortwave-infrared region,mostly related to water content.This review also highlights some major issues to be addressed such as the need to explore other major forest diseases and geographic areas,to further develop hyperspectral sensors for early detection and discrimination of forest disturbances,to improve devices for remote sensing,to implement longterm monitoring,and to advance algorithms for exploitation of spectral data.Achieving of these goals will enhance the capability of vegetation spectroscopy in early detection of forest stress and in managing forest diseases.

Plant Disease Diagnosis and Image Classification Using Deep Learning

Indian agriculture is striving to achieve sustainable intensification,the system aiming to increase agricultural yield per unit area without harming natural resources and the ecosystem.Modern farming employs technology to improve productivity.Early and accurate analysis and diagnosis of plant disease is very helpful in reducing plant diseases and improving plant health and food crop productivity.Plant disease experts are not available in remote areas thus there is a requirement of automatic low-cost,approachable and reliable solutions to identify the plant diseases without the laboratory inspection and expert’s opinion.Deep learning-based computer vision techniques like Convolutional Neural Network(CNN)and traditional machine learning-based image classification approaches are being applied to identify plant diseases.In this paper,the CNN model is proposed for the classification of rice and potato plant leaf diseases.Rice leaves are diagnosed with bacterial blight,blast,brown spot and tungro diseases.Potato leaf images are classified into three classes:healthy leaves,early blight and late blight diseases.Rice leaf dataset with 5932 images and 1500 potato leaf images are used in the study.The proposed CNN model was able to learn hidden patterns from the raw images and classify rice images with 99.58%accuracy and potato leaves with 97.66%accuracy.The results demonstrate that the proposed CNN model performed better when compared with other machine learning image classifiers such as Support Vector Machine(SVM),K-Nearest Neighbors(KNN),Decision Tree and Random Forest.

MRMR Based Feature Vector Design for Efficient Citrus Disease Detection

In recent times,the images and videos have emerged as one of the most important information source depicting the real time scenarios.Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane.The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition.One of the application fields pertains to detection of diseases occurring in the plants,which are destroying the widespread fields.Traditionally the disease detection process was done by a domain expert using manual examination and laboratory tests.This is a tedious and time consuming process and does not suffice the accuracy levels.This creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its spreading.The digital images captured from the field’s forms the dataset which trains the machine learning models to predict the nature of the disease.The accuracy of these models is greatly affected by the amount of noise and ailments present in the input images,appropriate segmentation methodology,feature vector development and the choice of machine learning algorithm.To ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent stages.Therefore the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and selection.The training vector thus developed is capable of presenting the relationship between the feature values and the target class.In this article,a highly accurate system model for detecting the diseases occurring in citrus fruits using a hybrid feature development approach is proposed.The overall improvement in terms of accuracy is measured and depicted.

Detection of plant leaf diseases using image segmentation and soft computing techniques

Agricultural productivity is something on which economy highly depends.This is the one of the reasons that disease detection in plants plays an important role in agriculture field,as having disease in plants are quite natural.If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality,quantity or productivity is affected.For instance a disease named little leaf disease is a hazardous disease found in pine trees in United States.Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops,and at very early stage itself it detects the symptoms of diseases i.e.when they appear on plant leaves.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.Image segmentation,which is an important aspect for disease detection in plant leaf disease,is done by using genetic algorithm.

Performance analysis of deep learning CNN models for disease detection in plants using image segmentation

Food security for the 7 billion people on earth requires minimizing crop damage by timely detectionofdiseases.Most deep learningmodels forautomated detectionof diseases in plants suffer fromthe fatal flaw that once tested on independent data,their performance drops significantly.This work investigates a potential solution to this problem by using segmented image data to train the convolutional neural network(CNN)models.As compared to the F-CNN model trained using full images,S-CNN model trained using segmented imagesmore than doubles in performance to 98.6%accuracy when tested on independent data previously unseen by the models even with 10 disease classes.Not only this,by using tomato plant and target spot disease type as an example,we show that the confidence of self-classification for S-CNN model improves significantly over F-CNN model.This research work brings applicability of automated methods closer to non-experts for timely detection of diseases.

Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network

Plants are susceptive to various diseases in their growing phases.Early detection of diseases in plants is one of the most challenging problems in agriculture.If the diseases are not identified in the early stages,then theymay adversely affect the total yield,resulting in a decrease in the farmers'profits.To overcome this problem,many researchers have presented different state-of-the-art systems based on Deep Learning and Machine Learning approaches.However,most of these systems either use millions of training parameters or have lowclassification accuracies.This paper proposes a novel hybrid model based on Convolutional Autoencoder(CAE)network and Convolutional Neural Network(CNN)for automatic plant disease detection.To the best of our knowledge,a hybrid system based on CAE and CNN to detect plant diseases automatically has not been proposed in any state-ofthe-art systems present in the literature.In this work,the proposed hybrid model is applied to detect Bacterial Spot disease present in peach plants using their leaf images,however,it can be used for any plant disease detection.The experiments performed in this paper use a publicly available dataset named PlantVillage to get the leaf images of peach plants.The proposed system achieves 99.35%training accuracy and 98.38%testing accuracy using only 9,914 training parameters.The proposed hybrid model requires lesser number of training parameters as compared to other approaches existing in the literature.This,in turn,significantly decreases the time required to train the model for automatic plant disease detection and the time required to identify the disease in plants using the trained model.

