Plant Datasets Research Articles

An ultra-lightweight efficient network for image-based plant disease and pest infection detection

Plant diseases and pest infections are major factors that undermine the growth of plants along with their life cycle. Optical image-based plant disease detection provides an efficient and low cost way for real-time plant growth monitoring and management. In recent years, the thriving development of deep learning techniques in a variety of communities has validated its great performance in image interpretation and understanding. Existing deep learning-based methods for plant disease classification mostly adopt convolutional neural networks (CNNs) that have been originally developed for general image classification purposes. These CNN architectures consist of a very large volume of training parameters, which severely hinders its applicability under scenarios requiring fast and flexible deployment on compact devices with limited computation powers. In this paper, an ultra-lightweight efficient network (ULEN) is proposed targeting image-based plant disease and pest infection detection. The proposed network consists of two parts, a deep feature extraction module that adopts residual depth-wise convolution and a classification module receiving multi-scale features enhanced by a spatial pyramid pooling layer. The network is constructed in a very compact design with approximately only 100 000 parameters, which greatly favors the demand for a lightweight model for practical needs. Two publicly available plant datasets collected at the indoor and outdoor environments were tested on two compact devices to validate its applicability under different scenarios. Compared with the state-of-the-art architectures, the proposed network showed superior performance with the least computation complexity and compelling classification accuracy.

An Intelligent and Precise Agriculture Model in Sustainable Cities Based on Visualized Symptoms

Plant diseases represent one of the critical issues which lead to a major decrease in the quantity and quality of crops. Therefore, the early detection of plant diseases can avoid any losses or damage to these crops. This paper presents an image processing and a deep learning-based automatic approach that classifies the diseases that strike the apple leaves. The proposed system has been tested using over 18,000 images from the Apple Diseases Dataset by PlantVillage, including images of healthy and affected apple leaves. We applied the VGG-16 architecture to a pre-trained unlabeled dataset of plant leave images. Then, we used some other deep learning pre-trained architectures, including Inception-V3, ResNet-50, and VGG-19, to solve the visualization-related problems in computer vision, including object classification. These networks can train the images dataset and compare the achieved results, including accuracy and error rate between those architectures. The preliminary results demonstrate the effectiveness of the proposed Inception V3 and VGG-16 approaches. The obtained results demonstrate that Inception V3 achieves an accuracy of 92.42% with an error rate of 0.3037%, while the VGG-16 network achieves an accuracy of 91.53% with an error rate of 0.4785%. The experiments show that these two deep learning networks can achieve satisfying results under various conditions, including lighting, background scene, camera resolution, size, viewpoint, and scene direction.

Hyperspectral Non-Imaging Measurements and Perceptron Neural Network for Pre-Harvesting Assessment of Damage Degree Caused by Septoria/Stagonospora Blotch Diseases of Wheat

The detection and identification of plant diseases is a fundamental task for sustainable crop production. Septoria tritici and Stagonospora nodorum blotch (STB and SNB) are two of the most common diseases of cereal crops that cause significant economic damage. Both pathogens are difficult to identify at early stages of infection. Determining the degree of the disease at a late infection stage is useful for assessing cereal crops before harvesting, as it allows the assessment of potential yield losses. Hyperspectral sensing could allow for automatic recognition of Septoria harmfulness on wheat in field conditions. In this research, we aimed to collect information on the hyperspectral data on wheat plants with different lesion degrees of STB&SNB and to create and train a neural network for the detection of lesions on leaves and ears caused by STB&SNB infection at the late stage of disease development. Spring wheat was artificially infected twice with Septoria pathogens in the stem elongation stage and in the heading stage. Hyperspectral reflections and brightness measurements were collected in the field on wheat leaves and ears on the 37th day after STB and the 30th day after SNB pathogen inoculation using an Ocean Insight “Flame” VIS-NIR hyperspectrometer. Obtained non-imaging data were pre-treated, and the perceptron model neural network (PNN) was created and trained based on a pairwise comparison of datasets for healthy and diseased plants. Both statistical and neural network approaches showed the high quality of the differentiation between healthy and damaged wheat plants by the hyperspectral signature. A comparison of the results of visual recognition and automatic STB&SNB estimation showed that the neural network was equally effective in the quality of the disease definition. The PNN, based on a neuron model of hyperspectral signature with a spectral step of 6 nm and 2000–4000 value datasets, showed a high quality of detection of the STB&SNB severity. There were 0.99 accuracy, 0.94 precision, 0.89 recall and 0.91 F-score metrics of the PNN model after 10,000 learning epochs. The estimation accuracy of diseased/healthy leaves ranged from 88.1 to 97.7% for different datasets. The accuracy of detection of a light and medium degree of disease was lower (38–66%). This method of non-imaging hyperspectral signature classification could be useful for the identification of the STB and SNB lesion degree identification in field conditions for pre-harvesting crop estimation.

