A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Ali, Zain Anwar; Yang, Chenguang; Israr, Amber; Zhu, Quanmin

doi:10.3390/drones7020097

Cited by 6 publications

(2 citation statements)

References 144 publications

(135 reference statements)

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“…Therefore, the establishment of a centralized national database platform and other valuable tools such as artificial intelligence need to be developed to record accurate and uniform data across institutions to make evidence-based decisions by policymakers and development planners. Furthermore, as wheat is highly susceptible to various fungal and bacterial diseases, automatic disease detection systems using unmanned aerial vehicles (UAV) serve as powerful tools to control diseases, as described in Ali et al (2023).…”

Section: Establish a Centralized Database Platformmentioning

confidence: 99%

The irrigated wheat initiative of Ethiopia: a new paradigm emulating Asia’s green revolution in Africa

Effa,

Fana,

Nigussie

et al. 2023

Environ Dev Sustain

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The aim of this paper is to assess and document the experience of the successful irrigated wheat initiative in Ethiopia, identify potential limitations, and suggest alternative options that contribute towards the sustainability of the program. In the past decades, Ethiopia has initiated several projects to ensure food and wheat security but did not achieve the anticipated impact as planned. In 2019, however, the current government of Ethiopia put in place structural, economic, and sectorial reforms, with wheat chosen as a strategic commodity for food security, raw material for the agroindustry, import substitution that transits to export, and job creation along the value chain. With a total of 2.6 million ha of land cultivated under both rain-fed and irrigated systems, followed by a record amount of wheat harvest (8.2 million tons) in 2022, Ethiopia achieved a wheat self-sufficiency ratio of 100% and more than 1 million tons of surplus for export, indicating that the new irrigated wheat initiative of Ethiopia has been found transformational and is becoming a game changer. Moreover, modest case scenario projections revealed that Ethiopia will become a net exporter of wheat by 2023, indicating that the country is achieving not only wheat security but also championing the possibility of replicating Asia’s Green Revolution in Africa. Yet, the sustainability of the current initiative relies on an efficient water management system, private sector engagement, sustainable input supply, well-established input and output marketing systems, and leadership commitment at all levels.

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Section: Establish a Centralized Database Platformmentioning

confidence: 99%

The irrigated wheat initiative of Ethiopia: a new paradigm emulating Asia’s green revolution in Africa

Effa,

Fana,

Nigussie

et al. 2023

Environ Dev Sustain

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“…Various Agritech startups are providing services of drone-based monitoring and disease identification which reduces the imports and boosts the exports of a country. UAVs are not only economically beneficial but also environmentally friendly as they produce negligible or no pollution [6].…”

Section: Introductionmentioning

confidence: 99%

AI-Based UAV Swarms for Monitoring and Disease Identification of Brassica Plants Using Machine Learning: A Review

Ali¹,

Deng²,

Shaikh³

et al. 2024

CSSE

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Technological advances in unmanned aerial vehicles (UAVs) pursued by artificial intelligence (AI) are improving remote sensing applications in smart agriculture. These are valuable tools for monitoring and disease identification of plants as they can collect data with no damage and effects on plants. However, their limited carrying and battery capacities restrict their performance in larger areas. Therefore, using multiple UAVs, especially in the form of a swarm is more significant for monitoring larger areas such as crop fields and forests. The diversity of research studies necessitates a literature review for more progress and contribution in the agricultural field. In this review, the comparative analysis of existing literature surveys is explored. This paper aims to provide an overview of AIbased UAV swarms, different cameras and sensors, image processing, and machine learning (ML) algorithms for image analysis having the purpose of monitoring and disease identification. Brassica plants are focused as they are grown on wider scales globally. Brassica species, the commonly infected diseases, and different types of disease detection methods are discussed. Investigations show the significance of using UAV swarms for growth monitoring growth for yield estimation, health monitoring, water status monitoring and irrigation management, nutrition disorders monitoring, pest and disease detection, and pesticide and fertilizer spraying in Brassica plants. Finally, some challenges of swarm-based applications are also addressed that require future consideration. The significance of this paper is that it suggests its readers embrace swarm-based technologies in the pursuit of more efficient production with relevant economic benefits. KEYWORDSArtificial intelligence (AI); Brassica; disease identification; image analysis; image processing; machine learning (ML); monitoring; swarm; remote sensors; unmanned aerial vehicles (UAVs) UAV Swarm Brassica Plants ML Approaches Research FocusFigure 1: Review framework with the research focusWe searched articles in different databases, such as Science Direct, Google Scholar, SpringerLink, SAGE Journals, IEEE Xplore, and Wiley Online Library Journal. In the advanced search section of these database repositories, the period from 2018 to 2023 was selected. While plotting the framework we ensured to include all the important studies. After selecting the first batch of research papers, their related articles were also searched. To extract relevant articles, we read the abstract of all the retrieved articles. The screening criteria include deleting non-English and duplicate papers. We included the papers which were focused on optical sensors and machine learning approaches. We excluded the papers which were focused on other remote sensing tools and sensors. This review is proposed for farmers and researchers who are inclined to study Brassica plants and UAV swarms. The major contributions of the paper include:

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An aerial framework for Multi-View grape bunch detection and route Optimization using ACO

Ariza-Sentís,

Vélez,

Baja

et al. 2024

Computers and Electronics in Agriculture

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A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Cited by 6 publications

References 144 publications

The irrigated wheat initiative of Ethiopia: a new paradigm emulating Asia’s green revolution in Africa

The irrigated wheat initiative of Ethiopia: a new paradigm emulating Asia’s green revolution in Africa

AI-Based UAV Swarms for Monitoring and Disease Identification of Brassica Plants Using Machine Learning: A Review

An aerial framework for Multi-View grape bunch detection and route Optimization using ACO

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