Plant Disease Diagnosing Based on Deep Learning Techniques: A Survey and Research Challenges

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

The effect of queue size on the throughput, in group failure mode, for the loaded transport channel

The external data flow decreases the throughput of the transport connection. The indicator of this external load is the queue size in front of the protocol data. In this article, using a mathematical model in analytical and numerical forms, the relation between the throughput of the channel and the protocol parameters are presented including the queue size parameter. In this work the effect of the queue size on time-out duration has been shown, which is one of the important parameters and it's studied weakly in researches. Also, the relation between round-trip delay, the reliability of the transmission of the information segments with queue size are also shown

Computed tomography for evaluation of abdominal wall hernias-what is the value of the Valsalva maneuver?

PURPOSE To investigate the differences in the visibility and size of abdominal wall hernias in computed tomography (CT) with and without Valsalva maneuver. METHODS This single-center retrospective study included consecutive patients who underwent abdominal CTs with Valsalva maneuver between January 2018 and January 2022. Inclusion criteria was availability of an additional non-Valsalva CT within 6 months. A combined reference standard including clinical and surgical findings was used. Two independent, blinded radiologists measured the hernia sac size and rated hernia visibility on CTs with and without Valsalva. Differences were tested with a Wilcoxon signed rank test and McNemar's test. RESULTS The final population included 95 patients (16 women; mean age 46 ± 11.6 years) with 205 hernias. Median hernia sac size on Valsalva CT was 31 mm compared with 24 mm on non-Valsalva CT (p < 0.001). In 73 and 82% of cases, the hernias were better visible on CT with Valsalva as compared to that without. 14 and 17% of hernias were only visible on the Valsalva CT. Hernia visibility on non-Valsalva CT varied according to subtype, with only 0 and 3% of umbilical hernias not being visible compared with 43% of femoral hernias. CONCLUSIONS Abdominal wall hernias are larger and better visible on Valsalva CT compared with non-Valsalva CT in a significant proportion of patients and some hernias are only visible on the Valsalva CT. Therefore, this method should be preferred for the evaluation of abdominal wall hernias

Non-Contact Inspection of Railhead via Laser-Generated Rayleigh Waves and an Enhanced Matching Pursuit to Assist Detection of Surface and Subsurface Defects

Laser ultrasonic technology can provide a non-contact, reliable and efficient inspection of train rails. However, the laser-generated signals measured at the railhead are usually contaminated with a high level of noise and unwanted wave components that complicate the identification of defect echoes in the signal. This study explores the possibility of combining laser ultrasonic technology (LUT) and an enhanced matching pursuit (MP) to achieve a fully non-contact inspection of the rail track. A completely non-contact laser-based inspection system was used to generate and sense Rayleigh waves to detect artificial surface horizontal, surface edge, subsurface horizontal and subsurface vertical defects created at railheads of different dimensions. MP was enhanced by developing two novel dictionaries, which include a finite element method (FEM) simulation dictionary and an experimental dictionary. The enhanced MP was used to analyze the experimentally obtained laser-generated Rayleigh wave signals. The results show that the enhanced MP is highly effective in detecting defects by suppressing noise, and, further, it could also overcome the deficiency in the low repeatability of the laser-generated signals. The comparative analysis of MP with both the FEM simulation and experimental dictionaries shows that the enhanced MP with the FEM simulation dictionary is highly efficient in both noise removal and defect detection from the experimental signals captured by a laser-generated ultrasonic inspection system. The major novelty contributed by this research work is the enhanced MP method with the developments of, first, an FEM simulation dictionary and, second, an experimental dictionary that is especially suited for Rayleigh wave signals. Third, the enhanced MP dictionaries are created to process the Rayleigh wave signals generated by laser excitation and received using a 3D laser scanner. Fourth, we introduce a pioneer application of such laser-generated Rayleigh waves for inspecting surface and subsurface detects occurring in train rails

GENETIC ANALYSIS FOR GRAIN YIELD AND VARIOUS MORPHOLOGICAL TRAITS IN MAIZE (ZEA MAYS L.) UNDER NORMAL AND WATER STRESS ENVIRONMENTS

ABSTRACT A genetic analysis study was carried out for various morphological traits in a complete 8 × 8 diallel cross of maize inbred lines under normal irrigation and drought conditions. Estimation of genetic components of variation and graphical presentation deduced that most of the traits like days to pollen shed, anthesis-silking interval, ear height, kernel rows per ear, 100-kernel weight, shelling percentage, grain yield per plant showed over-dominance type of inheritance under both normal and drought conditions unlike leaf rolling which showed partial dominance under normal but over-dominance type of inheritance under drought conditions. It can be inferred that because of over-dominance nature of inheritance of most of the yield related traits, heterosis breeding can be pursued to exploit high yielding hybrids with considerable drought tolerance

Techniques for extraction of bioactive compounds from plant materials: a review

The use of bioactive compounds in different commercial sectors such as pharmaceutical, food and chemical industries signifies the need of the most appropriate and standard method to extract these active components from plant materials. Along with conventional methods, numerous new methods have been established but till now no single method is regarded as standard for extracting bioactive compounds from plants. The efficiencies of conventional and non conventional extraction methods mostly depend on the critical input parameters; understanding the nature of plant matrix; chemistry of bioactive compounds and scientific expertise. This review is aimed to discuss different extraction techniques along with their basic mechanism for extracting bioactive compounds from medicinal plant

Comparative Genomic Studies in Leguminous Species

Abstract: Comparative Genomics is relatively a new discipline of Genetics, which deals with the relationships between the genomes of genera, species, varieties or strains. For elaborating comparative genomics of legumes; fourteen accessions belonging to seven species viz; Vigna radiate, Vigna mungo, Vicia ervilia, Vicia faba, Phaseolus vulgaris, Cicer arietinum and Lens culinaris, were analyzed using Randomly Amplified Polymorphic DNA (RAPD). Alleles of various sizes ranging from 500-1400 bp were amplified. On an average 3.5 allele per genotype were amplified. The average genetic distance ranged from 4-91%. Phylogenetic relationship among the legumes based upon DNA analysis was studied through dendrogram analysis. All the accessions were clustered in 6 groups. It was found that Vigna radiata and Vigna mungo accessions were most distantly related to each other