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Cauliflower, a globally cultivated and nutritionally rich crop, confronts significant challenges in quality and yield due to the rising prevalence of diseases. Traditional manual detection methods, suitable for empiricists or plant pathologists, prove inefficient. Furthermore, existing automated disease identification methods in cauliflower often neglect crucial computational performance metrics within computer vision algorithms, such as complexity, inference speed and training time. This study introduces LiteMixer, a novel lightweight model designed to address these challenges. The Lightweight Mixed-Domain Feature Extraction module (LMFE) meticulously captures global image features, followed by a maximum pooling layer that downscales the resulting multidimensional feature matrix. The Plug-and-Play Multi-Scale Lightweight Convolutional Attention Fusion module (MLCAF) integrates multichannel spatial features, connecting to fully connected layers for the final classification. Ablation experiments highlight the effectiveness of the LMFE module coupled with the MLCAF module. Comparative analyses against state-of-the-art and other lightweight models demonstrate LiteMixer achieving the highest accuracy in identifying cauliflower diseases at 99.86%. Notably, LiteMixer exhibits optimal computational performance, featuring minimal storage costs (4.02M) and the lowest parameter count, resulting in cost-effective computational expenses (16.78M). LiteMixer also boasts the fastest inference time (4.69 ms) and the shortest training time (865 s). This study positions LiteMixer as an advanced solution for diagnosing cauliflower leaf diseases in agricultural settings, underscoring its efficacy and practicality in overcoming the unique challenges associated with cauliflower disease detection within the realm of computer vision algorithms. [ABSTRACT FROM AUTHOR]

Plant disease detection has played a significant role in combating plant diseases that pose a threat to global agriculture and food security. Detecting these diseases early can help mitigate their impact and ensure healthy crop yields. Machine learning algorithms have emerged as powerful tools for accurately identifying and classifying a wide range of plant diseases from trained image datasets of affected crops. These algorithms, including deep learning algorithms, have shown remarkable success in recognizing disease patterns and early signs of plant diseases. Besides early detection, there are other potential benefits of machine learning algorithms in overall plant disease management, such as soil and climatic condition predictions for plants, pest identification, proximity detection, and many more. Over the years, research has focused on using machine-learning algorithms for plant disease detection. Nevertheless, little is known about the extent to which the research community has explored machine learning algorithms to cover other significant areas of plant disease management. In view of this, we present a cross-comparative review of machine learning algorithms and applications designed for plant disease detection with a specific focus on four (4) economically important plants: apple, cassava, cotton, and potato. We conducted a systematic review of articles published between 2013 and 2023 to explore trends in the research community over the years. After filtering a number of articles based on our inclusion criteria, including articles that present individual prediction accuracy for classes of disease associated with the selected plants, 113 articles were considered relevant. From these articles, we analyzed the state-of-the-art techniques, challenges, and future prospects of using machine learning for disease identification of the selected plants. Results from our review show that deep learning and other algorithms performed significantly well in detecting plant diseases. In addition, we found a few references to plant disease management covering prevention, diagnosis, control, and monitoring. In view of this, little or no work has explored the prediction of the recovery of affected plants. Hence, we propose opportunities for developing machine learning-based technologies to cover prevention, diagnosis, control, monitoring, and recovery. [ABSTRACT FROM AUTHOR]

Agriculture is the most important sector of the Indian economy. Rice cultivation plays an important role in many regions of India. Most farmers in India are fully dependent on rice. Early detection of diseases in rice plants plays an important role in yielding more. This paper proposes a solution to detect and classify rice plant diseases too early using automatic image processing techniques. Automatic detection uses image segmentation and neural networks for classification of plant leaves. It takes the image as input and applies techniques to that image, like pre-processing and segmentation, and then the input is given to the convolutional neural network in order to classify the disease. Most Indian farmers are not well educated to detect the disease of the plant before it gets damaged, which results in less production. Rice production has the main role in Indian economics, so adequate efforts are needed to improve it. [ABSTRACT FROM AUTHOR]

Multiple microbes can alter a plant's development and agricultural productivity, which has significant implications for the ecosystem and human life. As a result, timely identification, prevention, and prompt treatment are required. Fundamental methods have some drawbacks to plant disease identification like more time-consuming, accuracy, doesn't support multiple plant detection. This paper introduces a hybrid model that uses a random forest classifier combined with the AdaBoost Classifier to classify plant diseases to overcome the above-said drawbacks. So as to individualize normal and abnormal leaves from data sets, the suggested methodology employs the Random Forest with AdaBoost algorithm. The operational processes in our suggested study are preprocessing, segmentation, feature extraction, training the classifier, and classification. The produced datasets of infected and uninfected leaves are combined and processed using the Random Forest classifier to categorize the infected and uninfected photos. Color Histogram is used to gather features from imagery. KNN, Naive Bayes, and SVM are all used to evaluate our suggested technique. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In agriculture, one of the most challenging tasks is the early detection of plant diseases. It is essential to identify diseases early in order to boost agricultural productivity. This problem has been solved with machine learning and deep learning techniques using an automated method for detecting plant diseases on large crop farms which is beneficial because it reduces monitoring time. In this paper, we used the dataset "Plant Village" with 17 basic diseases, with a display of four bacterial diseases, two viral illnesses, two mould illnesses, and one mite-related disease. A total of 12 crop species are also shown with images of unaffected leaves. The machine learning approaches viz support vector machines (SVMs), gray-level co-occurrence matrices (GLCMs), and convolutional neural networks (CNNs) are used for the development of prediction models. With the development of backpropagation ANNs, artificial intelligence for classification has also evolved. A K-mean clustering operation is also used to detect disease based on the real-time leaf images collected. [ABSTRACT FROM AUTHOR]

