Portable solutions for plant pathogen diagnostics: development, usage, and future potential

Frontiers in Microbiology, Год журнала: 2025, Номер 16

Опубликована: Янв. 31, 2025

The increasing prevalence of plant pathogens presents a critical challenge to global food security and agricultural sustainability. While accurate, traditional diagnostic methods are often time-consuming, resource-intensive, unsuitable for real-time field applications. emergence portable tools represents paradigm shift in disease management, offering rapid, on-site detection with high accuracy minimal technical expertise. This review explores technologies' development, deployment, future potential, including handheld analyzers, smartphone-integrated systems, microfluidics, lab-on-a-chip platforms. We examine the core technologies underlying these devices, such as biosensors, nucleic acid amplification techniques, immunoassays, highlighting their applicability detect bacterial, viral, fungal diverse settings. Furthermore, integration devices digital technologies, Internet Things (IoT), artificial intelligence (AI), machine learning (ML), is transforming surveillance management. diagnostics have clear advantages speed, cost-effectiveness, user accessibility, challenges related sensitivity, durability, regulatory standards remain. Innovations nanotechnology, multiplex platforms, personalized agriculture promise further enhance efficacy diagnostics. By providing comprehensive overview current exploring directions, this underscores role advancing precision mitigating impact on production.

Язык: Английский

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Leveraging Artificial Intelligence for Sustainable Development in Agriculture

IGI Global eBooks, Год журнала: 2025, Номер unknown, С. 187 - 212

Опубликована: Фев. 7, 2025

In a world where sustainability has been given utmost priority, agriculture plays pivotal role. Artificial Intelligence in the agricultural sector changed landscape of across globe. ‘Agvolution' (evolution agriculture) including AI supported precision farming methods, data analytics, and robotics is novel strategy which increases crop yields using less fertilizers, energy. supports ethical farming, boost revenue, lessen negative environmental effects. systems aggregate from weather stations, sensors, satellites to produce improved forecasts. This mechanism enhances sustainability. Despite numerous advantages with AI, community face challenges like security privacy, high cost machines tools. light above, authors explore usage attain sustainability, analyze need establish governance structures for increasing food overcome faced by farmers.

Язык: Английский

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Individual action, sharing scarce resources, sharing information? A study on how to effectively manage forest pests and diseases based on carbon trading

PLoS ONE, Год журнала: 2025, Номер 20(4), С. e0322237 - e0322237

Опубликована: Апрель 28, 2025

In recent years, forest pests and diseases have had a significant impact on ecosystems. To incentivize corporations to manage diseases, the government provides certain carbon compensations enterprises involved in this management. process of controlling modes collaboration between are primarily categorized into three modes: independent action, scarce resource sharing, information sharing. determine applicability each relational mode, paper constructs differential game models compares analyzes equilibrium results obtained from these modes. The research indicates that if cost government-managed pest disease control is high benefits such low, then sharing mode can offer maximum benefit; conversely, provide with greatest benefit. If corporate-managed otherwise,

Язык: Английский

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Exploitation of volatile organic compounds for rice field insect‐pest management: current status and future prospects

Physiologia Plantarum, Год журнала: 2025, Номер 177(3)

Опубликована: Май 1, 2025

Abstract Insect pests are major biotic factors that cause significant damage to rice crops, posing a challenge global production. Synthetic pesticides the most effective and reliable technique for pest management. However, their high cost, non‐biodegradability, adverse effects on human environmental health have driven search more sustainable, eco‐friendly, economically viable alternatives. Recently, Volatile Organic Compounds (VOCs), both plant‐derived or synthetically made, emerged as promising tool insect management in diverse agricultural practices. Rice plants continuously release VOCs facilitate tritrophic interactions among plants, herbivores, natural enemies of these highlighting ecological importance. being explored semiochemicals strategies various including rice. Although applications remain laboratory stage, they hold great promise future field implementation. This review highlights role herbivore‐natural enemy explores regulating release. It provides comprehensive analysis recent advancements, ongoing challenges, prospects using Additionally, emphasizes integration with precision agriculture genetic engineering approaches along advanced monitoring technologies, develop sustainable practices agroecosystems.

Язык: Английский

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Integration of smart sensors and IOT in precision agriculture: trends, challenges and future prospectives

Frontiers in Plant Science, Год журнала: 2025, Номер 16

Опубликована: Май 14, 2025

Traditional farming methods, effective for generations, struggle to meet rising global food demands due limitations in productivity, efficiency, and sustainability amid climate change resource scarcity. Precision agriculture presents a viable solution by optimizing use, enhancing fostering sustainable practices through data-driven decision-making supported advanced sensors Internet of Things (IoT) technologies. This review examines various smart used precision agriculture, including soil moisture, pH, plant stress etc. These deliver real-time data that enables informed decision-making, facilitating targeted interventions like optimized irrigation, fertilization, pest management. Additionally, the highlights transformative role IoT agriculture. The integration sensor networks with platforms allows remote monitoring, analysis via artificial intelligence (AI) machine learning (ML), automated control systems, enabling predictive analytics address challenges such as disease outbreaks yield forecasting. However, while offers significant benefits, it faces high initial investment costs, complexities management, needs technical expertise, security privacy concerns, issues connectivity agricultural areas. Addressing these technological economic is essential maximizing potential sustainability. Therefore, this we explore latest trends, challenges, opportunities associated enabled

