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Dear Colleagues,

Smart agriculture draws huge interest from many researchers from not only agriculture but various other academic disciplines too. Precision agriculture pursues the management of the spatial and temporal variability of farm land and plant and animal growth for protected horticulture and livestock production, as well as open field crop production. One of the major important characteristics of precision agriculture or smart agriculture is data and information exchange through wireless communication, featuring sensor networks and control networks. Smart agriculture utilizes artificial intelligence based on big data, collected via wired or wireless communication. This Special Issue addresses recent developments in and applications of wireless monitoring and control networks for smart agriculture.

Prof. Dr. Sun-Ok Chung
Prof. Dr. Minzan Li
Guest Editors

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Keywords

smart agriculture
precision agriculture
farm monitoring and management
sensor network for agriculture
control network for agriculture

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23 pages, 12552 KiB

Open AccessArticle

Application of Artificial Intelligence and Sensor Fusion for Soil Organic Matter Prediction

by Md Jasim Uddin, Jordan Sherrell, Anahita Emami and Meysam Khaleghian

Sensors 2024, 24(7), 2357; https://doi.org/10.3390/s24072357 - 08 Apr 2024

Viewed by 423

Abstract

Soil organic matter (SOM) is one of the best indicators to assess soil health and understand soil productivity and fertility. Therefore, measuring SOM content is a fundamental practice in soil science and agricultural research. The traditional approach (oven-dry) of measuring SOM is a costly, arduous, and time-consuming process. However, the integration of cutting-edge technology can significantly aid in the prediction of SOM, presenting a promising alternative to traditional methods. In this study, we tested the hypothesis that an accurate estimate of SOM might be obtained by combining the ground-based sensor-captured soil parameters and soil analysis data along with drone images of the farm. The data are gathered using three different methods: ground-based sensors detect soil parameters such as temperature, pH, humidity, nitrogen, phosphorous, and potassium of the soil; aerial photos taken by UAVs display the vegetative index (NDVI); and the Haney test of soil analysis reports measured in a lab from collected samples. Our datasets combined the soil parameters collected using ground-based sensors, soil analysis reports, and NDVI content of farms to perform the data analysis to predict SOM using different machine learning algorithms. We incorporated regression and ANOVA for analyzing the dataset and explored seven different machine learning algorithms, such as linear regression, Ridge regression, Lasso regression, random forest regression, Elastic Net regression, support vector machine, and Stochastic Gradient Descent regression to predict the soil organic matter content using other parameters as predictors. Full article

(This article belongs to the Special Issue Wireless Monitoring and Control Network for Smart Agriculture)

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