Agriculture in India plays crucial role in the economy and growth of the country. However, technology driven innovation in this field known as Precision Agriculture (PA) is still in its infancy. Nevertheless, there are significant improvements with technology innovations. With the emergence of deep learning as part of Artificial Intelligence (AI), it is made possible to bring about technology into agriculture activities. One of the activities that can be automated is leaf disease detection. With scalable access to cloud computing resources, this area of the research has attracted many academia and researchers. The existing approaches with Convolutional Neural Network (CNN) exhibited shortcomings in terms of adaptation and reuse of learned outcomes. In this paper, we used CNN with pre-trained deep models with transfer learning to fill this gap. We proposed a framework known as Deep Leaf DiseasePrediction Framework (DLDPF) with CNN equipped with AlexNet and GoogLeNet cascade inception. The underlying algorithm is known as Cascade Inception based Deep CNN with Transfer Learning (CIDCNN-TL). Keras and TensorFlow along with Python data science platform are used for implementation of the proposed framework. The proposed framework DLDPF is compared with many deep learning models such as AlexNet, GoogLeNet, VGGNet-16 and ResNet-20. Apple leafs dataset is used for empirical study. The experimental results revealed that the DLDPF outperforms the state-of-the-art deep learning models for automated prediction of lead diseases. [ABSTRACT FROM AUTHOR]

Copyright of Engenharia na Agricultura is the property of Engenharia na Agricultura and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The phenomenon of plant leaf illness has fatalistic influences on production and assists in promoting safety in products concerning agriculture. Owing to the reason that the leaf is major vulnerable portion of a plant it is influenced easily upon comparison with the other parts. Plant disease detection by means of numerous automatic models is advantageous as it minimizes immense work of big farm monitoring, and also at the early stage assists in detection of the disease symptoms. Several frameworks have been designed for plant disease. But the error rate and overhead incurred in plant leaf disease diagnosis was not focused. In this research, characterize and investigate the plant leaf disease prediction using three novel proposed frameworks using Plant Village dataset. They are Gaussian Distributive Czekanowskis Region-based Deming Regression (GDCR-DM), Covariance Kalman Geometric Graphbased Bernoulli Classifier (CKGG-BC) and Independent Gaussian Gray Level Gaussian Gray Level Geometric Neural Network Classifier (IGGL-GNNC). The GDCR-DM framework identifies the plant leaf disease in an accurate manner with minimum false positive rate employing Czekanowski's dice and Deming regression function. Next, to reduce noise involved during preprocessing and segmentation, CKGG-BC framework is proposed that with the aid of Covariance Kalman filter function and Multiple Kernel Learning Classifier not only reduces the error but also enhances true positive rate considerably. Finally, IGGL-GNNC framework is designed that Sine Cosine Position Update using geometric functions for realistic disease prediction. The different parameters were evaluated for distinct plant leaf images obtained from Plant Village Image dataset. [ABSTRACT FROM AUTHOR]

The disease in tomato plants, especially on tomato leaves will have an impact on the quality and quantity of tomatoes produced. Handling disease on tomato leaves that must be done is to detect the type of disease as early as possible, then determine the treatment that must be done. Detection of its types of tomato plant diseases requires sufficient knowledge and experience. The problem is that many beginner farmers in growing tomatoes do not have much knowledge, so they have failed in growing tomatoes. Based on these cases, this study proposes a model for the early detection of disease in tomato leaves based on image processing. The research method used is divided into 5 stages, namely preprocessing, segmentation, feature extraction, classification, and performance evaluation. The feature extraction stage used is texture-based with Gabor filters and color-based filters. The final decision is determined by the Support Vector Machine (SVM) classification algorithm with the Radial Basis Function (RBF) kernel. The test results of the tomato leaf disease detection system produced an average performance parameter of 98.83% specificity, 90.37% sensitivity, 90.34% F1-score, 90.37% accuracy, and 94.60% area under the curve (AUC). Referring to the resulting of the AUC performance, the tomato leaf disease detection system is in the very good category. [ABSTRACT FROM AUTHOR]

Augmented reality system enables effective interaction with field view data and executes a particular task effectively by visual display aid. Precision agriculture involves precision measurement, data generation, analysis for interpretation of data, and decision-making to improve the yield and monitor the plant. Augmented reality will help to systematically acquire the needed data and interpret the required information from the analytical result on the field. This paper presents a low-cost development of the augmented reality system for on-field analysis of plant diseases. The article also presents a framework of deep learningbased cloud data analytic to enable on-field real-time interaction between the farmers and cloud data processing systems using a head-mounted unit. The proposed augmented reality system performance is validated for its accuracy in detecting plant diseases, real-time interaction response time, and ease of usage by the farmer community. The results show that the proposed mechanism will be able to produce real-time augment interaction to the farmer for the task of disease inspection of the plant effectively and accurately. [ABSTRACT FROM AUTHOR]

Fourteen underground vegetables including roots, bulbs, rhizomes, corms and tubers have been observed to be infected by sixteen different species of Alternaria which cause leafs pots/blight, root rot and black rot in the field and decay, deterioration in storage. Radish, turnip and knol khol are affected by A. raphani, A. brassicae and A. brassiciola. Carrot, an underground taproot vegetable is infected by three species of Alternaria causing leaf blight and black rot. A. porri and A. alli cause purple blotch in onion, garlic and leek. On sweet potato and potato, four Alternaria spp. are involved and in case of rhizomes and corms. A. alternata of wide spread occurrence, appeared to cause the lesions, spots and blight in six underground vegetables like radish, sugarbeet, ginger, turmeric, zimikand and potato. All the sixteen species of Alternaria representing different sections of this genus differ in their morphological characteristics. Simple and suitable keys for their correct identification have been framed also where there is the involvement of more than one Alternaria species in causation of disease. [ABSTRACT FROM AUTHOR]

