Early detection of plant diseases is essential for effective crop disease management to prevent yield loss. In this study, we developed a methodology for classifying diseases in rice leaves using four deep learning models and a dataset with 2658 images of healthy and diseased rice leaves. Four models, namely LeafNet, Modified LeafNet, MobileNetV2, and Xception, were compared. The Modified LeafNet model involved updates to LeafNet’s architectural parameters, whereas transfer learning techniques were applied to the MobileNetV2 and Xception pretrained models. The optimal hyperparameters for training were determined by considering several factors such as batch size, data augmentation, learning rate, and optimizers. The Modified LeafNet model achieved the highest accuracies of 97.44% and 87.76% for the validation and testing datasets, respectively. In comparison, LeafNet obtained 88.92% and 71.84%, Xception obtained 88.64% and 71.95%, and MobileNetV2 obtained 82.10% and 67.68% for the validation and test accuracies on the same datasets, respectively. This study contributes to the development of automated disease classification systems for rice leaves, thereby leading to increased agricultural productivity and sustainability.

Personal safety is concerned to be as a crucial part for the industrial workers while working in an industrial environment. Industries provide personal protective equipment to their workers to ensure their safety, similarly the workers are also meant to wear and follow all the regulations regarding the personal protective equipment’s (PPEs) provided to them. Our study provides the methodology to detect the industrial safety helmet using the surveillance cameras. In this study, we have trained two different single shot detector models i.e., Single Shot Detector (SSD) MobilenetV2 and Single Shot Detector (SSD) Resnet50 and used transfer learning methodology to detect the industrial safety helmet. We have utilized a publically available dataset from Kaggle website and utilized that dataset for the purpose of training the models. Furthermore, the models evaluation is done based on these parameters i.e., classification loss, localization loss, regularization loss and total loss. However, we concluded that the SSD Mobilenet V2 performs better than SSD Resnet50 model based on loss parameters. For SSD Mobilenet v2 we achieved a classification loss of 0.11, localization loss of 0.05, regularization loss of 0.15, and a total loss as 0.32 respectively. Moreover, the graphs for the loss of each model has also been studied.

This paper aims to provide a solution where home automation systems can reduce electricity consumption by using Internet of Things technology based on HC-SR501 PIR motion sensor, DS18B20 temperature sensor, and LDR analog sensor in conjunction with NodeMCU ESP8266 microcontroller and relays so that the home appliances can be automatically controlled based on human presence, temperature, and ambient light. The proposed solution includes a web application for users to give an idea of the amount of electricity consumed with daily and monthly unit cost, as well as to provide automatic and manual control functions of the appliances. PZEM-004T electrical energy sensor used with the NodeMCU ESP8266 to determine energy consumption. The hardware prototype was tested rigorously for an ample amount of time. The test results confirmed that the prototype functioned well and could control electrical appliances automatically and manually via the web app. Firebase is used as the cloud-hosted database to provide cloud infrastructure through which the web app and ESP8266 communicate with each other. The web app successfully collected electrical energy data and displayed the daily and monthly costs on the app's dashboard, as well as real-time motion, temperature, and light intensity.