Язык: Английский

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Spectroscopic detection of cotton Verticillium wilt by spectral feature selection and machine learning methods

Frontiers in Plant Science, Год журнала: 2025, Номер 16

Опубликована: Май 15, 2025

Verticillium wilt is a severe soil-borne disease that affects cotton growth and yield. Traditional monitoring methods, which rely on manual investigation, are inefficient impractical for large-scale applications. This study introduces novel approach combining machine learning with feature selection to identify sensitive spectral features accurate efficient detection of wilt. We conducted comprehensive hyperspectral measurements using handheld devices (350-2500 nm) analyze leaves in controlled greenhouse environment employed Unmanned Aerial Vehicle (UAV) imaging (400-995 capture canopy-level data field conditions. The were pre-processed extract wavelet coefficients indices (SIs), enabling the derivation disease-specific (DSSFs) through advanced techniques. Using these DSSFs, we developed models assess both incidence severity leaf damage by at scale canopy scale. Initial analysis identified critical reflectance bands, coefficients, SIs exhibited dynamic responses as progressed. Model validation demonstrated achieved peak classification accuracy 85.83%, about 10% higher than traditional methods without selection. showed improved precision increased, ranging from 46.82% 93.10%. At scale, UAV-based remarkable 93.0% detection. highlights significant impact enhancing performance hyperspectral-based remote sensing monitoring. It also explores transferability across different scales, laying groundwork future early warning systems

Язык: Английский

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DSKN: Deep Spiking Kronecker Network for leaf type classification and multi-class leaf disease detection in internet of things based sustainable agriculture

Evolutionary Intelligence, Год журнала: 2025, Номер 18(3)

Опубликована: Май 31, 2025

Язык: Английский

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Employing IoT and pest sound analysis with multi-feature and multi-deep learning networks for detecting, preventing and controlling the pests in expansive farmland

International Journal of Machine Learning and Cybernetics, Год журнала: 2025, Номер unknown

Опубликована: Июнь 3, 2025

Язык: Английский

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Multi-Features and Multi-Deep Learning Networks to identify, prevent and control pests in tremendous farm fields combining IoT and pests sound analysis

Опубликована: Апрель 24, 2024

The agriculture sectors, which account for approximately 50% of the worldwide economic production, are fundamental cornerstone each nation. significance precision cannot be understated in assessing crop conditions and identifying suitable treatments response to diverse pest infestations. conventional method identification exhibits instability yields subpar levels forecast accuracy. Nevertheless, monitoring techniques frequently exhibit invasiveness, require significant time resources, susceptible various biases. Numerous insect species can emit distinct sounds, readily identified recorded with minimal expense or exertion. Applying deep learning enables automated detection classification sounds derived from field recordings, hence facilitating biodiversity assessment distribution ranges. current research introduces an innovative detecting pests through IoT-based computerized modules that employ integrated deep-learning methodology using dataset comprising audio recordings sounds. This included techniques, DTCDWT method, Blackman-Nuttall window, Savitzky-Golay filter, FFT, DFT, STFT, MFCC, BFCC, LFCC, acoustic detectors, PID sensors. proposed MF-MDLNet train, test, validate data. 9,600 auditory were examined identify their unique characteristics numerical properties. recommended system designed implemented ultrasound generator, a programmable frequency control panel preventing controlling solar-charging supplying power connected devices networks spanning large farming areas. suggested approach attains accuracy (99.82%), sensitivity (99.94%), specificity (99.86%), recall F1 score (99.89%), (99.96%). findings this study demonstrate enhancement compared previous scholarly investigations, including VGG 16, VOLOv5s, TSCNNA, YOLOv3, TrunkNet, DenseNet, DCNN.

Язык: Английский

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Evaluating the Impact of Controlled Ultraviolet Light Intensities on the Growth of Kale Using IoT-Based Systems

IoT, Год журнала: 2024, Номер 5(2), С. 449 - 477

Опубликована: Июнь 15, 2024

Incorporating Internet of Things (IoT) technology into indoor kale cultivation holds significant promise for revolutionizing organic farming methodologies. While numerous studies have investigated the impact environmental factors on growth in IoT-based smart agricultural systems, such as temperature, humidity, and nutrient levels, ultraviolet (UV) LED light’s operational efficiencies advantages still need to be explored. This study assessed efficacy 15 UV light-controlling experiments three distinct lighting groups: cultivated using conventional household lights, specialized lights designed plant cultivation, hybrid grow lights. The real-time monitoring light, soil, air conditions, well automated irrigation a water droplet system, was employed throughout experiment. experimental setup conditioning maintained temperatures at constant 26 degrees Celsius over 45-day period. results revealed that combination daylight 4000 K scored highest, indicating optimal conditions. second group exposed warm white red light exhibited slightly lower scores but larger leaf size than third grown under likely attributable reduced intensity or suboptimal levels. highlights potential address challenges posed by urbanization climate change, thereby contributing efforts mitigate carbon emissions enhance food security urban environments. research contributes positioning sustainable superfood optimizing cultivation.

Язык: Английский

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