Present and future scopes and challenges of plant pest and disease (P&amp;D) monitoring: Remote sensing, image processing, and artificial intelligence perspectives

Abdullah, Hasan Muhammad; Mohana, Nusrat T.; Khan, Bhoktear M.; Ahmed, Syed Masud; Hossain, Maruf; Islam, K. S.; Redoy, Mahadi H.; Ferdush, Jannatul; Bhuiyan, Md. Abdullahil Baki; Hossain, Md. Motaher; Ahamed, Tofayel

doi:10.1016/j.rsase.2023.100996

Cited by 9 publications

(4 citation statements)

References 112 publications

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“…Advanced imaging and deep learning technologies have ushered in a revolution in agricultural pest management and crop health assessment. These innovative developments have combined the power of deep learning, including deep neural networks, with a range of imaging modalities, such as hyperspectral, RGB, multispectral, IR, and NIR, to reshape the landscape of agriculture ( Figure 3 ) ( Chandel et al., 2021 ; Liang et al., 2021 ; Abdullah et al., 2023 ). This integration has given birth to innovative solutions for pest detection, classification, and localization leading to a significant improvement in agricultural efficiency.…”

Section: Technological Innovations In Vegetable Cultivationmentioning

confidence: 99%

Advancing horizons in vegetable cultivation: a journey from ageold practices to high-tech greenhouse cultivation—a review

Ahmed,

Zhang,

Deng

et al. 2024

Front. Plant Sci.

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Vegetable cultivation stands as a pivotal element in the agricultural transformation illustrating a complex interplay between technological advancements, evolving environmental perspectives, and the growing global demand for food. This comprehensive review delves into the broad spectrum of developments in modern vegetable cultivation practices. Rooted in historical traditions, our exploration commences with conventional cultivation methods and traces the progression toward contemporary practices emphasizing the critical shifts that have refined techniques and outcomes. A significant focus is placed on the evolution of seed selection and quality assessment methods underlining the growing importance of seed treatments in enhancing both germination and plant growth. Transitioning from seeds to the soil, we investigate the transformative journey from traditional soil-based cultivation to the adoption of soilless cultures and the utilization of sustainable substrates like biochar and coir. The review also examines modern environmental controls highlighting the use of advanced greenhouse technologies and artificial intelligence in optimizing plant growth conditions. We underscore the increasing sophistication in water management strategies from advanced irrigation systems to intelligent moisture sensing. Additionally, this paper discusses the intricate aspects of precision fertilization, integrated pest management, and the expanding influence of plant growth regulators in vegetable cultivation. A special segment is dedicated to technological innovations, such as the integration of drones, robots, and state-of-the-art digital monitoring systems, in the cultivation process. While acknowledging these advancements, the review also realistically addresses the challenges and economic considerations involved in adopting cutting-edge technologies. In summary, this review not only provides a comprehensive guide to the current state of vegetable cultivation but also serves as a forward-looking reference emphasizing the critical role of continuous research and the anticipation of future developments in this field.

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Section: Technological Innovations In Vegetable Cultivationmentioning

confidence: 99%

Advancing horizons in vegetable cultivation: a journey from ageold practices to high-tech greenhouse cultivation—a review

Ahmed,

Zhang,

Deng

et al. 2024

Front. Plant Sci.

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“…This system extracts features such as color and texture, enabling quantitative analysis for highly accurate early detection and diagnosis by farmers. Intelligent farming, incorporating image-based approaches with GSM, remote sensing, or other telecommunication technologies, allows remote crop monitoring, aiding in early disease detection and preventing further crop loss ( Abdullah et al, 2023 ).…”

Section: Interventions and Control Of Asian Soybean Rust In Asiamentioning

confidence: 99%

Understanding Phakopsora pachyrhizi in soybean: comprehensive insights, threats, and interventions from the Asian perspective

Hossain,

Sultana,

Yesmin

et al. 2024

Front. Microbiol.

