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.

Plant growth-promoting rhizobacteria(PGPR)and its mechanisms against plant diseases for sustainable agriculture and better productivity

Plant growth-promoting rhizobacteria(PGPR)are specialized bacterial communities inhabiting the root rhizosphere and the secretion of root exudates helps to,regulate the microbial dynamics and their interactions with the plants.These bacteria viz.,Agrobacterium,Arthobacter,Azospirillum,Bacillus,Burkholderia,Flavobacterium,Pseudomonas,Rhizobium,etc.,play important role in plant growth promotion.In addition,such symbiotic associations of PGPRs in the rhizospheric region also confer protection against several diseases caused by bacterial,fungal and viral pathogens.The biocontrol mechanism utilized by PGPR includes direct and indirect mechanisms direct PGPR mechanisms include the production of antibiotic,siderophore,and hydrolytic enzymes,competition for space and nutrients,and quorum sensing whereas,indirect mechanisms include rhizomicrobiome regulation via.secretion of root exudates,phytostimulation through the release of phytohormones viz.,auxin,cytokinin,gibberellic acid,1-aminocyclopropane-1-carboxylate and induction of systemic resistance through expression of antioxidant defense enzymes viz.,phenylalanine ammonia lyase(PAL),peroxidase(PO),polyphenyloxidases(PPO),superoxide dismutase(SOD),chitinase andβ-glucanases.For the suppression of plant diseases potent bio inoculants can be developed by modulating the rhizomicrobiome through rhizospheric engineering.In addition,understandings of different strategies to improve PGPR strains,their competence,colonization efficiency,persistence and its future implications should also be taken into consideration.

Advances in Measures of Reducing Chemical Pesticides to Control Plant Diseases

In order to provide the technological support for further implementing measures of reducing chemical pesticide to control plant diseases,the research progress on non-chemical pesticide measures to control plant diseases are reviewed from the aspects of agricultural control,botanical pesticide control and microbial pesticide control,and the development prospects are proposed,including accelerating innovative research on botani-cal pesticide control such as Chinese herb extracts,and screening microbial pesticides from valuable bio-control bacteria or plant endophyte metabolites for commercial production and utilization.

Enhanced Disease Identification Model for Tea Plant Using Deep Learning

Tea plant cultivation plays a significant role in the Indian economy.The Tea board of India supports tea farmers to increase tea production by preventing various diseases in Tea Plant.Various climatic factors and other parameters cause these diseases.In this paper,the image retrieval model is developed to identify whether the given input tea leaf image has a disease or is healthy.Automation in image retrieval is a hot topic in the industry as it doesn’t require any form of metadata related to the images for storing or retrieval.Deep Hashing with Integrated Autoencoders is our proposed method for image retrieval in Tea Leaf images.It is an efficient andflexible way of retrieving Tea Leaf images.It has an integrated autoencoder which makes it better than the state-of-the-art methods giving better results for the MAP(mean average precision)scores,which is used as a parameter to judge the efficiency of the model.The autoencoders used with skip connections increase the weightage of the prominent features present in the previous tensor.This constitutes a hybrid model for hashing and retrieving images from a tea leaf data set.The proposed model will examine the input tea leaf image and identify the type of tea leaf disease.The relevant image will be retrieved based on the resulting type of disease.This model is only trained on scarce data as a real-life scenario,making it practical for many applications.

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.

Lightweight Method for Plant Disease Identification Using Deep Learning

In the deep learning approach for identifying plant diseases,the high complexity of the network model,the large number of parameters,and great computational effort make it challenging to deploy the model on terminal devices with limited computational resources.In this study,a lightweight method for plant diseases identification that is an improved version of the ShuffleNetV2 model is proposed.In the proposed model,the depthwise convolution in the basic module of ShuffleNetV2 is replaced with mixed depthwise convolution to capture crop pest images with different resolutions;the efficient channel attention module is added into the ShuffleNetV2 model network structure to enhance the channel features;and the ReLU activation function is replaced with the ReLU6 activation function to prevent the gen-eration of large gradients.Experiments are conducted on the public dataset PlantVillage.The results show that the proposed model achieves an accuracy of 99.43%,which is an improvement of 0.6 percentage points compared to the ShuffleNetV2 model.Compared to lightweight network models,such as MobileNetV2,MobileNetV3,EfficientNet,and EfficientNetV2,and classical convolutional neural network models,such as ResNet34,ResNet50,and ResNet101,the proposed model has fewer parameters and higher recognition accuracy,which provides guidance for deploying crop pest identification methods on resource-constrained devices,including mobile terminals.

