S S Manivannan

Bio: S S Manivannan is an academic researcher from VIT University. The author has contributed to research in topics: Agriculture & Information technology. The author has co-authored 1 publications.

Intelligent Agricultural Farming System using Internet of Things

Abstract: In the present world, especially in our own country, farmers, if not rich, have a hard time adapting to the rising prices each day. Intelligent Agricultural Farming System instills on harnessing of Innovative Information Technologies as a driver of more effective, productive, and money-making agricultural organizations. These technologies must be thoughtfully combined to deliver meaningful information in near real-time.

A Contemporary Review on Deep Learning Models for Drought Prediction

Abstract: Deep learning models have been widely used in various applications, such as image and speech recognition, natural language processing, and recently, in the field of drought forecasting/prediction. These models have proven to be effective in handling large and complex datasets, and in automatically extracting relevant features for forecasting. The use of deep learning models in drought forecasting can provide more accurate and timely predictions, which are crucial for the mitigation of drought-related impacts such as crop failure, water shortages, and economic losses. This review provides information on the type of droughts and their information systems. A comparative analysis of deep learning models, related technology, and research tabulation is provided. The review has identified algorithms that are more pertinent than others in the current scenario, such as the Deep Neural Network, Multi-Layer Perceptron, Convolutional Neural Networks, and combination of hybrid models. The paper also discusses the common issues for deep learning models for drought forecasting and the current open challenges. In conclusion, deep learning models offer a powerful tool for drought forecasting, which can significantly improve our understanding of drought dynamics and our ability to predict and mitigate its impacts. However, it is important to note that the success of these models is highly dependent on the availability and quality of data, as well as the specific characteristics of the drought event.

Application of Big Data Analysis to Agricultural Production, Agricultural Product Marketing, and Influencing Factors in Intelligent Agriculture

Abstract: Agricultural Internet of things (AIoT) promotes the modernization of traditional agricultural production and marketing model. However, the existing time series prediction methods for agricultural production and agricultural product (AP) marketing cannot adapt well to most real-world scenarios, failing to realize multistep forecast of production and AP marketing data. To solve the problem, this paper explores the big data analysis of agricultural production, AP marketing, and influencing factors in intelligent agriculture. To realize long-, and short-term predictions, a small-sample time series model was set up for AIoT production, and a big-sample time series model was constructed for AP marketing. The data fusion algorithm based on Kalman filter (KF) was adopted to fuse the massive multi-source AP marketing data. The proposed strategy was proved valid through experiments.