AgroVision: Advancing Agricultural Insights with Image-Based Deep Learning Techniques

The field of precision agriculture has been advancing rapidly with the growing availability of data from sensors, drones, and satellites in the past few years. One important aspect of precision agriculture is cropping recommendation, which involves suggesting the best crops to sow in a particular area based on soil and weather conditions. In this paper, a unique method for crop recommendation using soil images and weather data collected from a flutter application is provided. The presented methodology uses convolutional neural networks (CNNs) implemented in TensorFlow Keras to analyze the soil images and weather data and make crop recommendations based on the results. The effectiveness of the proposed approach is demonstrated through extensive experimentation and comparison with existing methods. The findings demonstrate that the suggested solution performs better than existing methods concerning accuracy and efficiency and has the potential to significantly boost agricultural output while decreasing waste. This paper contributes to the growing body of research in precision agriculture and has important implications for sustainable farming practices.