Agriculture is essential for the entire global population. An advanced, robust, and empirically sound agriculture sector is essential for nourishing the global population. Various leaf diseases cause financial hardships for farmers and related businesses. Early identification of foliar diseases in crops would greatly help farmers, leading to a substantial increase in agricultural productivity. The tomato is a widely recognized and nourishing food that is easily accessible and highly favored by farmers. Early diagnosis of tomato leaf diseases is crucial to maximize tomato crop production. This study aims to utilize a deep learning approach to accurately detect and classify damaged leaves and disease patterns in tomato leaf images. By employing a substantial quantity of deep convolutional network models, we achieved a high level of precision in diagnosing the condition. The dataset used in our study work is a self-contained dataset obtained by direct observation of tomato fields in rural areas of Bangladesh. It consists of four classes: healthy, black mold, grey mold, and powdery mildew. In this study work, we utilized various image pre-processing techniques and applied VGG16, InceptionV3, DenseNet121, and AlexNet models. Our results showed that the DenseNet121 model attained the higher accuracy of 97%. This discovery guarantees accurate detection of tomato diseases in a rapid manner, ushering in a new agricultural revolution.

With the rapid technological advancement the increasing cell phone dependency has led to set up cellular towers in numerous communities. These towers are known as base stations, have antennas and electronic equipment that transmit and receive RF (radio-frequency) signals. Various experimental and epidemiological analysis have been done for searching the potential effect of BTS (Base Transceiver Station) establishment in residential areas and result shows significant health issues, as the RF radiation can mess with the human body’s own EMFs. Therefore, for ensuring public safety it is essential to ascertain the minimum safe distance of establishing BTSs from locality. In this calculation based empirical work, on the basis of SAR (Specific Absorption Rate) mentioned by the World Health Organization, we have analyzed and proposed the minimum reasonable distance of nine major human tissues for being affected by the radiation. This analysis has been done at different frequency bands based on a specific type of antenna at Bangladesh context.