An Extensive Study on Precision Farming Based on Crop Yield Using Integrated Approaches to Learning

Precision agriculture aims to optimize the use of resources by tailoring them to specific field conditions. Previous research has investigated the use of IoT devices, remote sensing techniques, and machine learning algorithms to develop variable rate technology systems. Smart agriculture has been a topic of extensive research and development, with numerous studies focusing on different aspects of its implementation. Existing approaches use remote sensing techniques to predict the yield, but remote sensing imagery is typically acquired at a certain spatial resolution, which may need to be finer to capture small-scale variations within a field or distinguish individual plants or crops. This can limit the accuracy and precision of crop yield predictions, particularly when dealing with heterogeneous fields or small plot sizes. Traditional statistical models often assume linear relationships between input variables and crop yield. However, machine learning algorithms can identify and capture nonlinear relationships, interactions, and complex patterns in the data. This ability to model nonlinearities is particularly advantageous in agriculture, where multiple factors influence crop growth and yield and exhibit nonlinear behaviour. IoT sensors and imaging technologies can collect data on crop health indicators. Machine learning algorithms can analyze this data to identify patterns and detect early signs of crop diseases, nutrient deficiencies, or pest infestations. Early detection enables prompt action and targeted interventions, minimizing the spread of diseases and reducing the need for excessive pesticide use. This paper focuses on the comparative study of crop yield prediction and recommendation using different mechanisms.