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Soussi A, Zero E, Sacile R, Trinchero D, Fossa M. Smart Sensors and Smart Data for Precision Agriculture: A Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:2647. [PMID: 38676264 PMCID: PMC11053448 DOI: 10.3390/s24082647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/16/2024] [Accepted: 04/18/2024] [Indexed: 04/28/2024]
Abstract
Precision agriculture, driven by the convergence of smart sensors and advanced technologies, has emerged as a transformative force in modern farming practices. The present review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture. The main focus is on the integration of smart sensors, coupled with technologies such as the Internet of Things (IoT), big data analytics, and Artificial Intelligence (AI). This analysis is set in the context of optimizing crop management, using resources wisely, and promoting sustainability in the agricultural sector. This review aims to provide an in-depth understanding of emerging trends and key developments in the field of precision agriculture. By highlighting the benefits of integrating smart sensors and innovative technologies, it aspires to enlighten farming practitioners, researchers, and policymakers on best practices, current challenges, and prospects. It aims to foster a transition towards more sustainable, efficient, and intelligent farming practices while encouraging the continued adoption and adaptation of new technologies.
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Affiliation(s)
- Abdellatif Soussi
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, 16145 Genova, Italy; (E.Z.); (R.S.)
| | - Enrico Zero
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, 16145 Genova, Italy; (E.Z.); (R.S.)
| | - Roberto Sacile
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, 16145 Genova, Italy; (E.Z.); (R.S.)
| | - Daniele Trinchero
- iXem Labs, Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy;
| | - Marco Fossa
- Department Mechanical, Energy, Management and Transportation Engineering, University of Genoa, 16145 Genova, Italy;
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Tiruneh GA, Meshesha DT, Adgo E, Tsunekawa A, Haregeweyn N, Fenta AA, Reichert JM, Aragie TM, Tilahun K. Monitoring impacts of soil bund on spatial variation of teff and finger millet yield with Sentinel-2 and spectroradiometric data in Ethiopia. Heliyon 2023; 9:e14012. [PMID: 36895390 PMCID: PMC9989656 DOI: 10.1016/j.heliyon.2023.e14012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 02/03/2023] [Accepted: 02/17/2023] [Indexed: 03/03/2023] Open
Abstract
Data from remote sensing devices are essential for monitoring environmental protection practices and estimating crop yields. However, yield estimates in Ethiopia are based on time-consuming surveys. We used Sentinel-2, spectroradiometeric, and ground-truthing data to estimate the grain yield (GY) of two major crops, teff, and finger millet, in Ethiopia's Aba Gerima catchment in 2020 and 2021. At the flowering stage, we performed supervised classification on October Sentinel-2 images and spectral reflectance measurement. We used regression models to identify and predict crop yields, as evaluated by the coefficient of determination (adjusted R2) and root mean square error (RMSE). The enhanced vegetation index (EVI) and normalized-difference vegetation index (NDVI) provided the best fit to the data among the vegetation indices used to predict teff and finger millet GY. Soil bund construction increased the majority of vegetation indices and GY of both crops. We discovered a strong correlation between GY and the satellite EVI and NDVI. However, NDVI and EVI had the greatest influence on teff GY (adjusted R2 = 0.83; RMSE = 0.14 ton/ha), while NDVI had the greatest influence on finger millet GY (adjusted R2 = 0.85; RMSE = 0.24 ton/ha). Teff GY ranged from 0.64 to 2.16 ton/ha for bunded plots and 0.60 to 1.85 ton/ha for non-bunded plots using Sentinel-2 data. Besides, finger millet GY ranged from 1.92 to 2.57 ton/ha for bunded plots and 1.81 to 2.38 ton/ha for non-bunded plots using spectroradiometric data. Our findings show that Sentinel-2- and spectroradiometeric-based monitoring can help farmers manage teff and finger millet to achieve higher yields, more sustainable food production, and better environmental quality in the area. The study's findings revealed a link between VIs and soil management practices in soil ecological systems. Model extrapolation to other areas will necessitate local validation.
