Deep learning model for temperature prediction: A case study in New Delhi

Abstract

This study is based on temperature prediction in the capital of India (New Delhi). We have adopted different ML models such as (MPR and DNN) which are designed and implemented for temperature prediction. The MPR models are varied on the degree of the polynomial, whereas the DNN models differ in the number of input parameters. DNNM‐1 takes date, time, and temperature as input, and DNNM‐2 receives date, time, temperature, pressure, humidity, and dew point as input parameters, whereas DNNM‐3, is a complex model that takes date, time, temperature, pressure, humidity, dew point, and 32 weather conditions as input. To evaluate the accuracy of the predictions, a comparison of the predicted temperature and the actual recorded temperature is done, and the performance and accuracy of the models are examined. The MPR models work well in case of fewer input features, but as the number of input features grows, the DNN model outperforms the MPR models. The DNN model (DNNM‐3) outperformed the other models with better accuracy as compared to past evidence.

Constraints identified in production and marketing of farm produce in Prayagraj district of Uttar Pradesh

“…Therefore, agriculture is key sector for all strategic planning for socio-economic development of our country. Speedy growth of agriculture is necessary not only to achieve self-reliance at country level but also for household food security and to bring about equity in distribution of wealth and income resulting in quick reduction in poverty line (Shrivastava et al 2010) [2] . In the study area farm community facing various constraints related to cultivation and marketing of farm produce of different crops.…”

Optimized Deep Learning Model for Disease Prediction in Potato Leaves

“…A kind of machine learning [4][5][6] called deep learning (DL) has been demonstrated to be particularly good at classifying images [5][6][7][8][9]. Convolutional Neural Network (CNN) is used in a variety of tasks, including object identification and image classification [10][11][12][13]. To predict potato leaf diseases, an optimized deep learning model is tuned in this work.…”