A review of imaging techniques for plant disease detection

Agriculture is the basis of every economy worldwide.Crop production is one of the major factors affecting domestic market condition in any country.Agricultural production is also a major prerequisite of economic development,be it any part of any country.It plays a crucial role as it even provides raw material,employment and food to different citizens.A lot of issues are responsible for estimated crop production varying in different parts of the world.Some of these include overutilization of chemical fertilizers,presence of chemicals in water supply,uneven distribution of rainfall,different soil fertility and others.Other than these issues one of the commonly faced challenges across the globe equally includes destruction of themajor part of production due to diseases.After providing effective resources to the fields,major section of the production is diminished by the presence of diseases in the plants grown.This leads to focus on effective ways of detection of disease in plants.Presence of various diseases in plant is a major concern among farmers.Plant diseases acts as a major threat to small scale farmers as they lead tomajor destruction in overall food supply.To provide effectivemeasures for detection and avoidance of the destruction requires an early identification of type of plant disease present.In recent timemajorwork is being done for the identification of plant disease presents in varied parts of theworld affection varied crops.Majorwork is being done in the domain of identification of causing factors of these diseases.Someof the diseases are marked by the presence of viruses while some are resultant of fungal infection.This becomes a major issuewhen the causing factor is not traceable before it has already spread to major production section.This paper brings a review on effective use of different imaging techniques and computer vision approaches for the identification and classification of plant diseases.Detection of Plant disease is initiated with image acquisition followed by pre-processingwhile using the process of segmentation.It is further accompanied by different techniques used for feature extraction alongwith classification.In this Paper we present the Current Trends and Challenges for detection of plant disease using computer vision and advance imaging technique.

Comparison of CNN-based deep learning architectures for rice diseases classification

Although convolutional neural network(CNN)paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures,few studies have focused on the performance comparison of the applicability of these techniques in detecting and localizing rice diseases.Moreover,most CNN-based rice disease detection studies only considered a small number of diseases in their experiments.Both these shortcomings were addressed in this study.In this study,a rice disease classification comparison of six CNN-based deep-learning architectures(DenseNet121,Inceptionv3,MobileNetV2,resNext101,Resnet152V,and Seresnext101)was conducted using a database of nine of the most epidemic rice diseases in Bangladesh.In addition,we applied a transfer learning approach to DenseNet121,MobileNetV2,Resnet152V,Seresnext101,and an ensemble model called DEX(Densenet121,EfficientNetB7,and Xception)to compare the six individual CNN networks,transfer learning,and ensemble techniques.The results suggest that the ensemble framework provides the best accuracy of 98%,and transfer learning can increase the accuracy by 17%from the results obtained by Seresnext101 in detecting and localizing rice leaf diseases.The high accuracy in detecting and categorisation rice leaf diseases using CNN suggests that the deep CNN model is promising in the plant disease detection domain and can significantly impact the detection of diseases in real-time agricultural systems.This research is significant for farmers in rice-growing countries,as like many other plant diseases,rice diseases require timely and early identification of infected diseases and this research develops a rice leaf detection system based on CNN that is expected to help farmers to make fast decisions to protect their agricultural yields and quality.

An IoT-based agriculture maintenance using pervasive computing with machine learning technique

Purpose-In cultivation,early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates,ensuring that the economy remains balanced.The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification.In this investigation,the accurate prior phase of crop imagery has been collected from different datasets like cropscience,yesmodes and nelsonwisc.In the current study,the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science,yes_modes,nelson_wisc dataset.Design/methodology/approach-In this research work,random forest machine learning-based persuasive plants healthcare computing is provided.If proper ecological care is not applied to early harvesting,it can cause diseases in plants,decrease the cropping rate and less production.Until now different methods have been developed for crop analysis at an earlier stage,but it is necessary to implement methods to advanced techniques.So,the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation.This implemented design is verified on Python 3.7.8 software for simulation analysis.Findings-In this work,different methods are developed for crops at an earlier stage,but more methods are needed to implement methods with prior stage crop harvesting.Because of this,a disease-finding system has been implemented.The methodologies like“Threshold segmentation”and RFO classifier lends 97.8% identification precision with 99.3%real optimistic rate,and 59.823 peak signal-to-noise(PSNR),0.99894 structure similarity index(SSIM),0.00812 machine squared error(MSE)values are attained.Originality/value-The implemented machine learning design is outperformance methodology,and they are proving good application detection rate.