Image based Plant leaf disease detection using Deep learning

Agriculture is important for India. Every year growing variety of crops is at loss due to inefficiency in shipping, cultivation, pest infestation in crop and storage of government-subsidized crops.  There is reduction in production of good crops in both quality and quantity due to Plants being affected by diseases. Hence it is important for early detection and identification of diseases in plants. The proposed methodology consists of collection of Plant leaf dataset, Image preprocessing, Image Augmentation and Neural network training. The dataset is collected from ImageNet for training phase. The CNN technique is used to differentiate the healthy leaf from disease affected leaf. In image preprocessing resizing the image is carried out to reduce the training phase time. Image augmentation is performed in training phase by applying various transformation function on Plant images. The Network is trained by Caffenet deep learning framework. CNN is trained with ReLu (Rectified Linear Unit). The convolution base of CNN generates features from image through the multiple convolution layers and pooling layers. The classifier part of CNN classifies the image based on the features extracted from the convolution base. The classification is performed through the fully connected layers. The performance is measured using 10-fold cross validation function. The final layer uses activation function like softmax to categorize the outputs

Increased levels of ligands of Toll-like receptors 2 and 4 in type 1 diabetes

Type 1 diabetes is a proinflammatory state characterised by increased levels of circulating biomarkers of inflammation and monocyte activity. We have shown increased Toll-like receptor 2 (TLR2) and TLR4 expression and signalling in monocytes from type 1 diabetic patients. Several endogenous ligands of TLR2 and TLR4 have been identified; however, there is a paucity of data on levels of these endogenous ligands in diabetes. Thus, the aim of this study was to examine circulating levels of exogenous/endogenous ligands of TLR2 and TLR4 in type 1 diabetic patients and to compare these with the levels in matched healthy controls. Healthy controls (n = 37) and type 1 diabetic patients (n = 34) were recruited, and a fasting blood sample was obtained. Circulating levels of endotoxin, heat-shock protein 60 (Hsp60), high-mobility group box 1 (HMGB1) and growth arrest-specific 6 (GAS6) proteins were assessed by ELISA, and TLR2 and TLR4 expression was determined by flow cytometry. Levels of the classical TLR4 ligand, endotoxin, were significantly elevated in type 1 diabetic patients compared with those in matched controls. Hsp60 and HMGB1 concentrations were also significantly increased in the patients (p < 0.01 and p < 0.001, respectively). No significant differences were observed in GAS6. We report the novel observation that levels of ligands of TLR2 and TLR4 are significantly elevated in type 1 diabetes, and this, in concert with hyperglycaemia, accounts for the increase in TLR2 and TLR4 activity, underscoring the proinflammatory state of type 1 diabetes

Drug utilization study in ophthalmology out patients in a tertiary care teaching hospital

Background: The objective of the study was to evaluate the utilization of the drugs in the ophthalmology outpatient department.Methods: Present study was conducted at ESIC medical college & PGIMSR Ophthalmology Department. Total 700 prescriptions were collected from 700 patients and prescriptions were analysed for total and average number drugs per prescription, duration of treatment, dosage form, drug encounter with antibiotics and other group of drugs also percentage of drugs prescribed by generic names.Results: After analysing the prescriptions, average number of drugs per prescription was 2.14 and the range of drugs prescribed were varied from 1-5. Total drugs prescribed were 1502 with 7 different dosage forms. Most commonly prescribed were antibiotics (28.14%) followed by antihistamines (14.28%) and vitamins and minerals (14.28%). Thirdly ocular lubricants were frequently prescribed (12.12%). Fluroquinolones (moxifloxacin) were very commonly prescribed in antibiotics. The common prescription writing errors were very minimal.Conclusions: The present study revealed trend of prescribing practices of the Opthalmologists of the Institute. This study shows less polypharmacy, use of injection was limited and majority of the drugs prescribed in generic and hospital formulary drug list. Antibiotics were prescribed most frequently

A new species of Argulus (Brachiura) from a marine fish Psammoperca waigiensis (Cuvier)

A specimen of ArguluJ taken from the body surface of the marine perch Psammo- perca waigiensis (Cuvier) caught from the Palk Bay near Mandapam has been found to be a new species, and its description is given here. Species and subspecies of the genus Argulus Milller so far recorded from India are A. indieus Weber, A. gigan- teus Ramakrishna, A. bengalen.ri.r Ramakrishna, A. siameni-is Wilson, A. siamensis penin.rulari.r Ramakrishna and A. puthenvelien.ri.r Ramakrishna (see Ramakrishna, 1951, 1962 ) . The postembryonic development of A. puthenvelien.ri.r has been dealt with by Thomas (1961). Thomas & Devaraj (in press) have described two new species, namely A. fluviatili.r and A. cauveriensis collected from the river Cauvery

Secondary Metabolites and Nutrient Balance in Casuarinas: an Insight Into Protein Competition Model (PCM)

