JoWUA

Abstract

Precision farming is gaining much popularity as it increases crop yields and, at the same time, reduces input costs. Soil is a major factor in precision agriculture, particularly in terms of nutrient management, where we analyze and determine the quantity of nutrients in the soil that need to be supplemented with fertilizers to improve crop yield. Traditional soil nutrient detection methods were time-consuming and labor-intensive, making them unsuitable for large-scale agricultural planting. In this work, we leverage automatic, real-time soil nutrient mapping in precision agriculture through deep learning and sensor fusion. The approach is based on deep learning and utilizes both soil sensors and remote sensing. They have been used to relate sensor data to soil properties through deep learning. By sensor fusion, better prediction ability and precision are achieved. The procedure begins by capturing field and environmental data using sensors and drones and collecting thousands of data samples. The data is preprocessed and then processed through a deep learning model, which was trained to map soil nutrients. In-plant nutrient level prediction model: In this situation, plants with known nutrient levels will be selected only for model calibration. This farm nutrient level prediction model achieves optimal farm nutrient levels using remote sensing information. The model improves its ability to predict soil conditions over time as it learns and iterates. The method of the invention offers several benefits compared to traditional methods of soil nutrient mapping. It Saves You Time with Soil Sampling and Precise Measurements.