New Fish meal plant at Karwar to process oil sardine

Karnataka recorded an appreciable catch of 1,00,179 t of oil sardine in 2007. The boom in oil sardine landings has lead to the establishment of a new fish meal plant at Baithkol landing centre of Karwar which became functional in March, 2008 (Fig. 1). The Karnataka Fisheries Development Board has leased out the fish meal plant to a private company, Sai Annapoorna Bio-Protein Private Ltd. The products manufactured by the fish meal plant are fish oil and fish meal powder. They supply fish oil to CP Aquamarine which exports the fish oil to south-east Asian countries such as Vietnam and Thailand. The fish meal powder is used as prawn feed and it is procured and marketed by CP Aquamarine. The company procures fish from Mangalore to Goa from boat owners and agents. The average supply of fish is 200 t/day. The company processes only oil sardine (Sardinella longiceps) for the manufacture of fish meal and fish oil

Techno-economic performance of mechanised fishing in Karwar, Karnataka

The techno-economic and financial performance of mechanised purse seiners and trawlers operating in Karwar Fishing Harbour was compared using various indicators. The average diesel consumption per trip was 179 l for purse seiners and 79 l for trawlers. The average operating cost and gross revenue per trip were `21,818 and `44,383 respectively for purse seiners and `4,803 and `6,571 respectively for trawlers. Oilsardines and mackerels contributed more than 85% of the catch of purse seiners whereas shrimps and flatfishes contributed nearly 50% of the catch of trawlers. Capital productivity was higher (operating ratio - 0.49) for purse seiners than trawlers (operating ratio - 0.73). The economic and financial performance indicators like net benefit-earnings ratio (0.43), benefit-cost (BC) ratio (1.75) and internal rate of return, IRR (117%) were higher for purse seiners which suggested that the investment on purse seiners is a more viable undertaking when compared to trawlers in the location

Machine learning for the classification of breast cancer tumor: a comparative analysis

The detection and diagnosis of Breast cancer at an early stage is a challenging task. With the increase in emerging technologies such as data mining tools, along with machine learning algorithms, new prospects in the medical field for automatic diagnosis have been developed, with which the prediction of a disease at an early stage is possible. Early detection of the disease may increase the survival rate of patients. The main purpose of the study was to predict breast cancer disease as benign or malignant by using supervised machine learning algorithms such as the K-nearest neighbor (K-NN), multilayer perceptron (MLP), and random forest (RF) and to compare their performance in terms of the accuracy, precision, F1 score, support, and AUC. The experimental results demonstrated that the MLP achieved a high prediction accuracy of 99.4%, followed by random forest (96.4%) and K-NN (76.3%). The diagnosis rates of the MLP, random forest and K-NN were 99.9%, 99.6%, and 73%, respectively. The study provides a clear idea of the accomplishments of classification algorithms in terms of their prediction ability, which can aid healthcare professionals in diagnosing chronic breast cancer efficiently

Development of a computer aided decision support system for the design of drip irrigation laterals

Drip Design Check (DDC) is a powerful software solution designed to assist irrigation system designers in evaluating the effectiveness of their drip irrigation designs for non-uniform slope conditions. The software makes use of recent developments in drip design methods as well as the advancements in software development methodologies for simulating and optimizing the design of laterals. The software features an easy-to-use interface that allows users to input key design parameters, including slope details, discharge rate of drippers, length of lateral, spacing between drippers and pressure head at the inlet of lateral. The software calculates relative pressure head variation (vh) and pressure head values at each outlet. The software evaluates the goodness of the design based on the allowable relative pressure head variation. Simulation of lateral also considers dripper connection losses. Variations in lateral and dripper configurations are handled by the software in line with the intuition of the user’s perceptions. Hence, data input is simple and easy. The software can be used to analyse numerous design alternatives and to identify the most appropriate design. DDC has undergone several tests using different typical sample data and hence its accuracy and reliability are more. Demonstrations were conducted for designers to assess the user friendliness of the drip simulation software, and we received favourable feedback from them. In summary, Drip Design Check is a useful and reliable tool for the irrigation industry to check the goodness of lateral design

Development of a web-based simulation application for efficient drip irrigation submain design

