An application for Farmers to recommend them the best types of crops which can be cultivated on a certain piece of land using Soil Image.
The field of precision agriculture has been advancing rapidly in recent years, with the increasing availability of data from sensors, drones, and satellites. One important aspect of precision agriculture is crop recommendation, which involves suggesting the best crops to plant in a particular area based on soil and weather conditions.
we provided a unique method for crop recommendation using soil images and weather data collected from a flutter application. Our method 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. We demonstrate the effectiveness of our approach through extensive experimentation and comparison with existing methods.
Our findings demonstrate that our suggested strategy surpasses current approaches in terms of accuracy and efficiency and has the potential to greatly increase agricultural yields and decrease waste. This project contributes to the growing body of research in precision agriculture and has important implications for sustainable farming practices.
Flutter TensorFlow-Kereas CNN Algorithm