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Soybean (Glycine max L.) is an important crop in Asia, accounting for 17% of global soybean cultivation. However, this crop faces formidable challenges from the devastating foliar disease, Asian Soybean Rust (ASR), caused by Phakopsora pachyrhizi, a biotrophic fungus with a broad host range, causing substantial yield losses (10–100%) in Asia. This comprehensive review consolidates knowledge on ASR, encompassing its impact, historical perspectives, genetic diversity, epidemic drivers, early detection, risk assessment, and sustainable management strategies of ASR in the region. ASR has expanded globally from Asia, reaching Africa and Americas, driven by wind-dispersed urediniospores. Genetic diversity studies reveal the complexity of P. pachyrhizi, with distinct populations exhibiting varying virulence patterns. Factors affecting ASR epidemics in Asia include host susceptibility, landscape connectivity, climate, and environmental conditions. Understanding the interplay of these factors is essential for early intervention and control of ASR in soybean fields. Effectively managing ASR can exploit the utilization of diverse intervention strategies, encompassing disease forecasting, automated early detection, disease resistance, fungicide application, and biological control. A pivotal aspect of successful, sustainable disease management lies in reducing the ASR pathogen virulence and preventing it from developing fungicide resistance, while the highpoint of effectiveness in disease control is attained through a synergistic approach, integrating various strategies. In summary, this comprehensive review provides insights into multifaceted approaches that contribute to the development of sustainable and economically impactful soybean production in the face of the persistent threat of ASR in Asia.

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“…Similarly, yield-related phenotypes require manual calculations after wheat maturation and threshing, involving long experimental cycles, low estimation efficiency, high labor intensity, and significant time costs. Moreover, with the advancement of remote sensing technology, its efficiency, comprehensive information acquisition, and independence from terrain conditions have led to its widespread application in agriculture [6][7][8][9][10]. Since 1970, satellite remote sensing has been extensively used for large-scale crop yield prediction due to its excellent spatial, temporal, and spectral resolution.…”

Section: Introductionmentioning

confidence: 99%

Method for Monitoring Wheat Growth Status and Estimating Yield Based on UAV Multispectral Remote Sensing

Zhu,

Li,

Wang

et al. 2024

Agronomy

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An efficient and accurate estimation of wheat growth and yield is important for wheat assessment and field management. To improve the accuracy and stability of wheat growth and yield estimation, an estimation method based on a genetic algorithm-improved support vector regression (GA-SVR) algorithm was proposed in this study. The correlation analysis between vegetation indices calculated from spectral data and wheat growth phenotypes and yields was performed to obtain the optimal combination of vegetation indices with high correlation and good estimation performance. At the same time, the optimal model for wheat growth monitoring was screened and constructed in experiments with 12 wheat varieties and 3 gradient nitrogen fertilizer application levels. Then, the yield estimation model was established and its applicability was verified under different nitrogen fertilizer application levels. The results showed that the constructed models for the leaf area index, plant height, and yield estimation performed well, with coefficients of determination of 0.82, 0.71, and 0.70, and root mean square errors of 0.09, 2.7, and 68.5, respectively. This study provided an effective UAV remote sensing technique for monitoring wheat growth status and estimating yield. This study provides an effective unmanned aerial remote sensing technique for monitoring wheat growth and estimating yield, and provides technical support for wheat yield assessment and field management.

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Present and future scopes and challenges of plant pest and disease (P&D) monitoring: Remote sensing, image processing, and artificial intelligence perspectives

Cited by 9 publications

References 112 publications

Advancing horizons in vegetable cultivation: a journey from ageold practices to high-tech greenhouse cultivation—a review

Advancing horizons in vegetable cultivation: a journey from ageold practices to high-tech greenhouse cultivation—a review

Understanding Phakopsora pachyrhizi in soybean: comprehensive insights, threats, and interventions from the Asian perspective

Method for Monitoring Wheat Growth Status and Estimating Yield Based on UAV Multispectral Remote Sensing

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