Drip simulation software is essential for accurately optimizing and maximizing the efficiency of drip irrigation systems, enabling precise water management and resource conservation. The present study developed a powerful web-based application to assist irrigation system designers in evaluating the effectiveness of the submain design on uniform or non-uniform slope conditions. The software facilitates the simulation and optimisation of submain design by incorporating modern drip design approaches and state-of-the-art software development methodologies. With its intuitive user interface, the software allows users to effortlessly enter important design parameters, including slope specifications, lateral discharge rates, submain length, lateral spacing and submain inlet pressure head. The software calculates to determine the pressure head values at each outlet and the relative variation in pressure head (vh), allowing for comprehensive design evaluation. Extensive testing using various typical sample data ensured the high accuracy and reliability of the developed web application. It empowers users to explore multiple design alternatives and determine the most suitable option. Rigorous testing, employing various typical sample data, has further enhanced the accuracy and reliability of the developed application. Live demonstrations were conducted to evaluate its user-friendliness, yielding overwhelmingly positive feedback from designers. The software can be accessed conveniently via the website https://www.dripdesigncheck.in/telescopic/submain, ensuring easy availability to users

Some observations on the structure and life-history of Cercaria andhraensis N. Sp.(Trematoda: Echinostomatidae) from the apple snailPila globosa swainson of waltair

The morphology, anatomy and stages in life-history of Cercaria andhraensis n.sp. from the apple snailPila globosa Swainson of Waltair have been described.C. andhraensis has 33 spines which are inconspicuous in the cercaria but are clearly seen in the metacercaria occurring in the snail host itself. The anatomy of the cercarial tail is given in detail. Differences in the tegument of the body and the tail are indicated. The tail tegument more clearly gives the impression that the so-called tegument of trematodes is a modified epidermis. It is suggested that the cercaria and the metacercaria may one day prove to be the stages of either Echinostoma govindum Moghe, 1932, or E. crecci Verma, 1936

Heavy landing of Charybdis smithii and need for proper utilization

Heavy landings of Charybdis smithii during the January to March, 2020 was documented in Mangalore fisheries harbour. These crabs were the part of trawl discards as geo-coded in situ data collection on trawl discards showed that C. smithii was available along Karnataka coast during August to December and in May as pelagic or semi-pelagic shoals from a depth range of more than 100 m. Landing of this species in Fisheries Harbours was generally rare since there was very limited market demand for these crab

Strategic selection of white maize inbred lines for tropical adaptation and their utilization in developing stable, medium to long duration maize hybrids

White maize plays an important role in human diet, especially in traditional crop growing regions of northern hill region, north-eastern states and central-western parts of India. Breeding efforts to enhance the genetic potential of white maize was not so prominent as compared to yellow maize in the country. As a result, genetic base of the material utilized in white maize breeding program in India is very narrow and majorly contains indigenous germplasm and few introductions. Hence, efforts were made to use 365 white maize inbred lines from CIMMYT, Mexico, for breeding program. These new inbred lines were grown at winter nursery center, Indian Institute of Maize Research, New Delhi for its tropical adaptation. After preliminary evaluation, a total 47 inbred lines were selected and evaluated in randomized complete block design with two replications at Regional Maize Research and Seed Production Centre, Begusarai, Bihar, during rabi 2014. Out of this top performing 12 inbred lines viz, CML 47, CML 95, CML 314, CML 319, CML 377, CML 488, CML 494, CML 504, CML 517, CML 522, CML 531 and CML 538 were selected and were crossed in diallel manner to obtain 66 medium to long duration experimental hybrids. Stability analysis using AMMI model was done to identify adaptive hybrids with high yielding potentiality. According to the ASVi value obtained, the hybrid G38 appeared to be stable followed by G50 and G44. On the other hand, the hybrid G25 appeared as location specific hybrid suitable for high input conditions

A comprehensive survey exploring the application of machine learning algorithms in the detection of land degradation

Early and reliable detection of land degradation helps policymakers to take strict action in more vulnerable areas by making strong rules and regulations in order to achieve sustainable land management and conservation. The detection of land degradation is carried out to identify desertification processes using machine learning techniques in different geographical locations, which are always a challenging issue in the global field. Due to the significance of the detection of land degradation, this article provides an exhaustive review of the detection of land degradation using machine learning algorithms. Initially, the current status of land degradation in India is presented, along with a brief discussion on the overview of widely used factors, evaluation parameters, and algorithms used. Consequently, merits and demerits related to machine learning-based land degradation identification are presented. Additionally, solutions are prescribed in order to reduce existing problems in the detection of land degradation. Since one of the major objectives is to explore the future perspectives of machine learning-based land degradation detection, areas including the application of remote sensing, mapping, optimum features, and algorithms have been broadly discussed. Finally, based on a critical evaluation of existing related studies, the architecture of the machine learning-based desertification process has been proposed. This technology can fulfill the research challenges in the detection of land degradation and computation difficulties in the development of models for the detection of land degradation