Mango Leaf Disease Recognition and Classification Using Novel Segmentation and Vein Pattern Technique

Mango fruit is in high demand. So, the timely control of mango plant diseases is necessary to gain high returns. Automated recognition of mango plant leaf diseases is still a challenge as manual disease detection is not a feasible choice in this computerized era due to its high cost and the non-availability of mango experts and the variations in the symptoms. Amongst all the challenges, the segmentation of diseased parts is a big issue, being the pre-requisite for correct recognition and identification. For this purpose, a novel segmentation approach is proposed in this study to segment the diseased part by considering the vein pattern of the leaf. This leaf vein-seg approach segments the vein pattern of the leaf. Afterward, features are extracted and fused using canonical correlation analysis (CCA)-based fusion. As a final identification step, a cubic support vector machine (SVM) is implemented to validate the results. The highest accuracy achieved by this proposed model is 95.5%, which proves that the proposed model is very helpful to mango plant growers for the timely recognition and identification of diseases.

Jakobeus surgery for pleural adhesions

When using an artificial pneumothorax (i.p.) there are often cases when it is the diseased part of the lung that cannot be compressed because of pleural adhesions, as a result of which the cavern continues to gape, the patient excretes sputum and bacilli, and continuation of i. i., in fact, is completely aimless. In rare cases, when the adhesions preventing the recession of the cavern are very thin, it is possible to rupture these adhesions by forceful blowing and bringing them to positive pressures. However, this method cannot be recommended for a number of reasons.

A review on the emerging use of targeted therapy by Nanoparticles and Nano medicine

There are number of innovations have been made in the field of medicine to give the ill patient the best of the treatment. As such the use of nanoparticles has also become an increasing demand because of their remarkable actions on the edge of targeted drug therapy. There are number of disorders that must be treated by the way as a specific treatment for the diseases like cancer in which the main purpose of the formation of these particles is to protect the healthy cells from the harmful effects of the drugs. And this could be only possible by making the drugs as specific in their action so these can only target the cells of interest as the abnormal one. Nano medicines are small particles which when given to the patient cause san improvement in the abnormality by better absorption and better distribution to the cells like abnormal one. So it is need of hour to make such drugs which have remarkable effects on the diseased part of body.

Extraction of Multiple Diseases in Apple Leaf Using Machine Learning

This paper proposes a novel algorithm of segmentation of diseased part in apple leaf images. In agriculture-based image processing, leaf diseases segmentation is the main processing task for region of interest extraction. It is also extremely important to segment the plant leaf from the background in case on live images. Automated segmentation of plant leaves from the background is a common challenge in the processing of plant images. Although numerous methods have been proposed, still it is tough to segment the diseased part of the leaf from the live leaf images accurately by one particular method. In the proposed work, leaves of apple having different background have been segmented. Firstly, the leaves have been enhanced by using Brightness-Preserving Dynamic Fuzzy Histogram Equalization technique and then the extraction of diseased apple leaf part is done using a novel extraction algorithm. Real-time plant leaf database is used to validate the proposed approach. The results of the proposed novel methodology give better results when compared to existing segmentation algorithms. From the segmented apple leaves, color and texture features are extracted which are further classified as marsonina coronaria or apple scab using different machine learning classifiers. Best accuracy of 96.4% is achieved using K nearest neighbor classifier.

A Fully Automated Crop Disease Monitoring and Management System Based on IoT

In recent years, the IoT has evolved and plays a significant role in many fields like smart city, precision farm, traffic signal control system, and so on. In this chapter, an IoT-based crop disease management (CDM) system is proposed that adopts statistical methods for identifying disease, recognizing a right pesticide, and recommending a right pesticide to farmers. The proposed CDM system monitors the agricultural crops with the help of a CCD camera. The camera continuously photographs the crops and sends them to a Raspberry PI processor, which is placed at a workstation and it is connected to the camera with the help of IoT components. The proposed CDM system analyses the crop leaf images, such as removes noise; segments region of interest (RoI), that is, diseased part of the leaf image; extracts features from the RoI; and identifies the disease and takes appropriate measures to control the disease. The proposed IoT-based CDM system was experimented, and the results obtained encourage both the farmers and the researchers in this field.

Effects of heat and mass transfer on the peristaltic motion of Sutterby fluid in an inclined channel

PurposeThe purpose of this paper is to study the peristaltic mechanism of Sutterby fluid in a symmetric channel with mass and heat transfer.Design/methodology/approachMass and heat transfer are investigated in the assumption of Reynolds number and the long wavelength. The velocity, temperature and concentration terms for small values of Sutterby fluid parameter are achieved.FindingsGraphical results have been introduced for various important parameters. The effects of emerging key parameters are also highlighted.Originality/valueSutterby fluid model is one that represents the high polymer aqueous solutions. It is now strongly believed that any diseased part of the body would be cured better when exposed to magnetic force when compared with a drug. Peristalsis with mass and heat transfer occurs in treatment to destroy the unwanted tissues, hemodialysis and oxygenation process, etc.