Currently, the application of deep learning in crop disease classification is one of the active areas of research for which an image dataset is required. Eggplant (Solanum melongena) is one of the important crops, but it is susceptible to serious diseases which hinder its production. Surprisingly, so far no dataset is available for the diseases in this crop. The unavailability of the dataset for these diseases motivated the authors to create a standard dataset in laboratory and field conditions for five major diseases. Pre-trained Visual Geometry Group 16 (VGG16) architecture has been used and the images have been converted to other color spaces namely Hue Saturation Value (HSV), YCbCr and grayscale for evaluation. Results show that the dataset created with RGB and YCbCr images in field condition was promising with a classification accuracy of 99.4%. The dataset also has been evaluated with other popular architectures and compared. In addition, VGG16 has been used as feature extractor from 8th convolution layer and these features have been used for classifying diseases employing Multi-Class Support Vector Machine (MSVM). The analysis depicted an equivalent or in some cases produced better accuracy. Possible reasons for variation in interclass accuracy and future direction have been discussed. [ABSTRACT FROM AUTHOR]

An interview with Jacqueline Fletcher, director of the National Institute for Microbial Forensics and Food and Agricultural Biosecurity at Oklahoma State University, is presented. When asked about the plant microbial forensics as an area of research, she refers to diagnosis and treatment of plant diseases and pathogens. She cited ways on how plant diseases are transported as well as its causes. She also gives her opinion to justify for investment in the field of plant microbial forensics.

Necessity demands observation and assessment of growth development of Mango ginger plants (with a paucity of or no publication about its agronomy and pathology), as a prerequisite to understanding their nature and avoid incorrect plant disease diagnosis (by taking for an abnormal condition that which is normal). Hence, this study observed the morphological development of Mango ginger (Curcuma amada Roxb.) plants, a rare crop, and assessed for its plant height and number of leaves in the years 2016 and 2017. The rhizomes, the most economical part of the plant, was observed to emerge from a corm and appeared finger-like with dome ends. Mango ginger leaves averaged Nine per plant stand and showed curling nastic movement. Sunlight and temperature had a positive and significant effect on the daily curling and uncurling of leaves. Also, the height parameter of Mango ginger plants was inconsistent, when measured from soil level to tip of youngest leaf/apical bud. But when measured from soil level to peak/top (highest point attained at a natural position above soil level), irrespective of what leaf made the upper boundary, plants increased in height with time. A significant difference existed in 2016 between the two different upper boundaries of plant height. The study provides basic understanding of the morphology of Mango ginger plant; and gives original and significant insight on the nastic movement of its leaves, for better understanding of its fundamental botany, which may form bases for its disease identification and further investigation as an under-cultivated and underutilized crop (for colouring and flavouring drinks, and as: food preservative; medicine for cure of bacterial and fungal infection; anti-inflammatory and antipyretic; hypotriglyceridemic and hypoglycemic; anti-cancer with fertility potential). [ABSTRACT FROM AUTHOR]

Identifying plant pathogens for disease diagnosis and disease control strategy making is of great significance. In this study, based on near-infrared spectroscopy, a method for identifying three kinds of pathogens causing wheat smuts, including Tilletia foetida, Ustilago tritici, and Urocystis tritici, was investigated. Based on the acquired near-infrared spectral data of the teliospore samples of the three pathogens, pathogen identification models were built in different spectral regions using distinguished partial least squares (DPLS), backpropagation neural network (BPNN), and support vector machine (SVM). Satisfactory identification results were achieved using the DPLS, BPNN, and SVM models built in each of the 22 spectral regions. By contrast, the modeling effects of DPLS and SVM were better than those of BPNN. The modeling ratio of the training set to the testing set affected the identification results of the BPNN models more than those obtained using the DPLS and SVM models. In this study, a rapid, accurate, and nondestructive method was provided for plant pathogen identification, and some basis was provided for disease diagnosis, pathogen monitoring, and disease control. Moreover, some methodological references and supports were provided for identification of quarantine wheat smut fungi in plant quarantine. [ABSTRACT FROM AUTHOR]

Proposed method based on fuzzy logic, including the operation of calculating the 36 key parameters of the R, G, B, RG, RB, GB color components of the original RGB color space images of plant leaves and the GLCM adjacency matrix, forming fuzzy conclusions about the type of plant disease, defusing using threshold binarization and majority voting on 6 parameters. [ABSTRACT FROM AUTHOR]

The timely and accurate diagnosis of plant diseases plays an important role in preventing the loss of productivity and loss or reduced quantity of agricultural products. In order to solve such problems, methods based on machine learning can be used. In recent years, deep learning, which is especially widely used in image processing, offers many new applications related to precision agriculture. In this study, we evaluated the performance results using different approaches of nine powerful architectures of deep neural networks for plant disease detection. Transfer learning and deep feature extraction methods are used, which adapt these deep learning models to the problem at hand. The utilized pretrained deep models are considered in the presented work for feature extraction and for further fine-tuning. The obtained features using deep feature extraction are then classified by support vector machine (SVM), extreme learning machine (ELM), and K-nearest neighbor (KNN) methods. The experiments are carried out using data consisting of real disease and pest images from Turkey. The accuracy, sensitivity, specificity, and F1-score are all calculated for performance evaluation. The evaluation results show that deep feature extraction and SVM/ELM classification produced better results than transfer learning. In addition, the fc6 layers of the AlexNet, VGG16, and VGG19 models produced better accuracy scores when compared to the other layers. [ABSTRACT FROM AUTHOR]

