A Convolutional Neural Network Model for Wheat Crop Disease Prediction

Wheat is the most important cereal crop,and its low production incurs import pressure on the economy.It fulfills a significant portion of the daily energy requirements of the human body.The wheat disease is one of the major factors that result in low production and negatively affects the national economy.Thus,timely detection of wheat diseases is necessary for improving production.The CNN-based architectures showed tremendous achievement in the image-based classification and prediction of crop diseases.However,these models are computationally expensive and need a large amount of training data.In this research,a light weighted modified CNN architecture is proposed that uses eight layers particularly,three convolutional layers,three SoftMax layers,and two flattened layers,to detect wheat diseases effectively.The high-resolution images were collected from the fields in Azad Kashmir(Pakistan)and manually annotated by three human experts.The convolutional layers use 16,32,and 64 filters.Every filter uses a 3×3 kernel size.The strides for all convolutional layers are set to 1.In this research,three different variants of datasets are used.These variants S1-70%:15%:15%,S2-75%:15%:10%,and S3-80%:10%:10%(train:validation:test)are used to evaluate the performance of the proposed model.The extensive experiments revealed that the S3 performed better than S1 and S2 datasets with 93%accuracy.The experiment also concludes that a more extensive training set with high-resolution images can detect wheat diseases more accurately.

Remote Sensing and Precision Agriculture Technologies for Crop Disease Detection and Management with a Practical Application Example

Remote sensing technology has long been used to detect and map crop diseases.Airborne and satellite imagery acquired during growing seasons can be used not only for early detection and within-season management of some crop diseases,but also for the control of recurring diseases in future seasons.With variable rate technology in precision agriculture,site-specific fungicide application can be made to infested areas if the disease is stable,although traditional uniform application is more appropriate for diseases that can spread rapidly across the field.This article provides a brief overview of remote sensing and precision agriculture technologies that have been used for crop disease detection and management.Specifically,the article illustrates how airborne and satellite imagery and variable rate technology have been used for detecting and mapping cotton root rot,a destructive soilborne fungal disease,in cotton fields and how site-specific fungicide application has been implemented using prescription maps derived from the imagery for effective control of the disease.The overview and methodologies presented in this article should provide researchers,extension personnel,growers,crop consultants,and farm equipment and chemical dealers with practical guidelines for remote sensing detection and effective management of some crop diseases.

Crops Leaf Diseases Recognition:A Framework of Optimum Deep Learning Features

Manual diagnosis of crops diseases is not an easy process;thus,a computerized method is widely used.Froma couple of years,advancements in the domain ofmachine learning,such as deep learning,have shown substantial success.However,they still faced some challenges such as similarity in disease symptoms and irrelevant features extraction.In this article,we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases recognition.The proposed architecture consists of five steps.In the first step,data augmentation is performed to increase the numbers of training samples.In the second step,pre-trained DarkNet19 deep model is opted and fine-tuned that later utilized for the training of fine-tuned model through transfer learning.Deep features are extracted from the global pooling layer in the next step that is refined using Improved Cuckoo search algorithm.The best selected features are finally classified using machine learning classifiers such as SVM,and named a few more for final classification results.The proposed architecture is tested using publicly available datasets–Cucumber National Dataset and Plant Village.The proposed architecture achieved an accuracy of 100.0%,92.9%,and 99.2%,respectively.Acomparison with recent techniques is also performed,revealing that the proposed method achieved improved accuracy while consuming less computational time.

Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis

It is necessary to quantitatively identify different diseases and nitrogen-water stress for the guidance in spraying specific fungicides and fertilizer applications.The winter wheat diseases,in combination with nitrogen-water stress,are therefore common causes of yield loss in winter wheat in China.Powdery mildew(Blumeria graminis)and stripe rust(Puccinia striiformis f.sp.Tritici)are two of the most prevalent winter wheat diseases in China.This study investigated the potential of continuous wavelet analysis to identify the powdery mildew,stripe rust and nitrogen-water stress using canopy hyperspectral data.The spectral normalization process was applied prior to the analysis.Independent t-tests were used to determine the sensitivity of the spectral bands and vegetation index.In order to reduce the number of wavelet regions,correlation analysis and the independent t-test were used in conjunction to select the features of greatest importance.Based on the selected spectral bands,vegetation indices and wavelet features,the discriminate models were established using Fisher’s linear discrimination analysis(FLDA)and support vector machine(SVM).The results indicated that wavelet features were superior to spectral bands and vegetation indices in classifying different stresses,with overall accuracies of 0.91,0.72,and 0.72 respectively for powdery mildew,stripe rust and nitrogen-water by using FLDA,and 0.79,0.67 and 0.65 respectively by using SVM.FLDA was more suitable for differentiating stresses in winter wheat,with respective accuracies of 78.1%,95.6%and 95.7%for powdery mildew,stripe rust,and nitrogen-water stress.Further analysis was performed whereby the wavelet features were then split into high-scale and low-scale feature subsets for identification.The accuracies of high-scale and low-scale features with an overall accuracy(OA)of 0.61 and 0.73 respectively were lower than those of all wavelet features with an OA of 0.88.The detection of the severity of stripe rust using this method showed an enhanced reliability(R^(2)=0.828).

PNN based crop disease recognition with leaf image features and meteorological data

An automatic crop disease recognition method was proposed in this paper,which combined the statistical features of leaf images and meteorological data.The images of infected crop leaves were taken under different environments of the growth periods,temperature and humidity.The methods of image morphological operation,contour extraction and region growing algorithm were adopted for leaf image enhancement and spot image segmentation.From each image of infected crop leaf,the statistical features of color,texture and shape were extracted by image processing,and the optimal meteorological features with the highest accuracy rate were obtained and selected by the attribute reduction algorithm.The fusion feature vector of the image was formed by combining the statistical features and the meteorological features.Then the probabilistic neural networks(PNNs)classifier was adopted to evaluate the classification accuracy.The experimental results on three cucumber diseased leaf image datasets,i.e.,downy mildew,blight and anthracnose,showed that the crop diseases can be effectively recognized by the integrated application of leaf image processing technology,the disease meteorological data and PNNs classifier,and the recognition accuracy rate was higher than 90%,which indicated that the PNNs classifier trained on the disease feature coefficients extracted from the crop disease leaves and meteorological data could achieve higher classification accuracy.

Impact of dataset on the study of crop disease image recognition

Datasets are very important in image recognition research based on machine learning methods.In particular,advanced methods such as deep learning and transfer learning are more dependent on datasets used for training models.The quality of datasets directly affects the final effect of these methods.In the research of crop disease image recognition,due to the complication of the agricultural environment and the variety of crops,datasets are scarce at present.Therefore,more and more researches adopt methods based on transfer learning,which can make up for the lack of data in the target domain with the help of other datasets.Among these methods,the selection of auxiliary domain datasets has great impact on the modeling effect of target domain.In order to clarify the impact of datasets on the research of crop disease image recognition,this study used different deep neural network frameworks to study and compare the effects of different datasets in crop disease image recognition based on transfer learning.The selected datasets include PlantVillage and Image Database for Agricultural Diseases and Pests Research(IDADP),which have been widely used in recent studies.And the selected deep neural network frameworks include ResNet50,InceptionV3,and EfficientNet.In the method of this study,the datasets are preprocessed first,such as data enhancement.After dividing the auxiliary domain and the target domain,the selected deep neural network frameworks are used to pre-train the model on the auxiliary domain dataset.Finally,the parameter-based transfer learning method was used to construct the corresponding crop disease recognition model in the target.In the experiments,multiple different datasets and different models were tested and compared.The results show that when the test set samples and training sample scenarios are consistent,the recognition accuracy of different network frameworks on multiple test sets is generally high.When the scenarios of test set samples and training samples are inconsistent,the recognition of various test sets by different network models cannot obtain ideal results.For the recognition of crop disease images that are collected from the actual cultivation environment,the use of IDADP dataset modeling is better,and it has more practical value in the actual application of crop disease image recognition.

