AgriSenAI: Automating UAV thermal and multispectral image processing for precision agriculture

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX Pub Date : 2025-02-11 DOI:10.1016/j.softx.2025.102083

Boaz B. Tulu , Fitsum Teshome , Yiannis Ampatzidis , Niguss Solomon Hailegnaw , Haimanote K Bayabil

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引用次数: 0

Abstract

Unmanned Aerial Vehicles (UAVs) equipped with thermal and multispectral imaging capabilities, which offer high spatial and temporal resolutions, are becoming increasingly valuable for timely crop monitoring and informed decision-making in precision agriculture. However, processing and extracting useful information from UAV images is often complex, time-consuming, and requires specialized software, which limits its broader adoption for practical field implementations. To address these challenges, the Agriculture Sensing and Artificial Intelligence (AgriSenAI), a user-friendly Python-based desktop application, was developed to automate processing and information extraction from UAV-acquired thermal and multispectral imagery. AgriSenAI was developed by integrating advanced image processing with geospatial analysis to streamline field and plot extraction, plant canopy detection, noise removal, and extraction of information at pixel, plot, and field scales. The application was designed and tested using UAV-based thermal and multispectral imagery collected daily for three years from a research field at the University of Florida's Tropical Research and Education Center in Homestead, Florida. The research field consisted of 12 plots of green beans and 12 plots of sweet corn. The processing time and accuracy of AgriSenAI were evaluated. Results showed that AgriSenAI had a very high level of accuracy in extracting pixel values and significantly reduced processing time and costs compared with traditional approaches involving commercial software. The streamlined AgriSenAI workflow produced reliable canopy temperature information and vegetation indices, demonstrating the capacity to handle large-scale datasets and enhance precision agriculture through improved efficiency and accuracy in remote sensing data processing and information extraction, which could potentially be used to inform timely and data-driven crop management decisions.

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来源期刊

SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

5.50

自引率

2.90%

发文量

184

审稿时长

9 weeks

期刊介绍： SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.