• Automatic Plant disease diagnosis for early disease symptoms. • Novel image processing algorithm in combination with machine learning methods. • A testing on wheat was performed with 7 mobile devices over a period of 4 seasons. • Obtained results reveal AuC metrics higher than 0.80 for all the analyzed diseases. Fungal infection represents up to 50 % of yield losses, making it necessary to apply effective and cost efficient fungicide treatments, whose efficacy depends on infestation type, situation and time. In these cases, a correct and early identification of the specific infection is mandatory to minimize yield losses and increase the efficacy and efficiency of the treatments. Over the last years, a number of image analysis-based methodologies have been proposed for automatic image disease identification. Among these methods, the use of Deep Convolutional Neural Networks (CNNs) has proven tremendously successful for different visual classification tasks. In this work we extend previous work by Johannes et al. (2017) with an adapted Deep Residual Neural Network-based algorithm to deal with the detection of multiple plant diseases in real acquisition conditions where different adaptions for early disease detection have been proposed. This work analyses the performance of early identification of three relevant European endemic wheat diseases: Septoria (Septoria triciti), Tan Spot (Drechslera triciti-repentis) and Rust (Puccinia striiformis & Puccinia recondita). The analysis was done using different mobile devices, and more than 8178 images were captured in two pilot sites in Spain and Germany during 2014 , 2015 and 2016. Obtained results reveal an overall improvement of the balanced accuracy from 0.78 (Johannes et al., 2017) up to 0.87 under exhaustive testing, and balanced accuracies greater than 0.96 on a pilot test performed in Germany. [ABSTRACT FROM AUTHOR]

Featured Application: A low complexity image processing and a new classification method are proposed in this paper. These methods can be employed by plant disease diagnosis applications implemented on smart phones. The supported set of diseases can be extended by the end user. A plant disease diagnosis method that can be implemented with the resources of a mobile phone application, that does not have to be connected to a remote server, is presented and evaluated on citrus diseases. It can be used both by amateur gardeners and by professional agriculturists for early detection of diseases. The features used are extracted from photographs of plant parts like leaves or fruits and include the color, the relative area and the number of the lesion spots. These classification features, along with additional information like weather metadata, form disease signatures that can be easily defined by the end user (e.g., an agronomist). These signatures are based on the statistical processing of a small number of representative training photographs. The extracted features of a test photograph are compared against the disease signatures in order to select the most likely disease. An important advantage of the proposed approach is that the diagnosis does not depend on the orientation, the scale or the resolution of the photograph. The experiments have been conducted under several light exposure conditions. The accuracy was experimentally measured between 70% and 99%. An acceptable accuracy higher than 90% can be achieved in most of the cases since the lesion spots can recognized interactively with high precision. [ABSTRACT FROM AUTHOR]

Reliable detection and identification of plant pathogens are essential for disease control strategies. Diagnostic methods commonly used to detect plant pathogens have limitations such as requirement of prior knowledge of the genome sequence, low sensitivity and a restricted ability to detect several pathogens simultaneously. The development of advanced DNA sequencing technologies has enabled determination of total nucleic acid content in biological samples. The possibility of using the single‐molecule sequencing platform of Oxford Nanopore as a general method for diagnosis of plant diseases was examined. It was tested by sequencing DNA or RNA isolated from tissues with symptoms from plants of several families inoculated with known pathogens (e.g. bacteria, viruses, fungi, phytoplasma). Additionally, samples of groups of 200 seeds containing one infected seed of each of two or three pathogens, as well as samples with symptoms but unidentified pathogens were tested. Sequencing results were analysed with Nanopore data analysis tools. In all the inoculated plants, pathogens were identified in real time within 1–2 h of running the Nanopore sequencer and were classified to the species or genus level. DNA sequencing or direct RNA sequencing of samples with unidentified disease agents were validated by conventional diagnostic procedures (e.g. PCR, ELISA, Koch test), which supported the results obtained by Nanopore sequencing. The advantages of this technology include: long read lengths, fast run times, portability, low cost and the possibility of use in every laboratory. This study indicates that adoption of the Nanopore platform will be greatly advantageous for routine laboratory diagnosis. [ABSTRACT FROM AUTHOR]

Highlights • A new method is proposed to achieve accurate extraction of tea plant leaf disease saliency map under complex backgrounds. • Simple Linear Iterative Cluster is used as a preprocessing step to separate the salient region and background. • The combination of SLIC and SVM realized the automatic selection of SVM algorithm training set. • Proposed method performs better than the other SLIC based algorithm both in visual effects and quality assessment index. Abstract For the purpose of improving the extraction of tea plant leaf disease saliency map under complex backgrounds, a new algorithm combining SLIC (Simple Linear Iterative Cluster) with SVM (Support Vector Machine) is proposed in this paper. Firstly, super-pixel block is obtained by SLIC algorithm, significant point is detected by Harris algorithm, and fuzzy salient region contour is extracted by employing convex hull method. Secondly, the four-dimensional texture features of super-pixel blocks in salient regions and background areas are extracted, and then the classification map is obtained by classifying the super-pixel blocks with the help of SVM classifier. Lastly, the morphological and algebraic operations are implemented for repairing classified super-pixel blocks. As a result, one accurate saliency map of tea plant leaf disease image is obtained. Through testing based on 261 diseased images, the quality evaluation index, the accuracy, precision, recall and F-value are 98.5%, 96.8%, 98.6% and 97.7%, respectively. It demonstrates that the proposed method performs better than the other three SLIC-based algorithms in visual effects and quality assessment index. Such conclusion can be drawn that the proposed method can effectively extract tea plant leaf disease saliency map from complex background. Consequently, this research is expected to lay a good basis for the study of tea plant leaf disease identification. Last but not the least, the proposed method has good potential that extracts saliency map of crops or plants disease. [ABSTRACT FROM AUTHOR]