MTA-YOLACT: Multitask-aware network on fruit bunch identification for cherry tomato robotic harvesting

Accurate and rapid perception of fruit bunch posture is necessary for the cherry tomato harvesting robot to successfully achieve the bunch’s holding and separating. According to the postural relationship of the fruit bunch, bunch pedicel, and plant’ main-stem, the robotic end-effector’s holding region and approach path could be determined, which were important for successful picking operation. The main goal of this research was to propose a multitask-aware network (MTA-YOLACT), which simultaneously performed region detection on fruit bunch, and region segmentation on pedicel and main-stem. The MTA-YOLACT extended from the pre-trained YOLACT model, included two detector branch networks for detection and instance segment, which shared the same backbone network, and the loss function with weighting coefficients of the two branches was adopted to balance the multi-task learning, according to multi-task’s homoscedastic uncertainty during the model training. Furthermore, in order to cluster the fruit bunch, pedicel and main-stem from the same bunch target, a classification and regression tree (CART) model was built, based on the region’s positional relationship from the MTA-YOLACT output. An image dataset of cherry tomato plants in China greenhouse was built to training and test the model. The results indicated a promising performance of the proposed network, with an F1-score of 95.4% on detecting fruit bunches and the mean Average Precision of 38.7% and 51.9% on the instance segmentation of pedicel and main-stem, which was 1.1% and 3.5% more than original YOLACT. Beyond that, our approach performed a real-time detection and instance segmentation of 13.3 frames per second (FPS). The whole bunch could be identified by the CART model with an average accuracy of 99.83% and the time cost of 9.53 ms. These results demonstrated the research could be a viable support to the harvesting robot’s vision unit development and the end-effector’s motion planning in the future research.

Design and Implementation of Deep Learning Method for Disease Identification in Plant Leaf

In the whole agriculture plays a very important in country’s economic condition specially in Indian agriculture has a crucial role for raising the Indian economic structure and its level. India’s frequent changing climatic situation, various bacterial disease is much normal that drastically decreases the productivity of crop productivity. Most of the researcher is moving towards into this topic to find the early detection technique to identify the disease in small green leaves plants. A single, micro bacterial infectious disease can destroy all the agricultural small green leaves plants get damaged overnight and hence must be prevented and cured as earliest as possible so that agriculture production. In this research work, we had tried to developed a green small green leaves plants bacterial disease early detection system based on the deep learning network system which will detect the disease at very earlier state of symptoms observed. Deep learning technique is has various algorithms to detect the earliest stage of any of the procedural processing of any bacterial infections or disease. This paper consists of investigations and analysis of latest deep learning techniques. Initially we will explore the deep learning architecture, its various source of data and different types of image processing method that can be used for processing the images captured of leaf for data processing. Different DL architectures with various data visualization’s tools has recently developed to determine symptoms and classifications of different type of plant-based disease. We had observed some issue that was un identified in previous research work during our literature survey and their technique to resolve that issue in order to handle the functional auto-detection system for identifying the certain plant disease in the field where massive growth of green small green leaves plants production is mostly done. Recently various enhancement has been done in techniques in CNN (convolution neural network) that generates much accurate images classification of any object. Our research work is based on deep learning network that will observe and identifies the symptoms generated in leaflet of plant and identifies the type of bacterial infection in progress in that with the help of plant classification stated in the plant dataset. Our research work represents the implementation DCGAN and Hybrid Net Model using Deep learning algorithm for early-stage identification of green plant leaves disease in various environmental condition. Our result obtained shows that it has DCGAN accuracy 96.90% when compared withHybrid Net model disease detection methodologies.

