The energy which is obtained from the sun in the form of light and heat is known as solar energy. Nowadays, technology is focused on utilization of this rich resource of energy. So many ways to harness and utilize this source of energy have already been introduced; solar drying of crops is one of its applications. Drying of crops is required to improve the quality of the crops as well as to protect crops from so many unwanted issues like moisture, pest/insect attacks, and birds/animals. Traditional methods are still used to dry the crops; drying of crops is required to preserving food product for long time. In this paper, we studied about the working mechanism of indirect solar dryers and introduced IoT-based system to control and monitor the temperature of the solar dryer as per the requirement of specific crop. To achieve the automation accurately and precisely deep learning method is also used to set the required temperature according to the requirement of the specific crop.

This research looked into the viability of using metaheuristic algorithms in conjunction with an adaptive neurofuzzy system to predict seismicity and earthquakes. Different metaheuristic algorithms have been combined with an artificial intelligence (AI) algorithm. Subjected to seismicity is a promising factor. The new sensors have many advantages over the older, more impressive‐looking ones, including (a) a generally linear relationship between the measured values and real ground motion (described above), (b) the ability to measure three orthogonal components of ground movement in a single unit, (c) sensitivity to a very broad range of frequencies, and (d) high dynamic range, which allows for the detection of both very small and fairly large tremors. To accept the acquired results as a hybrid model of an adaptive neurofuzzy inference system with particle swarm optimization (PSO), genetic algorithm (GA), and extreme machine learning (ELM) (ANFIS‐PSO‐GA‐ELM) implemented. According to the dataset, all approaches produce excellent and realistic predictions of seismic loads; however, the method ANFIS‐PSO produces better results. All the strategies demonstrated a high level of predictability. Finally, this research urges researchers to investigate the performance of triple hybrid MT algorithms using a variety of hybrid metaheuristic methodologies, rather than the existing double hybrid MT algorithms.

Agriculture is essential for living in the Indian Himalayan region (IHR), as it functions as the main occupation and source of income. Therefore, monitoring crops and detecting diseases at early stages becomes essential. Our research leverages deep learning (DL), a dynamically growing technology proficient in handling large datasets. In this work, convolutional neural networks (CNNs) are used to solve real‐world problems using the computer vision technique. This paper proposes a modified AlexNet architecture for detecting diseases through images at an early stage and to increase the production rate by cultivating good quality crops. This model combines steps such as tomato leaf image data collection, image data preprocessing, and classification and detection of disease. In this work, a total of 5500 tomato leaf images have been taken from Kaggle, and an additional 1650 photographs have been collected from a variety of field sites, thereby creating a comprehensive dataset that will facilitate the development and assessment of a robust model. In the classification and detection of tomato plant leaf diseases, our proposed model achieves an accuracy of 94.80%. Using DL technology, we hope to give farmers an accurate way to monitor and protect their crops, ultimately enhancing agricultural quality and output.