Soil Condition Analysis Using Deep Learning

This study provides a complete overview of the use of deep learning techniques in soil condition analysis and crop selection in the agricultural area. Deep learning, a type of machine learning, has emerged as a potent tool for modelling intricate data patterns, making it ideal for assessing complex soil characteristics critical to crop development and yield. Deep learning algorithms, using artificial neural networks, can effectively process a variety of data sources, including satellite imagery, sensor readings, historical records, and weather data, to provide insights into soil moisture content, nutrient levels, soil texture, and other relevant features. Furthermore, deep learning models can assist in crop selection by incorporating characteristics like as seasonality, regional climatic patterns, and the distinction between Kharif and Rabi crops, which are crucial for improving farming techniques and increasing output. This paper examines the most recent research in this topic, focusing on major approaches, problems, and future directions. It also investigates the implications of implementing deep learning in agriculture, including its capacity to enhance food security, transform contemporary agricultural practices, and mitigate environmental damage.