Photovoltaic Module Degradation Forecast Models for Onshore and Offshore Floating Systems

The degradation trend of photovoltaic modules depends on the technology, manufacturing techniques and climatic conditions of the site where they are installed. Longer useful life of the PV modules means that they will be able to produce much more energy than was used to build them; thus, extending the useful life of the modules is beneficial for the environment and increases the cost effectiveness of PVs. The problem of land use has prompted the development of agrivoltaic systems to exploit the same land both for the production of energy and for agriculture, and on water surfaces such as lakes and dams (floating PV). The exploitation of floating PV systems in onshore and offshore areas is currently under study. This constitutes an opportunity for which many factors must be taken into account; a fundamental aspect is the environmental impact, on which some recent studies have focused. Another aspect is the impact of the marine environment on PV system reliability and durability, due to the stress on operating conditions. The aim of this preliminary study is to evaluate the influence of the marine environment on the degradation trend of photovoltaic modules, based on existing models whose inputs are meteorological data from offshore locations. The results obtained from the application of a cumulative exposure model unexpectedly showed a lower degradation value in the offshore environment than on the mainland: −0.95% and −3% values of power decay, respectively. The absolute value of power decay in the onshore case is higher than the typical values because the used model has to be revised, as the empirical coefficients of the model have to be calculated according to the installation environment. The empirical coefficients used in the model were obtained in environmental conditions different from those under study. In the offshore case, the degradation estimated by the model does not take into account some environmental factors typical of the marine environment. Model adaptations calibrated with datasets of plants in environmental conditions similar to those analyzed would allow for greater accuracy in the results.

Technical review of supervised machine learning studies and potential implementation to identify herbal plant dataset

Abstract The use of technology in everyday life is unavoidable, considering that technological advancement occurs very quickly. The current era is also known as industry 4.0. In the industry 4.0 era, there is a convergence between the industrial world and information technology. The use of modern machines in the industry makes it possible for business actors to digitize their production facilities and open up new business opportunities. One of the developments in information technology that is being widely used in its implementation is machine learning (ML) technology and its branches such as computer vision and image recognition. In this work, we propose a customized convolutional neural network-based ML model to perform image classification technique for Indonesian herb image dataset, along with the detailed review and discussion of the methods and results. In this work, we use the transfer learning method to adopt the opensource pre-trained model, namely, Xception, developed by Google.

MAK: a machine learning framework improved genomic prediction via multi-target ensemble regressor chains and automatic selection of assistant traits.

Incorporating the genotypic and phenotypic of the correlated traits into the multi-trait model can significantly improve the prediction accuracy of the target trait in animal and plant breeding, as well as human genetics. However, in most cases, the phenotypic information of the correlated and target trait of the individual to be evaluated was null simultaneously, particularly for the newborn. Therefore, we propose a machine learning framework, MAK, to improve the prediction accuracy of the target trait by constructing the multi-target ensemble regression chains and selecting the assistant trait automatically, which predicted the genomic estimated breeding values of the target trait using genotypic information only. The prediction ability of MAK was significantly more robust than the genomic best linear unbiased prediction, BayesB, BayesRR and the multi trait Bayesian method in the four real animal and plant datasets, and the computational efficiency of MAK was roughly 100 times faster than BayesB and BayesRR.