Early detection of foliar diseases is vital to the management of plant disease, since these pathogens hinder crop productivity worldwide. This research applied hyperspectral imaging (HSI) technology to early detection of Magnaporthe oryzae -infected barley leaves at four consecutive infection periods. The averaged spectra were used to identify the infection periods of the samples. Additionally, principal component analysis (PCA), spectral unmixing analysis and spectral angle mapping (SAM) were adopted to locate the lesion sites. The results indicated that linear discriminant analysis (LDA) coupled with competitive adaptive reweighted sampling (CARS) achieved over 98% classification accuracy and successfully identified the infected samples 24 h after inoculation. Importantly, spectral unmixing analysis was able to reveal the lesion regions within 24 h after inoculation, and the resulting visualization of host–pathogen interactions was interpretable. Therefore, HSI combined with analysis by those methods would be a promising tool for both early infection period identification and lesion visualization, which would greatly improve plant disease management. [ABSTRACT FROM AUTHOR]

Copyright of Al-Mustansiriyah Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Zinc deficit is one of the most important reasons for botanical disease. It can occur in many forms, for example, as rice blast, bacterial stripe, bacterial blight, and false smut. All these diseases will greatly lead to lower yields during rice harvest. In these paper, we show that Raman spectroscopy provides a single, fast, and sensitive method for detecting zinc deficit, especially during early forms of zinc deficiency. When a plant lacks zinc, the carotenoid content will increase, and many other components' content will decrease, such as starch, proteins, sugars, and amide. All of these changes will be brought out in the Raman spectrum of the rice leaves. In this study, we scanned the healthy leaves, the leaves with early zinc deficiency, and the leaves with zinc deficiency by confocal Raman spectrometer to get the Raman spectra within the wavenumber 200–1,200 cm−1. First, the ensemble empirical mode decomposition algorithm was used to remove the baseline and the noise in the Raman spectra. Then principal components selected by the successive projections algorithm (PC1, PC2, PC3, and PC6) and characteristic parameters of the spectra S2S1 (the area of 690–780 cm−1S1 is divided by the area of 1,080–1,200 cm−1S2) selected by one‐way variance of analysis and related with the content of carotenoid, starch, carbohydrate, soluble sugar, and so forth were the inputs of the least squares support vector machine model. The model is able to determine from a variety of samples whether the sample has zinc deficiency, early zinc deficiency, or if it is healthy, with a 100% accuracy. These results illustrate that early forms of zinc deficit can be detected before the plants show any physical symptoms. This is groundbreaking news for the future development of zinc deficiency monitoring equipment, as well as for other types of deficiency monitoring equipment in general. In these paper, we show that Raman spectroscopy provides a single, fast, and sensitive method for detecting zinc deficit, especially during early forms of zinc deficiency with an accuracy of 100%. These results illustrate that early forms of zinc deficit can be detected before the plants show any physical symptoms. This is groundbreaking news for the future development of zinc deficiency monitoring equipment, as well as for other types of deficiency monitoring equipment in general. [ABSTRACT FROM AUTHOR]

Highlights • Largest producers of crops include south Asian countries including Pakistan. • There is lack of exploitation of technology for enhancements in agriculture sector. • Diagnosis of crops diseases in rural areas is a challenge. • Intelligent mobile application with easy to use interface has been developed using fuzzy inference system. • Highly promising results leading up to 99% of accurate disease diagnosis have been achieved. Abstract South Asian countries are amongst the largest producers of crops with favourable climate conditions and fertile soil. However, traditional agricultural mechanisms are in place and inadequate effort has been put into exploit the usage of technology. One of the main problems being faced by agriculture sector in Pakistan and other developing countries is that crop diseases are not diagnosed timely and efficiently. Conventional methods for disease diagnosis in crops lead to less accurate and inefficient diagnosis, consequently leading to low productivity. In this paper, an intelligent approach for the diagnosis of crop diseases is proposed which is capable of working over Android mobile devices using fuzzy inference system as the main decision making engine at the backend. The system is capable enough to communicate to the farmers in Pakistan in their local language Urdu and assist them in diagnosing diseases in their crops. Agriculture experts in government sector can get equal benefit from it in diagnosis and prevention of crops diseases. It takes symptoms of the crops as input with a provision of vague input and generates the output in the form of diagnosed disease using its inference engine. The proposed system caters two main crops of Pakistan, cotton and wheat and is capable to diagnose their main diseases. The proposed system has been tested on a pool of 100 real crop problems and its inference engine has shown excellent performance in prediction of the right disease which is up to 99% accurate. [ABSTRACT FROM AUTHOR]

Highlights • Discussed challenges for detection and classification of citrus plant diseases. • Briefly explains recent studies including segmentation and classification. • Compare this review with existing state of the arts. • Discussed the advantages and drawbacks of each step with detail. Abstract The citrus plants such as lemons, mandarins, oranges, tangerines, grapefruits, and limes are commonly grown fruits all over the world. The citrus producing companies create a large amount of waste every year whereby 50% of citrus peel is destroyed every year due to different plant diseases. This paper presents a survey on the different methods relevant to citrus plants leaves diseases detection and the classification. The article presents a detailed taxonomy of citrus leaf diseases. Initially, the challenges of each step are discussed in detail, which affects the detection and classification accuracy. In addition, a thorough literature review of automated disease detection and classification methods is presented. To this end, we study different image preprocessing, segmentation, feature extraction, features selection, and classification methods. In addition, also discuss the importance of features extraction and deep learning methods. The survey presents the detailed discussion on studies, outlines their strengths and limitations, and uncovers further research issues. The survey results reveal that the adoption of automated detection and classification methods for citrus plants diseases is still in its infancy. Hence new tools are needed to fully automate the detection and classification processes. [ABSTRACT FROM AUTHOR]