Detection and Monitoring of Key Hazard Factors for Quality and Safety of Characteristic Berries and Standardized Production Technology and Application

Blueberry,kiwifruit,Rosa roxburghii,and raspberry are the characteristic fruits planted in Guizhou Province.However,in recent years,harmful factors such as plant diseases and insect pests,pesticides and heavy metal residues have affected the quality and safety of blueberry,kiwifruit,R.roxburghii,raspberry and other berries.These problems mainly include the frequent occurrence of plant diseases and insect pests,pesticide residues and heavy metal pollution,which not only seriously affect the quality and safety of berries,but also restrict the healthy development of berry industry.Therefore,it is very important to study the detection and monitoring of key hazard factors affecting the quality and safety of blueberry,kiwifruit,R.roxburghii and raspberry,as well as the standardized production technology.Using literature analysis,field investigation,questionnaire survey,comprehensive analysis,SWOT analysis,laboratory testing and other methods,this paper made a comprehensive and in-depth study of the berry industry in Guizhou Province.Through the analysis of the current situation of the berry industry in Guizhou Province,the problems and shortcomings in the planting,management,sales and other aspects of the industry were revealed.In order to solve these problems,a series of practical measures were put forward,including strengthening pest control,optimizing pesticide application technology,and strictly controlling heavy metal pollution,so as to ensure the healthy and stable development of berry industry.The implementation of these measures will help to improve the overall quality level of the berry industry in Guizhou Province.

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.

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.

An Optimal Classification Model for Rice Plant Disease Detection

Internet of Things(IoT)paves a new direction in the domain of smart farming and precision agriculture.Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain extent.In smart farming,IoT devices are linked among one another with new technologies to improve the agricultural practices.Smart farming makes use of IoT devices and contributes in effective decision making.Rice is the major food source in most of the countries.So,it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and IoT devices.The development and application of Deep Learning(DL)models in agriculture offers a way for early detection of rice diseases and increase the yield and profit.This study presents a new Convolutional Neural Network-based inception with ResNset v2 model and Optimal Weighted Extreme Learning Machine(CNNIR-OWELM)-based rice plant disease diagnosis and classification model in smart farming environment.The proposed CNNIR-OWELM method involves a set of IoT devices which capture the images of rice plants and transmit it to cloud server via internet.The CNNIROWELM method uses histogram segmentation technique to determine the affected regions in rice plant image.In addition,a DL-based inception with ResNet v2 model is engaged to extract the features.Besides,in OWELM,the Weighted Extreme Learning Machine(WELM),optimized by Flower Pollination Algorithm(FPA),is employed for classification purpose.The FPA is incorporated into WELM to determine the optimal parameters such as regularization coefficient C and kernelγ.The outcome of the presented model was validated against a benchmark image dataset and the results were compared with one another.The simulation results inferred that the presented model effectively diagnosed the disease with high sensitivity of 0.905,specificity of 0.961,and accuracy of 0.942.

Interaction Between Ustilaginoidea virens and Rice and Its Sustainable Control

Ustilaginoidea virens is a common rice pathogen that can easily lead to a decline in rice quality and the production of toxins pose potential risks to human health.In this review,we present a comprehensive literature review of research since the discovery of rice false smut.We provide a comprehensive and,at times,critical overview of the main results and findings from related research,and propose future research directions.Firstly,we delve into the interaction between U.virens and rice,including the regulation of transcription factors,the process of U.virens infecting rice panicles,and the plant immune response caused by rice infection.Following that,we discuss the identification and characterization of mycotoxins produced by the pathogenic fungus,as well as strategies for disease management.We emphasize the importance of comprehensive agricultural prevention and control methods for the sustainable management of U.virens.This knowledge will update our understanding of the interaction between U.virens and rice plants,offering a valuable perspective for those interested in U.virens.

PeachNet: Peach Diseases Detection for Automatic Harvesting

To meet the food requirements of the seven billion people on Earth,multiple advancements in agriculture and industry have been made.The main threat to food items is from diseases and pests which affect the quality and quantity of food.Different scientific mechanisms have been developed to protect plants and fruits from pests and diseases and to increase the quantity and quality of food.Still these mechanisms require manual efforts and human expertise to diagnose diseases.In the current decade Artificial Intelligence is used to automate different processes,including agricultural processes,such as automatic harvesting.Machine Learning techniques are becoming popular to process images and identify different objects.We can use Machine Learning algorithms for disease identification in plants for automatic harvesting that can help us to increase the quantity of the food produced and reduce crop losses.In this paper,we develop a novel Convolutional Neural Network(CNN)model that can detect diseases in peach plants and fruits.The proposed method can also locate the region of disease and help farmers to find appropriate treatments to protect peach crops.For the detection of diseases in Peaches VGG-19 architecture is utilized.For the localization of disease regions Mask R-CNN is utilized.The proposed technique is evaluated using different techniques and has demonstrated 94%accuracy.We hope that the system can help farmers to increase peach production to meet food demands.

Main Diseases and Pests of Jujube and Control Strategies in Shanxi

The main diseases and pests in the major growing area of jujube in Shanxi Province in recent years are investigated and studied,and several main diseases and pests are described.Based on the green prevention and control concept of crop diseases and pests proposed by the Ministry of Agriculture of China,the prevention and control of jujube diseases and pests are expounded from the perspectives of strengthening forecast,agricultural management,biological control and chemical control,in order to provide scientific basis for green development of jujube industry.