Plant Disease Diagnosing Based on Deep Learning Techniques

Agriculture crops are highly significant for the sustenance of human life and act as an essential source for national income development worldwide. Plant diseases and pests are considered one of the most imperative factors influencing food production, quality, and minimize losses in production. Farmers are currently facing difficulty in identifying various plant diseases and pests, which are important to prevent plant diseases effectively in a complicated environment. The recent development of deep learning techniques has found use in the diagnosis of plant diseases and pests, providing a robust tool with highly accurate results. In this context, this paper presents a comprehensive review of the literature that aims to identify the state of the art of the use of convolutional neural networks (CNNs) in the process of diagnosing and identification of plant pest and diseases. In addition, it presents some issues that are facing the models performance, and also indicates gaps that should be addressed in the future. In this regard, we review studies with various methods that addressed plant disease detection, dataset characteristics, the crops, and pathogens. Moreover, it discusses the commonly employed five-step methodology for plant disease recognition, involving data acquisition, preprocessing, segmentation, feature extraction, and classification. It discusses various deep learning architecture-based solutions that have a faster convergence rate of plant disease recognition. From this review, it is possible to understand the innovative trends regarding the use of CNN’s algorithms in the plant diseases diagnosis and to recognize the gaps that need the attention of the research community.

Data management system for the plant data in JT-60SA

The data management system for the plant data of JT-60SA has been developed. The new database system has been constructed to integrate the control and management of a set of plant data transferred from subsystems installed in JT-60SA into one system. This database system provides the users with stable control and safe management of the data acquisition process of a set of plant data without missing any data transaction. This system also provides an appropriate environment to compare plant data among several hardware systems easily. Access to the plant database is only permitted through the dedicated data access tool to prevent a malicious user from fabricating and falsifying the original plant data. The operation status and performance of the database system have been evaluated practically under the actual operation of JT-60SA. We have confirmed that our new database system is operated effectively by integrating the plant data of many hardware systems.

Industry 4.0 Based Efficient Energy Management in Microgrid

Industry 4.0 which includes new technologies such as artificial intelligence, machine learning, and the internet of things etc. has brought the revolution in the field of energy management of a microgrid. Energy management is the backbone of a microgrid that needs to be controlled efficiently for a low system failure. There are a lot of issues, such as the intermittent nature of generation, proper voltage distribution, and harmonics, which may arise while implementing an energy management for a microgrid. Machine learning establishes the core of industry 4.0 and is one of the best-suited methods to mitigate such challenges in the current industry 4.0 scenario. In this paper, a Back Propagation Neural Network (BPNN) based machine learning approach is applied for forecasting of a photovoltaic (PV) generation in a microgrid to deal with its intermittent nature for efficient energy management. Further, a firefly optimization technique is utilized to mitigate the harmonics in the voltage. This model is implemented on a real dataset of a solar power plant in Delhi, India. The proposed approach achieves the results of high precision, recall, and accuracy, which shows the efficiency of the system to monitor and regulate uncertainties in the PV microgrid systems.

Computational solar energy – Ensemble learning methods for prediction of solar power generation based on meteorological parameters in Eastern India

The challenges in applications of solar energy lies in its intermittency and dependency on meteorological parameters such as; solar radiation, ambient temperature, rainfall, wind-speed etc., and many other physical parameters like dust accumulation etc. Hence, it is important to estimate the amount of solar photovoltaic (PV) power generation for a specific geographical location. Machine learning (ML) models have gained importance and are widely used for prediction of solar power plant performance. In this paper, the impact of weather parameters on solar PV power generation is estimated by several Ensemble ML (EML) models like Bagging, Boosting, Stacking, and Voting for the first time. The performance of chosen ML algorithms is validated by field dataset of a 10kWp solar PV power plant in Eastern India region. Furthermore, a complete test-bed framework has been designed for data mining as well as to select appropriate learning models. It also supports feature selection and reduction for dataset to reduce space and time complexity of the learning models. The results demonstrate greater prediction accuracy of around 96% for Stacking and Voting EML models. The proposed work is a generalized one and can be very useful for predicting the performance of large-scale solar PV power plants also.