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Affiliation(s)
- Gizachew Ayalew Tiruneh
- Faculty of Agriculture and Environmental Sciences, Debre Tabor University, P.O.Box 272, Debre Tabor, Ethiopia.,Department of Natural Resource Management, Bahir Dar University, P.O.Box 1289, Bahir Dar, Ethiopia
| | - Derege Tsegaye Meshesha
- Department of Natural Resource Management, Bahir Dar University, P.O.Box 1289, Bahir Dar, Ethiopia
| | - Enyew Adgo
- Department of Natural Resource Management, Bahir Dar University, P.O.Box 1289, Bahir Dar, Ethiopia
| | - Atsushi Tsunekawa
- Arid Land Research Center, Tottori University, 1390 Hamasaka, Tottori, 680-0001, Japan
| | - Nigussie Haregeweyn
- International Platform for Dryland Research and Education, Tottori University, 1390 Hamasaka, Tottori, 680-0001, Japan
| | - Ayele Almaw Fenta
- Arid Land Research Center, Tottori University, 1390 Hamasaka, Tottori, 680-0001, Japan
| | - José Miguel Reichert
- Soils Department, Universidade Federal de Santa Maria (UFSM), Av. Roraima 1000, 97105-900 Santa Maria, RS, Brazil
| | - Temesgen Mulualem Aragie
- Department of Natural Resource Management, Bahir Dar University, P.O.Box 1289, Bahir Dar, Ethiopia
| | - Kefyialew Tilahun
- Department of Natural Resource Management, Bahir Dar University, P.O.Box 1289, Bahir Dar, Ethiopia
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LoRa Communications as an Enabler for Internet of Drones towards Large-Scale Livestock Monitoring in Rural Farms. SENSORS 2021; 21:s21155044. [PMID: 34372281 PMCID: PMC8348762 DOI: 10.3390/s21155044] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 07/08/2021] [Accepted: 07/13/2021] [Indexed: 01/23/2023]
Abstract
Currently, smart farming is considered an effective solution to enhance the productivity of farms; thereby, it has recently received broad interest from service providers to offer a wide range of applications, from pest identification to asset monitoring. Although the emergence of digital technologies, such as the Internet of Things (IoT) and low-power wide-area networks (LPWANs), has led to significant advances in the smart farming industry, farming operations still need more efficient solutions. On the other hand, the utilization of unmanned aerial vehicles (UAVs), also known as drones, is growing rapidly across many civil application domains. This paper aims to develop a farm monitoring system that incorporates UAV, LPWAN, and IoT technologies to transform the current farm management approach and aid farmers in obtaining actionable data from their farm operations. In this regard, an IoT-based water quality monitoring system was developed because water is an essential aspect in livestock development. Then, based on the Long-Range Wide-Area Network (LoRaWAN®) technology, a multi-channel LoRaWAN® gateway was developed and integrated into a vertical takeoff and landing drone to convey collected data from the sensors to the cloud for further analysis. In addition, to develop LoRaWAN®-based aerial communication, a series of measurements and simulations were performed under different configurations and scenarios. Finally, to enhance the efficiency of aerial-based data collection, the UAV path planning was optimized. Measurement results showed that the maximum achievable LoRa coverage when operating on-air via the drone is about 10 km, and the Longley–Rice irregular terrain model provides the most suitable path loss model for the scenario of large-scale farms, and a multi-channel gateway with a spreading factor of 12 provides the most reliable communication link at a high drone speed (up to 95 km/h). Simulation results showed that the developed system can overcome the coverage limitation of LoRaWAN® and it can establish a reliable communication link over large-scale wireless sensor networks. In addition, it was shown that by optimizing flight paths, aerial data collection could be performed in a much shorter time than industrial mission planning (up to four times in our case).
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