We report a method for precisely identifying and locating the pollen of different species in a microscope based on multihyperspectral imaging. Since pollen species are complicated and difficult to identify by one feature, both fluorescence and transmission spectral images of the samples were obtained and used for the identification together with their morphologic features. Three different species of pollens were used as test samples for investigation. The results showed that the accuracy reached 99.5%, which was a significant improvement comparing with the single spectral detection. Because more information was taken into consideration, this method has great potential for the precise identification of biological specimens. [ABSTRACT FROM AUTHOR]

Plant disease detection attracts significant attention in the field of agriculture where image based disease detection plays an important role. To improve the yield of plants, it is necessary to detect the onset of diseases in plants and advice the farmers to act based on the suggestions. In this paper, a novel web enabled disease detection system (WEDDS) based on compressed sensing (CS) is proposed to detect and classify the diseases in leaves. Statistical based thresholding strategy is proposed for segmentation of the diseased leaf. CS measurements of the segmented leaf are uploaded to the cloud to reduce the storage complexity. At the monitoring site, the measurements are retrieved and the features are extracted from the reconstructed segmented image. The analysis and classification is done using support vector machine classifier. The performance of the proposed WEDDS has been evaluated in terms of accuracy and is compared with the existing techniques. The WEDDS was also evaluated experimentally using Raspberry pi 3 board. The results show that the proposed method provides an overall detection accuracy of 98.5% and classification accuracy of 98.4%. [ABSTRACT FROM AUTHOR]

A fundamental problem that confronts deep neural networks is the requirement of a large amount of data for a system to be efficient in complex applications. Promising results of this problem are made possible through the use of techniques such as data augmentation or transfer learning of pre-trained models in large datasets. But the problem still persists when the application provides limited or unbalanced data. In addition, the number of false positives resulting from training a deep model significantly cause a negative impact on the performance of the system. This study aims to address the problem of false positives and class unbalance by implementing a Refinement Filter Bank framework for Tomato Plant Diseases and Pests Recognition. The system consists of three main units: First, a Primary Diagnosis Unit (Bounding Box Generator) generates the bounding boxes that contain the location of the infected area and class. The promising boxes belonging to each class are then used as input to a Secondary Diagnosis Unit (CNN Filter Bank) for verification. In this second unit, misclassified samples are filtered through the training of independent CNN classifiers for each class. The result of the CNN Filter Bank is a decision of whether a target belongs to the category as it was detected (True) or not (False) otherwise. Finally, an integration unit combines the information from the primary and secondary units while keeping the True Positive samples and eliminating the False Positives that were misclassified in the first unit. By this implementation, the proposed approach is able to obtain a recognition rate of approximately 96%, which represents an improvement of 13% compared to our previous work in the complex task of tomato diseases and pest recognition. Furthermore, our system is able to deal with the false positives generated by the bounding box generator, and class unbalances that appear especially on datasets with limited data. [ABSTRACT FROM AUTHOR]

High‐throughput sequencing (HTS) technologies have revolutionized plant pest research and are now raising interest for plant pest diagnostics, with plant virus diagnostics at the forefront of development. However, the application of HTS in plant pest diagnostics raises important challenges that plant health regulators will have to address. Adapted infrastructures, technical guidelines and training are pivotal for further use and adoption of the HTS technologies in the phytosanitary framework. [ABSTRACT FROM AUTHOR]

A new image recognition system based on multiple linear regression is proposed. Particularly, there are a number of innovations in image segmentation and recognition system. In image segmentation, an improved histogram segmentation method which can calculate threshold automatically and accurately is proposed. Meanwhile, the regional growth method and true color image processing are combined with this system to improve the accuracy and intelligence. While creating the recognition system, multiple linear regression and image feature extraction are utilized. After evaluating the results of different image training libraries, the system is proved to have effective image recognition ability, high precision, and reliability. [ABSTRACT FROM AUTHOR]

Specific scope: This Standard describes a diagnostic protocol for Ralstonia solanacearum, Ralstonia pseudosolanacearum and Ralstonia syzygii, i.e. phylotype/sequevar strain in the Ralstonia solanacearum Species Complex (RSSC). It should be used in conjunction with PM 7/76 Use of EPPO diagnostic protocols. Specific approval and amendment: Approved in 2003‐09. First revised in 2018‐02. [ABSTRACT FROM AUTHOR]

Specific scope: This Standard describes a diagnostic protocol for Heterodera glycines. This Standard should be used in conjunction with PM 7/76 Use of EPPO diagnostic protocols. Terms used are those in the EPPO Pictorial Glossary of Morphological Terms in Nematology. Specific approval and amendment: Approved in 2008–09. Revision approved in 2017–11. [ABSTRACT FROM AUTHOR]