Review of Studies on Rare Earth against Plant Disease

Agricultural application of rare earth (RE) has been generalized for several decades, and it is involved in crops, vegetables and stock raising in China. However, all the researches on RE mainly focus on the fields such as plant physiological activity, physiological and biochemical mechanism, sanitation toxicology and environmental security. Plant protection by using RE and the induced resistance of plant against diseases were summarized. The mechanism of rare earth against plant disease is highlighted, which includes following two aspects. First, RE elements can control some phytopathogen directly and reduce its virulence to host plant. Another possibility is that RE elements can affect host plant and induce the plant to produce some resistance to disease.

Problems, challenges and future of plant disease management: from an ecological point of view

Plant disease management faces ever-growing challenges due to： （i） increasing demands for total, safe and diverse foods to support the booming global population and its improving living standards; （ii） reducing production potential in agriculture due to competition for land in fertile areas and exhaustion of marginal arable lands; （iii） deteriorating ecology of agro-ecosystems and depletion of natural resources; and （iv） increased risk of disease epidemics resulting from agricultural intensification and monocultures. Future plant disease management should aim to strengthen food security for a stable society while simultaneously safeguarding the health of associated ecosystems and reducing dependency on natural resources. To achieve these multiple functionalities, sustainable plant disease management should place emphases on rational adaptation of resistance, avoidance, elimination and remediation strategies individually and collectively, guided by traits of specific host-pathogen associations using evolutionary ecology principles to create environmental （biotic and abiotic） conditions favorable for host growth and development while adverse to pathogen reproduction and evolution.

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.

Triple bottom-line consideration of sustainable plant disease management:From economic,sociological and ecological perspectives

Plant disease management plays an important role in achieving the sustainable development goals of the United Nations(UN)such as food security,human health,socio-economic improvement,resource conservation and ecological resilience.However,technologies available are often limited due to different interests between producers and society and lacks of proper understanding of economic thresholds and the complex interactions among ecology,productivity and profitability.A comprehensive synergy and conflict evaluation of economic,sociological and ecological effects with technologies,productions and evolutionary principles as main components should be used to guide sustainable disease management that aims to mitigate crop and economic losses in the short term while maintaining functional farm ecosystem in the long term.Consequently,there should be an increased emphasis on technology development,public education and information exchange among governments,researchers,producers and consumers to broaden the options for disease management in the future.

A Computer Software-Epitimulator~ for Simulating Temporal Dynamics of Plant Disease Epidemic Progress

The objective of the present study was to develop a computer software for simulating the temporal development of plant disease epidemics using Richards, logistic, Gompertz, monomolecular, and exponential functions, respectively, and for predicting disease with a fitted model. The software was programmed using Visual Basic （VB6.0） and packaged with the Wise Installation System. The Fibonacci （＇0.618＇） section strategy was used to find out the most appropriate value for the shape parameter （m） in Richards function simulation through looping procedures. The software program was repeatedly tested, debugged and edited until it was run through favorably and produced ideal outputs. It was named Epitimulator based on the phrase ＇epidemic time simulator＇ and has been registered by the National Copyright Department of China （Reg. no. 2007SR18489）. It can be installed and run on personal computers with all versions of Windows operational systems. Data of disease index and survey time are keyed in or imported from Access files. The output of fitted models and related data of parameters can be pasted into Microsoft Excel worksheet or into Word document for editing as required and the simulated disease progress curves can be stored in separate graphic files. After being finally tested and completed, Epitimulator was applied to simulate the epidemic progress of corn northern leaf blight （Exserohilum turcicum） with recorded data from field surveys of corn crops and the fitted models were output. Comparison of the simulation results showed that the disease progress was always best described by Richards function, which resulted in the most accurate simulation model. Result also showed that forecast of northern leaf blight development was highly accurate by using the computed progress model from Richards function.

RDA- CNN: Enhanced Super Resolution Method for Rice Plant Disease Classification

In thefield of agriculture,the development of an early warning diagnostic system is essential for timely detection and accurate diagnosis of diseases in rice plants.This research focuses on identifying the plant diseases and detecting them promptly through the advancements in thefield of computer vision.The images obtained from in-field farms are typically with less visual information.However,there is a significant impact on the classification accuracy in the disease diagnosis due to the lack of high-resolution crop images.We propose a novel Reconstructed Disease Aware–Convolutional Neural Network(RDA-CNN),inspired by recent CNN architectures,that integrates image super resolution and classification into a single model for rice plant disease classification.This network takes low-resolution images of rice crops as input and employs the super resolution layers to transform low-resolution images to super-resolution images to recover appearance such as spots,rot,and lesion on different parts of the rice plants.Extensive experimental results indicated that the proposed RDA-CNN method performs well under diverse aspects generating visually pleasing images and outperforms better than other con-ventional Super Resolution(SR)methods.Furthermore,these super-resolution images are subsequently passed through deep classification layers for disease classi-fication.The results demonstrate that the RDA-CNN significantly boosts the clas-sification performance by nearly 4–6%compared with the baseline architectures.