The purpose of this study is to identify the exact location of cyclones to avert cyclone-related damage. Knowing about natural disasters like cyclones in advance will help with planning and preparations as they can be extremely dangerous. Numerous methods have been developed in the past to forecast cyclones and gauge their severity. It is a difficult task that demands swiftness and effectiveness. On the INSAT-3D dataset, a hybrid model of CNN and Bidi-rectional GRU is created in this study to estimate the position of the next cyclone. The INSAT-3D satellite pictures’ IR and visible images are analyzed and segmented using K-means clustering. The model’s training time is 400 ms, as observed. There have been comparisons between a variety of prior methods, including CNN-LSTM and a hybrid model that combines CNN and Bidirectional-LSTM. The suggested model’s experimental results show a training loss of 58.6879.
Cyclones are one of the deadliest natural calamities capable of causing immense destruction. Knowing about natural disasters like cyclones in advancehelps in planning and preparations as they can beextremely dangerous. Numerous methods have beendeveloped in the past to track cyclones and gaugetheir severity after eye formation. The purpose ofthis paper is to track the movement of cyclones beforethe eye formation to avert cyclone-related damage ina fast and efficient way. This paper majorly focuseson cyclone forecasting before the formation of the eyewhich is a difficult task and demands effectiveness.The INSAT-3D satellite pictures consisting of IR andvisible images are analyzed and segmented using Detectron. Comparisons have been made between various alternate models, including CNN-LSTM and ahybrid model that combines CNN and BidirectionalLSTM. The model put forth in this paper is a combination of CNN and Bidirectional-GRU. The hybridmodel of CNN and Bidirectional GRU is trained onthe INSAT-3D dataset to estimate the next positionof the cyclone. The suggested model’s experimental results show an MSE of 1613.65 and an SSMI of(1.0,1.0).
A major part of the Indian economy relies on agriculture, thus identification of any diseased crop in the initial phase is very important as these diseases cause a significant drop in agricultural production and also affect the economy of the country. Tomato crops are susceptible to various diseases which may be caused due to transmission of diseases through Air or Soil. We have tried to automate the procedure of detection of diseases in the Tomato Plant by studying several attributes related to the leaf of the plant. Using various machine learning algorithms such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), ResNet, and InceptionV3 we have trained the model, and based on the results obtained we have evaluated and compared the performance of these algorithms on different features set. For the dataset we had 10 classes (healthy and other unhealthy classes) having a total of 18,450 images for the training of the models. After implementing all of the algorithms and comparing their results we found that the ResNet was most appropriate for extracting distinct attributes from images. The trained models can be used to detect diseases in Tomato Plant timely and automatically.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.