A Detailed Review on Plant Leaf Disease Detection and Classification Methodologies using Deep Learning Techniques

The rapid emergence and evolution of deep learning methodologies in the field of plant disease classification and detection has resulted in significant progress. Their application has revolutionized the way agriculture is done. This paper provides an overview of the advancements in utilizing deep learning models to address the crucial task of identifying and categorizing plant diseases. By harnessing the power of deep convolutional neural networks (CNNs) and transfer learning, researchers have achieved remarkable accuracy in disease classification, often surpassing traditional methods. This study also delves into the challenges that persist in this field, such as the scarcity of labeled data and potential biases in models. To address these concerns, the integration of visualization techniques is explored, allowing for better model interpretation and transparency. The collaborative efforts of agricultural experts and machine learning researchers are deemed crucial for overcoming these challenges and driving the future direction of research. Looking ahead, the interdisciplinary approach is anticipated to play a pivotal role in refining deep learning models for plant disease detection. A seamless collaboration between domain-specific professionals, machine learning experts, and agricultural practitioners is essential to foster innovation, enhance the reliability of models, and create a sustainable agricultural ecosystem. With the integration of cutting-edge architectures, emerging technologies like edge computing, and broader datasets, the field is poised to bring about transformative changes in agricultural practices, bolstering crop health and productivity

STUDY ON THE LIGHTSHIP CHARACTERISTICS OF MERCHANT SHIPS

Lightship weight and its distribution have significant influence on the intact/ damage stability and longitudinal strength of the ship. In this study, the range of limiting lightship longitudinal and vertical centre of gravity for different types of merchant ships have been determined. The merchant ships considered are bulk carriers, crude oil tankers, liquefied gas carriers, container ships and pure car carriers. Detailed hull form and general arrangement layout of the merchant ships were developed. Applicable rules and regulations and design considerations for each type of merchant ships were considered for this purpose. The principal dimensions, form coefficients, powering, stability and statutory rules and regulations are matched to the ships in service. At this stage, different rules and regulations concerning ship’s stability and trim were considered. Finally, after deducting the vertical and longitudinal center of gravity of the deadweight components (cargo, fuel and fresh water), the limiting lightship vertical and longitudinal center of gravity are determined

Hierarchical Predictive Control Algorithms for Optimal Design and Operation of Microgrids

In recent years, microgrids, i.e., disconnected distribution systems, have received increasing interest from power system utilities to support the economic and resiliency posture of their systems. The economics of long distance transmission lines prevent many remote communities from connecting to bulk transmission systems and these communities rely on off-grid microgrid technology. Furthermore, communities that are connected to the bulk transmission system are investigating microgrid technologies that will support their ability to disconnect and operate independently during extreme events. In each of these cases, it is important to develop methodologies that support the capability to design and operate microgrids in the absence of transmission over long periods of time. Unfortunately, such planning problems tend to be computationally difficult to solve and those that are straightforward to solve often lack the modeling fidelity that inspires confidence in the results. To address these issues, we first develop a high fidelity model for design and operations of a microgrid that include component efficiencies, component operating limits, battery modeling, unit commitment, capacity expansion, and power flow physics; the resulting model is a mixed-integer quadratically-constrained quadratic program (MIQCQP). We then develop an iterative algorithm, referred to as the Model Predictive Control (MPC) algorithm, that allows us to solve the resulting MIQCQP. We show, through extensive computational experiments, that the MPC-based method can scale to problems that have a very long planning horizon and provide high quality solutions that lie within 5\% of optimal.Comment: To appear in "Power Systems Computation Conference", Dublin, Irelan

NUMERICAL SIMULATION OF SHIP NAVIGATION IN ROUGH SEAS BASED ON ECMWF DATA

Recently, several changes have been observed in the Earth’s environment. This is also applicable to the ocean environment. The concept of weather routing has been applied for ship navigation for a long time. Many service providers offer weather routing service with the availability of high-quality satellite data. Unfortunately, not much information is available in the public domain as to how much the recent change in the weather pattern has affected ship navigation. The purpose of this paper is to fill this information gap. We investigate the influence of recent changes in the ocean environment on ship navigation. Weather data from ECMWF, namely ERA-Interim, is used for this purpose. The ECMWF data for the last 27 years is analysed. We compute the statistical characteristics of this data for the first 10 years, last 10 years, and 27 years. The statistical characteristics of the data are determined based on “summer” and “winter” zones as defined by international maritime regulations. Six different worldwide commercial ship routes are selected covering all the ocean regions. Navigation on great ellipse with waypoint is considered. MMG type ship manoeuvring model for 3 different ship types (DTMB 5415, PCC, VLCC) is used. The added resistance due to wave, wind and the effort of keeping the ship on the desired course using autopilot in the rough ocean environment is included in the MMG model. The fuel consumption and the duration of each one of the voyage are computed. Based on the analysis and simulation results it is shown that: (i) The mean wave height, wave period, and wind speed has increased in some ocean zones and decreased in other ocean zones. If any change has occurred, it is uniform for both seasons (summer and winter). (ii) In which ocean regions there is a perceptible change in fuel consumption, average ship speed and voyage time due to the changes in the weather pattern. (iii) The changing weather pattern in different ocean zones affects each ship type differently

