A review on plant diseases recognition through deep learning

Indrakumari, R.; Poongodi, T.; Khaitan, Supriya; Sagar, Shrddha; Balamurugan, B.

doi:10.1016/b978-0-12-823014-5.00009-0

Cited by 12 publications

(2 citation statements)

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“…It is essential to diagnose the causing pathogen first for appropriate disease diagnosis. Some common disease identification methods include manual disease identification by experts, microscopic methods, molecular and serological methods, gas chromatography, and remote sensing [29]. A brief overview of these methods is given below,…”

Section: Methods Of Disease Identificationmentioning

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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Section: Methods Of Disease Identificationmentioning

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

View full text Add to dashboard Cite

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“…Although carefully constructed imaging schemes can reduce design difficulty in these approaches, they increase costs and struggle to neutralise the impact of environmental changes on recognition results [11]. In complex natural environments, detection of plant diseases and pests encounters challenges such as small differences between the foreground and background, low contrast, varied lesion scales and types, and noise, making traditional methods ineffective for optimal detection of pest or disease [12].…”

Section: Pest and Disease Detectionmentioning

confidence: 99%

Current State, Data Requirements and Generative AI Solution for Learning-based Computer Vision in Horticulture

Baniya¹,

Lee²,

Eklund³

et al. 2023

Preprint

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The use of visual signals in horticulture has attracted significant attention and encompassed a wide range of data types such as 2D images, videos, hyperspectral images, and 3D point clouds. These visual signals have proven to be valuable in developing cutting-edge computer vision systems for various applications in horticulture, enabling plant growth monitoring, pest and disease detection, quality and yield estimation, and automated harvesting. However, unlike other sectors, developing deep learning computer vision systems for horticulture encounters unique challenges due to the limited availability of high-quality training and evaluation datasets necessary for deep learning models. This paper investigates the current status of vision systems and available data in order to identify the high-quality data requirements specific to horticultural applications. We analyse the impact of the quality of visual signals on the information content and features that can be extracted from these signals. To address the identified data quality requirements, we explore the usage of a deep learning-based super-resolution model for generative quality enhancement of visual signals. Furthermore, we discuss how these can be applied to meet the growing requirements around data quality for learning-based vision systems. We also present a detailed analysis of the competitive quality generated by the proposed solution compared to cost-intensive hardware-based alternatives. This work aims to guide the development of efficient computer vision models in horticulture by overcoming existing data challenges and paving a pathway forward for contemporary data acquisition.

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