The total phenolics, total condensed tannins (TCT), nitrogen (N) and total protein (TP) in needles of Casuarina equisetifolia and Casuarina junghuhniana were studied to understand the carbon-nutrient balance (CNB) and the growth-differentiation balance (GDB) hypotheses. The carbon-nutrient balance (CNB) hypothesis postulates that phenolic levels in plants are determined by the balance between carbon and nutrient availability1. The growth-differentiation balance (GDB) hypothesis2 considers factors that limit growth and differentiation. The production of phenolics dominates when factors other than photosynthate supply are suboptimal for growth (e.g., under nutrient limitation). Resource-based theories assume that the synthesis of defensive compounds is constrained by the external availability of resources and internal trade-offs in resource allocation between growth and defense. It is stated that growth processes dominate over the production of defensive compounds and that more carbon is left for defensive compounds only when plant growth is restricted by a lack of mineral nutrient (emphasized by the CNB hypothesis) or by any factor (according to the GDB hypothesis). Jones and Hartley3 presented a protein competition model (PCM) for predicting total phenolics allocation and content in leaves of higher plants. Protein competition model (PCM) stated that “protein and phenolics synthesis compete for the common, limiting resource phenylalanine,” so nitrogen (N) rather than C is the limiting resource for synthesis of phenolics. In our study, the contents of Total Phenolics, and Total Condensed Tannin (TCT) in needles of C. equisetifolia were higher than the C. junghuhniana. However, Total protein and nitrogen (N) contents were higher in C. junghuhniana than C. equisetifolia. There was a significant negative correlation between Total phenolics, TCT and Total Protein, N contents. Therefore, it is found from the present investigation that C. equisetifolia follows CNB hypothesis. However, C. junghuhniana follows GDB hypothesis, since it contains low defense chemicals viz., phenolics & TCT and high nitrogen and protein contents. Hence, the adaptability of C. equisetifolia in coastal areas and C. junghuhniana in drier inland condition is realized

Analytical study on maternal and fetal outcome of pre-eclampsia with severe features at tertiary care hospital

Background: Pre-eclampsia is a multi-system, pregnancy specific disorder that is characterized by the development of hypertension and proteinuria after 20 weeks. Pre-eclampsia is the majority of referrals to tertiary care centre. It is one of the major causes of maternal and perinatal morbidity and mortality. Methods: A retrospective analytical study done over a period of six months from January 1st 2023 to June 30th 2023. Pregnant women admitted with PE with severe features to Cheluvamba hospital, MMCRI, Mysore during the study were considered and analysed using the proforma. Data was entered into Microsoft excel data sheet and was analyzed. Categorical data was represented in the form of Frequencies and proportions Results: Incidence of PE with severe features in our hospital was 3.4%. Majority (69%) were between 23-27 years of age and 52.7% were primigravida. Maternal complications were noted in 37.5% attributed to renal dysfunction, postpartum haemorrhage, DIC, placental abruption, HELLP, pulmonary oedema and postpartum eclampsia. Conclusions: Maternal and perinatal complications are more in patients with severe pre-eclampsia. The incidence of severe pre-eclampsia can be reduced by early referral, better antenatal care, early recognition and treatment of pre-eclampsi

Relevance vector machine based fault classification in wind energy conversion system

This Paper is an attempt to develop the multiclass classification in the Benchmark fault model applied on wind energy conversion system using the relevance vector machine (RVM). The RVM could apply a structural risk minimization by introducing a proper kernel for training and testing. The Gaussian Kernel is used for this purpose. The classification of sensor, process and actuators faults are observed and classified in the implementation. Training different fault condition and testing is carried out using the RVM implementation carried out using Matlab on the Wind Energy Conversion System (WECS). The training time becomes important while the training is carried out in a bigger WECS, and the hardware feasibility is prime while the testing is carried out on an online fault detection scenario. Matlab based implementation is carried out on the benchmark model for the fault detection in the WECS. The results are compared with the previously implemented fault detection technique and found to be performing better in terms of training time and hardware feasibility

Hysteresis-based Voltage and Current Control Techniques for Grid Connected Solar Photovoltaic Systems: Comparative Study

Solar PV system development and integration with existing grid is very fast in recent years all over the world, as they require limited maintenance, pollution free and simple structure. When observing the factors affecting the performance of the grid connected solar photovoltaic system, the inverter output voltage with harmonics add with the harmonics generated due to the non-linear loads, retain a bigger challenge to maintain power quality in the grid. To maintain grid power quality, better inverter control technique should be developed. This paper presents the two control techniques for grid-tied inverters. This study developed the hysteresis controller for the inverter. Hysteresis controller used in this work two way (i) Voltage control mode (ii) Current control mode. Matlab/Simulink model is developed for the proposed system. Further the study presents the comparative evaluation of the performance of both control techniques based on the percentage of total harmonic distortion (THD) with the limits specified by the standards such as IEEE 1547 and IEC 61727 and IEEE Std 519-201