Year: 2024
Type: conference-paper
Abstract: Deep learning is widely used in agriculture. This study created a system for monitoring soil pH and nutrient levels by training deep learning models, visualizing trends and patterns with charts and display the test result using maps. Specifically, it achieved the following objectives. 1) to identify the features of the Deep Learning Based Web Application; 2) to determine the appropriate model for analyzing soil pH level data; 3) to develop a user-friendly interface for farmers to visualize deep learning predictions and interactive map; 4) to determine the acceptability ratio of the deep-learning based web application among farmers. The main features are daily pH reading record, digital chart, and an interactive Map. Both LSTM and GRU models may serve as viable alternatives for pH level prediction. The farmer and their heirs can easily identify which farm lots have sufficient or deficient soil nutrient levels using Google Map. Deep learning-based application has 80% acceptability ratio. The study concludes that the features enable farmers to make informed decisions by providing clear and actionable insights into soil nutrient levels and pH trends, thus enhancing farm management and productivity.
Author: Excel Philip Badere Guidang
Institution: University of Abra
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDGs: Zero hunger, Life on land
Open Access status: gold
OpenAlex ID: W4411747331