Specific scope: This Standard describes a diagnostic protocol for Tilletia indica. It should be used in conjunction with PM 7/76 Use of EPPO diagnostic protocols. Specific approval and amendment: This Standard was originally developed under the EU DIAGPRO Project (SMT 4‐CT98‐2252) by a partnership of contractor laboratories and interlaboratory comparison in European countries. First approved as an EPPO Standard in 2003–09. First revision approved in 2007–09. Second revision approved on 2017–11. Although this EPPO Diagnostic Standard differs in terms of format it is in general consistent with the content of the IPPC Standard adopted in 2014 on Tilletia indica (Annex 4 to ) with the following exceptions. (1) In the EPPO region, as the pest is not present, a higher confidence in the results is required, a sieve wash test should be carried out (optional in the IPPC protocol). (2) When fewer than 10 teliospores are found the options should allow testing the (<10) teliospores with conventional or real‐time PCR (this was not an option in the IPPC protocol flow chart, although it was stated that direct real‐time PCR could be used on individual teliospores in the text). (3) The method for extracting teliospores from untreated seed or grain by size‐selective sieving is slightly different based on the experience in the region (European Union test performance study). The EPPO Diagnostic Standard also includes a test for a direct real‐time PCR for use on pellets (developed in 2016). Some additional information on methods for morphological identification, from the former version of the EPPO Standard, which are not in the IPPC protocol are included in this protocol in Appendix 3 as they were considered useful by the members of the Panel on Diagnostics in Mycology. [ABSTRACT FROM AUTHOR]

Plant diagnostic laboratories are facing increasing demands for accreditation of their testing activities from both private and public organizations. For diagnostics, validating tests for the intended use is one of the key elements of accreditation. Thus far, validation has mainly focused on tests with a fixed scope, i.e. a test for organism X in matrix Y by method Z, suitable for the analysis of targeted samples. In many laboratories, however, targeted samples only form a minority of the samples. The majority consists of symptomatic (non‐targeted) samples including a huge variety of pest, host and matrix combinations. Since it is impossible to validate all tests used for the analysis of these non‐targeted samples, ensuring the reliability of the final results needs a different approach. This paper describes such an approach for diagnostic testing in plant virology, where the extent of validation of a test is determined based on the risks of a false result within the context of the diagnostic process. Examples illustrate how this risk‐based system approach relies on a tight inter‐relationship of validation, expertise and quality assurance within the diagnostic laboratory. [ABSTRACT FROM AUTHOR]

Abstract: The one‐step real‐time turbidity loop‐mediated isothermal amplification assay (RealAmp) was developed to detect Hosta virus X (HVX), the most devastating threat to hosta industry. The reaction was performed in a single tube at 63°C for 15 min, and real‐time turbidimetry was used to monitor the amplification results. Specificity and sensitivity analyses demonstrated that this RealAmp method was sensitive as real‐time TaqMan RT‐PCR and about 100‐fold higher than conventional RT‐PCR with no cross‐reaction with other viral pathogens. Field samples detection showed that HVX could be identified effectively with this method. Overall, this RealAmp assay for HVX detection was simple, specific, sensitive, convenient and time‐saving and could assist in the quarantine measures for prevention and control of the disease caused by HVX. [ABSTRACT FROM AUTHOR]

Food is basic necessity of any living being after air and water. Plants are the major part of food consumed by living being. But the presence of fungi, bacteria, viruses etc. cause diseases in fruits, vegetables and cereal crops which directly reduce the production of crops. Plant diseases can detect with the help of digital image processing which is not easily detected by human vision. This paper describes various techniques to analyse and detection of various diseases of fruits, vegetables and cereal crops. [ABSTRACT FROM AUTHOR]

Agriculture planning plays a significant growth and food security of an agro-based country like India. In this Review we present a comprehensive and critical survey on current challenges and methodologies applied for various image processing and Machine learning approaches on variety of crops in their productivity increase, considering the following measures: Early detection/recognition of crop diseases, diagnosing methods and crop selection method in yield prediction. This paper presents an overview of existing reported techniques useful in detection of diseases in variety of crops. Finally we identify the challenges and some opportunities for future developments in this area. [ABSTRACT FROM AUTHOR]

The article focuses on the development of the diagnostic techniques for the diagnosis of the diseases caused by the microbes downy mildews in plants. Topics include the effect of the downy mildews on the quality of crops, physiology of the downy mildews as a type of oomycete, and the method of detecting the downy mildews using ribosomal DNA. It offers information on the various species of plants infected by the downy mildews.

The accurate identification of root-knot nematode (RKN) species (Meloidogyne spp.) is essential for implementing management strategies. Methods based on the morphology of adults, isozymes phenotypes and DNA analysis can be used for the diagnosis of RKN. Traditionally, RKN species are identified by the analysis of the perineal patterns and esterase phenotypes. For both procedures, mature females are required. Over the last few decades, accurate and rapid molecular techniques have been validated for RKN diagnosis, including eggs, juveniles and adults as DNA sources. Here, we emphasized the methods used for diagnosis of RKN, including emerging molecular techniques, focusing on the major species reported in Brazil. [ABSTRACT FROM AUTHOR]

The detection and identification of plant diseases is crucial for an appropriate and targeted application of plant protection measures in crop production. Recently, intensive research has been conducted to develop innovative and technology-based optical methods for plant disease detection. In contrast to common visual rating and detection methods, optical sensors are able to measure pathogen-induced changes in the plant physiology non-in-vasively and objectively. Several studies showed that especially hyperspectral sensors are valuable tools for disease detection, identification and quantification on different scales from the tissue to the canopy level. This review describes the basic principles of hyperspectral measurements and different types of available hyperspectral sensors. Possible applications of hyperspectral sensors on different scales for disease detection and plant protection are discussed and evaluated. The advantages and disadvantages on each particular scale, as well as the impact of external factors, such as: light, wind, viewing angle, for measurements in laboratories, greenhouses and fields, are critically assessed in order to support researchers and agriculture technicians. Additionally, a comprehensive literature review about the use of hyperspectral sensors on these different scales for plant disease detection reflects the possibilities of non-invasive measurement systems. This highlights advantages of hyperspectral sensors when investigating plant-pathogen interactions through multiple examples. By some approaches, detection before visible symptoms appear is feasible. The potential of hyperspectral sensors as a tool for disease identification and quantification, based on disease characteristic changes in the plants spectral signature, is discussed as well. The review is concluded with an overview on different data analysis methods, which are required to extract key information from gathered hyperspectral datasets. [ABSTRACT FROM AUTHOR]