The Role of Agni karma in the management of Planter Fasciitis

Planter fasciitis is commonly found in 80% population in the society which is caused due to the affection of calcaneum. It is often seen in females and individuals over 35 years. It is one of the most troublesome complaints affecting the individuals in their routine work. Acharya Sushruta suggests the disease planter fasciitis can be correlated with Vatakantaka which is caused by vitiated Vata dosha due to constant standing and walking. Planter fasciitis can develop due to walking on uneven roads with ill fitting footwear which causes silent and repeated injury resulting into inflammation of planter fascia which results into painful heel, tenderness, early morning stiffness & restricted movements of heel. Sushruta has mentioned different methods of management of diseases, such as Bheshaja karma, Kshara karma, Agni karma, Shastrakarma and Raktamokshana. Aim-To study the role of Agnikarma in the management of Planter Fascitis. Objectives: To study planter fasciitis.To study the role of Agnikarma in planter fasciitis. Results and Discussion: Chronic Planter fasciitis can develop due to use of walking on uneven roads with ill fitting footwear which causes silent and repeates injury resulting into inflammation of planter fascia which results into painful heel, tenderness, restricted movements of heel, During the use of Agnikarma chikitsa, we take the Agni in the shalaka by making it Red Hot on fire. The Agni from shalaka is to be transferred to the diseased part, the dhatoo (twacha). The Dhatu Agni gets utkleshit (Activated). The activated dhatoo Agni use to produce Dosh Pachan.

To sider af et spejlhav, adskilt fra hinanden af et fælles sprog? Kroppen i narrativ antropologi og videnskabelig medicin

This paper addresses the vexed question of the intellectual relationships between physicians and medical anthropologists sometimes incorporated in the same body. It suggests that they are more divided than they think because neither side always recognises that the former are, by definition, required to be pragmatic and the latter, analytical. They are less divided than they think because both groups have recently moved towards focusing their study on the body; physicians rather than just on the diseased part, anthropologists instead of on reified, but disembodied, culture. The new developments, however, may lead to the concealing and reinforcing of a deeper division based on their diverse ways of seeing the body. They use each other as mirrors and, as through a glass darkly, see themselves as the despised other, and others as an esteemed self.

Maize Leaf Disease Severity Analysis using Integrated Color Filtering and Threshold Masking

As the color is a dominant factor for isolation of the diseased part of corn maize, the detachment of disease affected parts in maize plant leaf is achieved using integrated Color Filtering followed by a threshold masking. The Particular HSV from a color image of maize plant is extracted .Major steps involved here is to initially convert an RGB image of disease affected maize plant to HSV and second is to apply a threshold mask to filter out the green color of healthy maize plant and detach the brown and yellow diseased area thereof. This method is applied and tested with around 30 maize leaves, and the results found that the proposed methodology performs well with overlapped healthy maize leaf compared to K-means Clustering algorithm. False Positive is produced in K-means method and this Proposed system as integrates with Color Filtering and thresholding works well with overlapped images so that it increases True negative as the Accuracy of the proposed method increases. This proposed methodology identifies well with perfect maize leaf images and misclassifications occur only with images with dark shadows, light illuminations and sanded background.

Acinetobacter puyangensis sp. nov., isolated from the healthy and diseased part of Populus ×euramericana canker bark

Two Gram-negative, non-motile, rod-shaped strains, BQ4-1T and NHI3-2, isolated respectively from the healthy and diseased part of Populus ×euramericana canker bark, were characterized using a polyphasic approach. Chemotaxonomic characterization supported the inclusion of the two strains in the genus Acinetobacter , with genomic DNA G+C contents (42.5–43 mol%) within the range observed for this genus (38–47 mol%) and 9-octadecenoic acid (C18 : 1ω9c, 39.87 %), hexadecanoic acid (C16 : 0, 11.26 %) and summed feature 3 (comprising C16 : 1ω7c/C16 : 1ω6c, 18.90 %) as major fatty acids. Phylogenetic analysis based on 16S rRNA, rpoB and gyrB gene sequences revealed that strains BQ4-1T and NHI3 did not cluster with any species with validly published names, and formed a distinct cluster with 99–100 % bootstrap support on three phylogenetic trees within the genus Acinetobacter . Acid was not produced from d-glucose, and haemolysis was not observed on agar media supplemented with sheep erythrocytes. On the basis of phenotypic, genotypic and phylogenetic characteristics, the two strains are considered to represent a novel species of the genus Acinetobacter , for which the name Acinetobacter puyangensis sp. nov. is proposed. The type strain is BQ4-1T ( = CFCC 10780T = JCM 18011T).