Classification of Pear Leaf Diseases Based on Ensemble Convolutional Neural Networks

Over the last few years, the impact of climate change has increased rapidly. It is influencing all steps of plant production and forcing farmers to change and adapt their crop management practices using new technologies based on data analytics. This study aims to classify plant diseases based on images collected directly in the field using deep learning. To this end, an ensemble learning paradigm is investigated to build a robust network in order to predict four different pear leaf diseases. Several convolutional neural network architectures, named EfficientNetB0, InceptionV3, MobileNetV2 and VGG19, were compared and ensembled to improve the predictive performance by adopting the bagging strategy and weighted averaging. Quantitative experiments were conducted to evaluate the model on the DiaMOS Plant dataset, a self-collected dataset in the field. Data augmentation was adopted to improve the generalization of the model. The results, evaluated with a range of metrics, including accuracy, recall, precison and f1-score, showed that the proposed ensemble convolutional neural network outperformed the single convolutional neural network in classifying diseases in real field-condition with variation in brightness, disease similarity, complex background, and multiple leaves.

Machine learning in the estimation of CRISPR-Cas9 cleavage sites for plant system.

CRISPR-Cas9 system is one of the recent most used genome editing techniques. Despite having a high capacity to alter the precise target genes and genomic regions that the planned guide RNA (or sgRNA) complements, the off-target effect still exists. But there are already machine learning algorithms for people, animals, and a few plant species. In this paper, an effort has been made to create models based on three machine learning-based techniques [namely, artificial neural networks (ANN), support vector machines (SVM), and random forests (RF)] for the prediction of the CRISPR-Cas9 cleavage sites that will be cleaved by a particular sgRNA. The plant dataset was the sole source of inspiration for all of these machine learning-based algorithms. 70% of the on-target and off-target dataset of various plant species that was gathered was used to train the models. The remaining 30% of the data set was used to evaluate the model's performance using a variety of evaluation metrics, including specificity, sensitivity, accuracy, precision, F1 score, F2 score, and AUC. Based on the aforementioned machine learning techniques, eleven models in all were developed. Comparative analysis of these produced models suggests that the model based on the random forest technique performs better. The accuracy of the Random Forest model is 96.27%, while the AUC value was found to be 99.21%. The SVM-Linear, SVM-Polynomial, SVM-Gaussian, and SVM-Sigmoid models were trained, making a total of six ANN-based models (ANN1-Logistic, ANN1-Tanh, ANN1-ReLU, ANN2-Logistic, ANN2-Tanh, and ANN-ReLU) and Support Vector Machine models (SVM-Linear, SVM-Polynomial, SVM-Gaussian However, the overall performance of Random Forest is better among all other ML techniques. ANN1-ReLU and SVM-Linear model performance were shown to be better among Artificial Neural Network and Support Vector Machine-based models, respectively.

Invasion by an exotic grass species homogenizes native freshwater plant communities

