A field experiment was conducted at the AICRP on Management of Saline Water and Associated Salinization in Agriculture, RVSKVV, College of Agriculture, Indore, at Salinity Research Farm, Baphalgaon, Barwaha, District Khargone (M.P.), to evaluate the effects of gypsum and Gliricidia sepium green leaf biomass on the growth and yield of wheat under sodic Vertisols. The experiment was laid out in a split-plot design with three replications and comprised three levels of gypsum requirement (0, 50 and 75% GR) and three levels of Gliricidia biomass (0, 5 and 10 t ha-1). The results showed that increasing levels of gypsum and Gliricidia significantly improved plant population, plant height, number of tillers, dry matter accumulation, yield attributes, and wheat yield. The 75% GR treatment recorded the highest plant population (130.0 m-2), plant height at harvest (89.1 cm), tillers per plant (6.60), dry matter accumulation (22.73 g plant-1), grain spikes (19.8 plant-1), test weight (24.02 g), grain yield (3878.3 kg ha-1), straw yield (5150.0 kg ha-1), and biological yield (9028.3 kg ha-1). Similarly, the application of 10 t ha-1 Gliricidia produced superior growth and yield parameters. The combined use of gypsum and Gliricidia was effective in improving wheat productivity under sodic soil conditions. The study indicates that the integrated application of 75% gypsum requirement together with 10 t ha-1 Gliricidia biomass is a promising strategy for sustainable wheat production in sodic Vertisols of the Nimar Valley.
Ashu Patle, Bharat Singh, Divya Bhayal et al.· International Journal of Pla...· 0 citations
Breast cancer is one of the most common and life-threatening diseases, and early and accurate diagnosis is essential for the enhancement of survival rates. Histopathological image analysis is regarded as the gold standard of diagnosing breast cancer; nevertheless, manual practice carried out by the pathologists is time-consuming, subjective, and inter-observer variability may be present. The recent development in artificial intelligence and deep learning has made it possible to analyze medical images automatically, providing more diagnostic assistance and faster. The Automated Breast Cancer Detection Framework Using Hybrid CNN Models on Histopathological images that we propose in this paper combines various convolutional neural network (CNN) models to augment the feature detection and classification results. The hybrid model suggested is an integration of the merits of pre-trained deep learning models like the ResNet50, DenseNet121, and InceptionV3 to extract low-level and high-level features of histopathological images. The system consists of preprocessing, feature fusion, hybrid deep learning-founded classification, and decision support mechanisms. Experimental findings show that the proposed model yields an accuracy of 97.6%, precision of 96.9%, a recall of 96.4%, and an F1-score of 96.6%, and outperforms the traditional machine learning models as well as standalone CNN models. The results reveal that the hybrid CNN-based schemes can greatly enhance the accuracy and robustness of classification in using histopathological images.
P. Palsodkar, Naveen, Gagandeep Kaur et al.· International Conference Com...· 0 citations
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