The Survey on IoT based kit for smart farming

This survey paper presents a comprehensive analysis of 30 studies related to crop detection using machine learning and remote sensing technologies. The studies were conducted on various crops, including wheat, maize, rice, tomato, potato, soybean, and others, and utilized different sets of environmental factors such as humidity, temperature, soil pH, moisture, and nutrient content. The paper outlines the drawbacks and limitations of each model, such as the requirement for extensive training data, limited scalability, poor generalization, and high computational cost. The paper also discusses the different methodologies implemented, including deep learning, decision trees, random forests, support vector machines, and k-nearest neighbors. The datasets used in these studies were obtained from remote sensing, UAV imagery, and ground-based sensors. Overall, the paper provides valuable insights into the strengths and limitations of different machine learning models and remote sensing technologies for crop prediction, highlighting the need for further research in this area to improve the accuracy and scalability of these models.