Abstract A growing body of evidence has shown that biological invasions cause shifts in species composition of communities in space and time. Although biological invasions are considered a major driver of biotic homogenisation worldwide, most previous studies were conducted at small spatial scales and over short time periods, which may have underestimated the impacts of exotic species on native communities. Using a unique dataset of aquatic plants sampled in 235 sites over 12 years (2007–2010 and 2015–2019) in a large reservoir (Itaipu Reservoir; 1350 km2), we analysed how the invasion of a non‐native grass Urochloa arrecta affects the species richness, ecological uniqueness (i.e. local contribution to beta diversity—LCBD) and temporal β–diversity of native plant communities. From 3934 surveyed plant communities, U. arrecta was recorded in 2888 samples and it was absent from 1046 samples. Overall, species richness and ecological uniqueness of native plant communities were markedly lower in sites invaded than non‐invaded by U. arrecta. From 2007 to 2019, the ecological uniqueness of native plants was 60% lower in the invaded than non‐invaded sites. Whereas in invaded sites species loss was the dominant process driving native communities over time, in non‐invaded sites gain of new native species was the primary process underlying community trajectories. Moreover, comparing native plant communities before and after the invasion of U. arrecta, species richness, ecological uniqueness and species gains of native plant communities decreased, whereas species losses increased after the invasion of U. arrecta. Finally, the positive relationship between native biodiversity and precipitation was stronger in non‐invaded than invaded sites. Synthesis. Our findings provide comprehensive evidence that an invasive plant is decreasing the spatial and temporal β–diversity of native plant communities through declining species richness, rather than simply correlating with them. This suggests that U. arrecta is driving native plants to become less diverse and homogeneous after the invasion, both spatially and temporally. Our findings illustrate that at broad scales, aquatic plant communities may become increasingly homogeneous with the increasing number of biological invasion events taking place worldwide.

Photonic machine learning with on-chip diffractive optics

Machine learning technologies have been extensively applied in high-performance information-processing fields. However, the computation rate of existing hardware is severely circumscribed by conventional Von Neumann architecture. Photonic approaches have demonstrated extraordinary potential for executing deep learning processes that involve complex calculations. In this work, an on-chip diffractive optical neural network (DONN) based on a silicon-on-insulator platform is proposed to perform machine learning tasks with high integration and low power consumption characteristics. To validate the proposed DONN, we fabricated 1-hidden-layer and 3-hidden-layer on-chip DONNs with footprints of 0.15 mm2 and 0.3 mm2 and experimentally verified their performance on the classification task of the Iris plants dataset, yielding accuracies of 86.7% and 90%, respectively. Furthermore, a 3-hidden-layer on-chip DONN is fabricated to classify the Modified National Institute of Standards and Technology handwritten digit images. The proposed passive on-chip DONN provides a potential solution for accelerating future artificial intelligence hardware with enhanced performance.

AntDAS-DDA: A New Platform for Data-Dependent Acquisition Mode-Based Untargeted Metabolomic Profiling Analysis with Advantage of Recognizing Insource Fragment Ions to Improve Compound Identification.

Data-dependent acquisition (DDA) mode in ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) can provide massive amounts of MS1 and MS/MS information of compounds in untargeted metabolomics and can thus facilitate compound identification greatly. In this work, we developed a new platform called AntDAS-DDA for the automatic processing of UHPLC-HRMS data sets acquired under the DDA mode. Several algorithms, including extracted ion chromatogram extraction, feature extraction, MS/MS spectrum construction, fragment ion identification, and MS1 spectrum construction, were developed within the platform. The performance of AntDAS-DDA was investigated comprehensively with a mixture of standard and complex plant data sets. Results suggested that features in complex sample matrices can be extracted effectively, and the constructed MS1 and MS/MS spectra can benefit in compound identification greatly. The efficiency of compound identification can be improved by about 20%. AntDAS-DDA can take full advantage of MS/MS information in multiple sample analyses and provide more MS/MS spectra than single sample analysis. A comparison with advanced data analysis tools indicated that AntDAS-DDA may be used as an alternative for routine UHPLC-HRMS-based untargeted metabolomics. AntDAS-DDA is freely available at http://www.pmdb.org.cn/antdasdda.