Roles of small RNAs in crop disease resistance

Small RNAs(sRNAs)are a class of short,non-coding regulatory RNAs that have emerged as critical components of defense regulatory networks across plant kingdoms.Many sRNA-based technologies,such as host-induced gene silencing(HIGS),spray-induced gene silencing(SIGS),virus-induced gene silencing(VIGS),artificial microRNA(amiRNA)and synthetic trans-acting siRNA(syn-tasiRNA)-mediated RNA interference(RNAi),have been developed as disease control strategies in both monocot and dicot plants,particularly in crops.This review aims to highlight our current understanding of the roles of sRNAs including miRNAs,heterochromatic siRNAs(hc-siRNAs),phased,secondary siRNAs(phasiRNAs)and natural antisense siRNAs(nat-siRNAs)in disease resistance,and sRNAs-mediated trade-offs between defense and growth in crops.In particular,we focus on the diverse functions of sRNAs in defense responses to bacterial and fungal pathogens,oomycete and virus in crops.Further,we highlight the application of sRNA-based technologies in protecting crops from pathogens.Further research perspectives are proposed to develop new sRNAs-based efficient strategies to breed non-genetically modified(GMO),diseasetolerant crops for sustainable agriculture.

Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data

Crop diseases have a significant impact on plant growth and can lead to reduced yields.Traditional methods of disease detection rely on the expertise of plant protection experts,which can be subjective and dependent on individual experience and knowledge.To address this,the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease identification.In this paper,we propose a novel approach that utilizes a convolutional neural network(CNN)model in conjunction with Inception v3 to identify plant leaf diseases.The research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific diseases.The models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct classes.Through rigorous training and evaluation,the proposed system achieved an impressive accuracy rate of 99%.This mobile application serves as a convenient and valuable advisory tool,providing early detection and guidance in real agricultural environments.The significance of this research lies in its potential to revolutionize plant disease detection and management practices.By automating the identification process through deep learning algorithms,the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual expertise.The integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops.

Microecological Prevention and Control Technology of Facility Strawberry Continuous Cropping Diseases and Its Application Effects

In order to actively develop green ecological strawberry, the authors have studied integrated microecological prevention and control technologies of strawberry continuous cropping diseases, including increasing soil biomass and solar high temperature disinfection during strawberry greenhouse leisure period in summer, biological fertilizer (agent) treatment before colonization, root irrigation treatment with biocontrol agent, as well as appropriate amount of fulvic acid and cytex after colonization, forming a supporting technical system for the microecological prevention and control of soil-borne diseases in facilities. The demonstration application in production shows that the technology has the functions of restoring and enhancing soil biological fertility, enriching beneficial microbial flora, antagonizing harmful bacteria, significantly promoting the growth and development of strawberry, reducing the incidence of soil-borne diseases, reducing the use of chemical fertilizers and pesticides, promoting the early ripening and high yield of strawberry, significantly improving the quality and flavor of strawberry, reducing the risk of pesticide residues, and boosting consumer confidence, which further improves the economic benefit, social benefit and ecological benefit, with good application prospect in production.

Crop Leaf Disease Recognition Network Based on Brain Parallel Interaction Mechanism

In the actual complex environment,the recognition accuracy of crop leaf disease is often not high.Inspired by the brain parallel interaction mechanism,a two-stream parallel interactive convolutional neural network(TSPI-CNN)is proposed to improve the recognition accuracy.TSPI-CNN includes a two-stream parallel network(TSP-Net)and a parallel interactive network(PI-Net).TSP-Net simulates the ventral and dorsal stream.PI-Net simulates the interaction between two pathways in the process of human brain visual information transmission.A large number of experiments shows that the proposed TSPI-CNN performs well on MK-D2,PlantVillage,Apple-3 leaf,and Cassava leaf datasets.Furthermore,the effect of numbers of interactions on the recognition performance of TSPI-CNN is discussed.The experimental results show that as the number of interactions increases,the recognition accuracy of the network also increases.Finally,the network is visualized to show the working mechanism of the network and provide enlightenment for future research.