Grafted cacti constitute an important part of South Korea's export floricultural industry. Korean grafted cacti occupy more than 70% of the world's grafted cactus market. Many diseases (head rot, stem rot, basal rot) affect different species/cultivars of cacti, including Bimoran (Gymnocalycium mihanovichii var. friedrichii), So-Jung (Notocactus scopa) and San-Chwi (Chamaecereus silvestrii). This study was conducted to investigate the occurrence of diseases and their symptoms, accompanied by a molecular diagnosis of two major exported cactus cultivars, specifically So-Jung and Bimoran. Two commercial fields, greenhouse A (GA) and greenhouse B (GB), were investigated for the existence of pathogens, incidents of disease and their severity. GA indicated more disease incidence than (GB) in consecutive seasons. In cv. So-Jung, head rot and basal rot diseases were the most predominant in both GA and GB. In cv. Bimoran, symptoms of head rot confirmed that this was the most severe disease in both fields. In fact, two diseases -- head rot and basal rot -- were caused by Alternaría sp. (head rot), Fusarium oxysporum (basal rot), respectively. Mycological characteristics and multiplex PCR-based molecular diagnosis methods confirmed the pathogens for each disease. [ABSTRACT FROM AUTHOR]

Lactobacillus plantarum strains PM411 and TC92 can efficiently control bacterial plant diseases, but their fitness on the plant surface is limited under unfavourable low relative humidity (RH) conditions. To increase tolerance of these strains to water stress, an adaptive strategy was used consisting of hyperosmotic and acidic conditions during growth. Adapted cells had higher survival rates under desiccation than non-adapted cells. Transcript levels and patterns of general stress-related genes increased immediately after the combined-stress adaptation treatment, and remained unaltered or repressed during the desiccation challenge. However, there were differences between strains in the transcription patterns that were in agreement with a better performance of adapted cells of PM411 than TC92 in plant surfaces under low RH environmental conditions. The combined-stress adaptation treatment increased the survival of PM411 cells consistently in different plant hosts in the greenhouse and under field conditions. Stress-adapted cells of PM411 had similar biocontrol potential against bacterial plant pathogens than non-adapted cells, but with less variability within experiments. [ABSTRACT FROM AUTHOR]

Plant diseases can greatly affect the total production of food and agricultural materials, which may lead to high amount of losses in terms of quality, quantity and also in economic sense. To reduce the losses due to plant diseases, early diseases detection either based on a visual inspection or laboratory tests are widely employed. However, these techniques are labor-intensive and time consuming. In a view to overcome the shortcoming of these conventional approaches, several researchers have developed non-invasive techniques. Recently, spectroscopy technique has become one of the most available noninvasive methods utilized in detecting plant diseases. However, most of the studies on the application of this novel technology are still in the experimental stages, and are carried out in isolation with no comprehensive information on the most suitable approach. This problem could affect the advancement and commercialization of spectroscopy technology in early plant disease detection. Here, we review the applications and limitations of spectroscopy techniques (visible/infrared, electrical impedance and fluorescence spectroscopy) in early detection of plant disease. Particular emphasis was given to different spectral level, challenges and future outlook. [ABSTRACT FROM AUTHOR]

Pest control strategies for crop diseases highly depend on visual inspection to assess the severity of the infection, which usually lead to inconsistencies: either over or under assessment. These inconsistencies could be attributed to the limitations of humans to perceive small differences. A more precise disease assessment is needed for better pest management decision, which will result to a more efficient utilization and allocation of resources for farm inputs. This translates to a better income for cacao farmers. This paper introduces a mobile application named AuToDiDAC or Automated Tool for Disease Detection and Assessment for Cacao Black Pod Rot (BPR). AuToDiDAC automatically detects, separates, and assesses the infection level of BPR in cacao through image processing and machine learning techniques. It gives the farmers the capacity to objectively monitor and report the infection level of the BPR compared to the common visual rating for plant disease level of infection. Pixel-level accuracy test of the tool showed an average of 84% accuracy on an independent test set of ten cacao pod images. [ABSTRACT FROM AUTHOR]

In this paper, combined image descriptors that can improve the performance of similar crop disease image retrieval system are suggested. When combining descriptors, the similarity between images is calculated using a single descriptor first. And, new similarity which corresponds to the combined descriptors is created by calculating the sum of image similarity corresponding to descriptors to be combined. Lastly, the image retrieval is carried out based on the distance value corresponding to the combined descriptors. The experiment was carried out with a total of 742 images of 3 crops including pear, grape and strawberry using the combined descriptors. As the experimental result, we discovered that using combined descriptors improved the system performance generally. And, we proved that a proper combination of descriptors varied for each crop and we found such combination. We also discovered that a combination of descriptors producing a high F-measure value of the system was different from a combination of descriptors having a higher probability that more accurate retrieval results would be outputted in the beginning of the screen. Therefore, proper combined descriptors should be selected according to actual system requirements. [ABSTRACT FROM AUTHOR]

Electronic nose technology has recently been applied to the detection of several plant diseases and pests, with promising results. However, in spite of its numerous advantages, including operational simplicity, non-destructivity, and bulk sampling, drawbacks include a low sensitivity and specificity in comparison with microbiological and molecular methods. A critical review of the use of an electronic nose for plant disease diagnosis and pest detection is presented, describing the instrumental and procedural advances of sensorial analysis, for the improvement of discrimination between healthy and infected or infested plants. In conclusion, the use of electronic nose technology is suggested to assist, direct, and optimise traditionally adopted diagnostic techniques. [ABSTRACT FROM AUTHOR]