The Application of Augmented Reality Technology in Urban Greening Plant Growth State Detection

The current target detection network in deep learning has been widely used in plant growth state detection. However, with the development of deep learning, within the field of plant growth state detection, the performance of the detection network is no longer the primary factor limiting the detection accuracy and model generalization ability. The construction of high-quality and large-scale plant datasets is more significant for the improvement of model detection accuracy and generalization ability. However, traditional methods for building deep learning datasets for plants have a large time span and low efficiency. And it is difficult to construct and expand the dataset for plants with complex growth environments and difficult image acquisition by existing methods. To address this problem, this paper proposes a method for constructing plant datasets based on augmented reality techniques. The method proposed in this paper allows for the rapid and efficient construction of large-scale field datasets that match the actual inspection environment in the lack of data. Meanwhile, this paper proposes an automatic annotation method for datasets in conjunction with the imaging environment in virtual space. In this paper, we experimentally compare the proposed method with the method of expanding the dataset using GAN networks. Using the virtual dataset constructed by the method proposed in this paper as the training set, the trained YOLOv5 model achieves an average accuracy (@0.5:0.95) of 0.71 for the three detection categories on the test set. The detection accuracy of the six mixed datasets constructed using the two data expansion methods on the test set was experimentally tested. The proposed method in this paper improved the accuracy by 2.2%, 3.1%, and 7.0%, respectively. The smaller the percentage of real images, the greater the accuracy improvement. Experiments show that the method proposed in this paper can well solve the problems faced in the field of plant growth state detection, such as the lack of data, and provides a new idea for the production and expansion of datasets in plant detection tasks.

Image Generation of Tomato Leaf Disease Identification Based on Small-ACGAN

Plant diseases have become a challenging threat in the agricultural field. Various learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases early. However, deep learning entails extensive data for training, and it may be challenging to collect plant datasets. Even though plant datasets can be collected, they may be uneven in quantity. As a result, the problem of classification model overfitting arises. This study targets this issue and proposes an auxiliary classifier GAN (small-ACGAN) model based on a small number of datasets to extend the available data. First, after comparing various attention mechanisms, this paper chose to add the lightweight Coordinate Attention (CA) to the generator module of Auxiliary Classifier GANs (ACGAN) to improve the image quality. Then, a gradient penalty mechanism was added to the loss function to improve the training stability of the model. Experiments show that the proposed method can best improve the recognition accuracy of the classifier with the doubled dataset. On AlexNet, the accuracy was increased by 11.2%. In addition, small-ACGAN outperformed the other three GANs used in the experiment. Moreover, the experimental accuracy, precision, recall, and F1 scores of the five convolutional neural network (CNN) classifiers on the enhanced dataset improved by an average of 3.74%, 3.48%, 3.74%, and 3.80% compared to the original dataset. Furthermore, the accuracy of MobileNetV3 reached 97.9%, which fully demonstrated the feasibility of this approach. The general experimental results indicate that the method proposed in this paper provides a new dataset expansion method for effectively improving the identification accuracy and can play an essential role in expanding the dataset of the sparse number of plant diseases.

RNA-Seq Data Processing in Plant-Pathogen Interaction System: A Case Study.

In RNA-seq data processing, short reads are usually aligned from one species against its own genome sequence; however, in plant-pathogen interaction systems, reads from both host and pathogen samples are blended together. In contrast with single-genome analyses, both pathogen and host reference genomes are involved in the alignment process. In such circumstances, the order in which the alignment is carried out, whether the host or pathogen is aligned first, or if both genomes are aligned simultaneously, influences the read counts of certain genes. This is a problem, especially at advanced infection stages. It is crucial to have an appropriate strategy for aligning the reads to their respective genomes, yet the existing strategies of either sequential or parallel alignment become problematic when mapping mixed reads to their corresponding reference genomes. The challenge lies in the determination of which reads belong to which species, especially when homology exists between the host and pathogen genomes. This chapter proposes a combo-genome alignment strategy, which was compared with existing alignment scenarios. Simulation results demonstrated that the degree of discrepancy in the results is correlated with phylogenetic distance of the two species in the mixture which was attributable to the extent of homology between the two genomes involved. This correlation was also found in the analysis using two real RNA-seq datasets of Fusarium-challenged wheat plants. Comparisons of the three RNA-seq processing strategies on three simulation datasets and two real Fusarium-infected wheat datasets showed that an alignment to a combo-genome, consisting of both host and pathogen genomes, improves mapping quality as compared to sequential alignment procedures.