Physical-biological Collaborative Prevention and Control of Facility Strawberry Continuous Cropping Diseases

The aim was to develop green strawberry varieties,control soil-borne diseases from facility strawberry continuous cropping,improve the safety and quality of strawberries and promote the healthy and stable development of the strawberry industry. Based on the production practices in recent years,we summarized the physical-biological collaborative prevention and control technology for the disease of facility strawberry continuous cropping facilities,established the technical goals,the main types of soil-borne diseases,and the physical-biological collaborative control prevention and control technologies,mainly including the specific methods and technical points of solar high temperature disinfection technology and biological bacterial fertilizer（ agent） treatment technology.

Crop pest image recognition based on the improved ViT method Author links open overlay panel

The crop pests and diseases in agriculture is one of the most important reason for the reduction of bulk grain and oil crops and the decline of fruit and vegetable crop quality,which threaten macroeconomic stability and sustainable development.However,the recognition method based on manual and instruments has been unable to meet the needs of scientific research and production due to its strong subjectivity and low efficiency.The recognition method based on pattern recognition and deep learning can automatically fit image features,and use features to classify and predict images.This study introduced the improved Vision Transformer(ViT)method for crop pest image recognition.Among them,the region with the most obvious features can be effectively selected by block partition.The self-attention mechanism of the transformer can better excavate the special solution that is not an obvious lesion area.In the experiment,data with 7 classes of examples are used for verification.It can be illustrated from results that this method has high accuracy and can give full play to the advantages of image processing and recognition technology,accurately judge the crop diseases and pests category,provide method reference for agricultural diseases and pests identification research,and further optimize the crop diseases and pests control work for agricultural workers in need.

Separation-enrichment method for airborne disease spores based on microfluidic chip

Airborne diseases are likely to cause crop yield reduction,which has aroused widespread concern.In this study,a two-stage separation-enrichment structure microfluidic chip with a compound field for separation and enrichment of the greenhouse crops airborne disease spores directly from gas flow was developed.The chip is mainly composed of three parts:arc structure pretreatment channel,semicircular electrode structure and collection tank.COMSOL 5.1 software was used to simulate the designed microfluidic chip.30μm particles were used to represent P.xanthii spores,25μm particles were used to represent P.cubensis spores,and 16μm particles were used to represent B.cinerea spores.The simulation results showed that the separation and enrichment efficiency of 16μm particles,25μm particles,and 30μm particles was 88%,91%,and 94%,respectively.The experimental verification results were observed under a microscope.The results showed that the separation and enrichment efficiency of B.cinereal spores,P.cubensis spores and P.xanthii spores was 75.7%,83.8%and 89.4%,respectively.As a result,the designed microfluidic chip can be used to separate and enrich the spores of airborne diseases of greenhouse crops,which can provide a basis for the research of real-time monitoring technology for greenhouse airborne diseases.

microRNA-mediated R gene regulation: molecular scabbards for double-edged swords

Plant resistance(R) proteins are immune receptors that recognize pathogen effectors and trigger rapid defense responses, namely effector-triggered immunity. R protein-mediated pathogen resistance is usually race specific. During plant-pathogen coevolution,plant genomes accumulated large numbers of R genes. Even though plant R genes provide important natural resources for breeding disease-resistant crops, their presence in the plant genome comes at a cost. Misregulation of R genes leads to developmental defects, such as stunted growth and reduced fertility. In the past decade, many microRNAs(miRNAs) have been identified to target various R genes in plant genomes. miRNAs reduce R gene levels under normal conditions and allow induction of R gene expression under various stresses. For these reasons, we consider R genes to be double-edged "swords" and miRNAs as molecular "scabbards". In the present review, we summarize the contributions and potential problems of these "swords" and discuss the features and production of the "scabbards", as well as the mechanisms used to pull the "sword" from the "scabbard"when needed.