Atypical presentation of Hand foot mouth disease (HFMD) caused by enterovirus serotype Coxsackievirus A6, in India

A 27-year-old male presented in the OPD of Naval Hospital in Port Blair, Andaman Islands, India, in 2011 with a history of low-grade fever associated with malaise and a pruritic skin rash. Case 2 – A 17-year-old male student reported to the OPD at Naval Hospital, Kochi Kerala, India, in August 2015. He presented with eruptions on both the palm and soles with a history of high-grade fever for the past 3–4 days. Clinically, both the cases were diagnosed as hand, foot, and mouth disease (HFMD). Both samples were tested against measles virus and varicella-zoster IgM antibodies by enzyme immunoassay and found negative. Stool sample (case 1) and lesion swab (case 2) were processed by enterovirus reverse transcription polymerase chain reaction and phylogenetic analysis, and both were positive for enterovirus human coxsackievirus A6 (CVA6) (untranslated region [UTR]). Phylogenetic analysis also confirmed that both the CVA6 etiology belonged to the genotype F. HFMD in adults often asymptomatic and very few patients get atypical symptoms. Clinical diagnosis is often troublesome to identify HFMD in such cases. An epidemiological surveillance/vigilance is essential to document these atypical cases in near future in developing countries like India

Ayurvedic herbal medicine and lead poisoning

Although the majority of published cases of lead poisoning come from occupational exposures, some traditional remedies may also contain toxic amounts of lead. Ayurveda is a system of traditional medicine that is native to India and is used in many parts of world as an alternative to standard treatment regimens. Here, we report the case of a 58-year-old woman who presented with abdominal pain, anemia, liver function abnormalities, and an elevated blood lead level. The patient was found to have been taking the Ayurvedic medicine Jambrulin prior to presentation. Chemical analysis of the medication showed high levels of lead. Following treatment with an oral chelating agent, the patient's symptoms resolved and laboratory abnormalities normalized. This case highlights the need for increased awareness that some Ayurvedic medicines may contain potentially harmful levels of heavy metals and people who use them are at risk of developing associated toxicities

Cultural adaptation of Alzheimer’s disease assessment scale–cognitive subscale for use in India and validation of the Tamil version for South Indian population

Objective: Currently no standardized tools are available in the Indian languages to assess changes in cognition. Our objectives are to culturally adapt the Alzheimer’s disease Assessment Scale–Cognitive Subscale (ADAS-Cog) for use in India and to validate the Tamil version in an urban Tamil-speaking older adult population. / Methods: Two panels of key stakeholders and a series of qualitative interviews informed the cultural and linguistic adaptation of the ADAS-Cog-Tamil. Issues related to levels of literacy were considered during the adaptation. Validation of the ADAS-Cog-Tamil was completed with 107 participants − 54 cases with a confirmed diagnosis of mild-moderate dementia, and 53 age, gender and education matched controls. Concurrent validity was examined with the Vellore Screening Instrument for Dementia (VSID) in Tamil. Internal consistency using Cronbach’s alpha, sensitivity and specificity data using the Area under the Receiver Operating Characteristics (AUROC) curve values were computed. Inter-rater reliability was established in a subsample. / Results: The ADAS-Cog-Tamil shows good internal consistency (α = 0.91), inter-rater reliability and concurrent validity (with VSID-Patient version: r = –0.84 and with VSID-Caregiver version: r = –0.79). A cut-off score of 13, has a specificity of 89% and sensitivity of 90% for the diagnosis of dementia. / Conclusion: ADAS-Cog-Tamil, derived from a rigorous, replicable linguistic and cultural adaptation process involving service users and experts, shows good psychometric properties despite the limitations of the study. It shows potential for use in clinical settings with urban Tamil speaking populations. The English version of the tool derived from the cultural adaptation process could be used for further linguistic adaptation across South Asia

ResMem: Learn what you can and memorize the rest

The impressive generalization performance of modern neural networks is attributed in part to their ability to implicitly memorize complex training patterns. Inspired by this, we explore a novel mechanism to improve model generalization via explicit memorization. Specifically, we propose the residual-memorization (ResMem) algorithm, a new method that augments an existing prediction model (e.g. a neural network) by fitting the model's residuals with a $k$-nearest neighbor based regressor. The final prediction is then the sum of the original model and the fitted residual regressor. By construction, ResMem can explicitly memorize the training labels. Empirically, we show that ResMem consistently improves the test set generalization of the original prediction model across various standard vision and natural language processing benchmarks. Theoretically, we formulate a stylized linear regression problem and rigorously show that ResMem results in a more favorable test risk over the base predictor

Coupling-and Repulsion-Phase RAPDs for Tagging of Brown Planthopper Resistance Genes in the F 2 s of IR50XPtb33 of Rice

) showed co-dominant banding pattern, generated polymorphic DNA fragments, of which, OPC7 697 (697 bp) and OPAG14 680 (680 bp) were associated in coupling phase to the resistant allele, while OPC7 846 (846 bp) and OPAG14 650 (650 bp) were linked in repulsion phase. The OPC7 697 and OPAG14 680 RAPD markers could be used in a cost effective way for marker-assisted selection of BPH resistant rice genotypes