Innovative Hybrid Deep Learning Strategy for Detecting and Classifying White Rot in Onions DOI
Arshleen Kaur, Vinay Kukreja,

Mukesh Kumar

et al.

2022 IEEE 7th International conference for Convergence in Technology (I2CT), Journal Year: 2024, Volume and Issue: unknown

Published: April 5, 2024

The disease severity of onion white rot has to be measured carefully and correctly ensure proper agricultural management this crop. It is one the most threatening diseases affecting onions since it caused by fungal organism called Sclerotium cepivourum. This imperative calls for research we introduce, a novel hybrid model combining ability Convolutional Neural Networks (CNN) with explained decision tree (DT). symbiotic integration tries enhance precision classifying intensity fine-tuned automated diagnosis. Our study based on custom database 3500 detailed pictures 6 grades rot. heterogeneous provided inputs our which was achieve an impressive overall accuracy 94.82%. performance model's robustness also using multitude measures such as precision, recall, F1 score. proves superior in comparison conventional approaches, evidenced both high increased visibility making decisions. discriminate essential stakeholders who want understand basis assigned severities. goes beyond limits academic institutions implications agriculture. automatically provides accurate estimates leading focused intervention, preventing yield loss, improving resource exploitation. aligns objectives pursuing sustainable knowledge-based

Language: Английский

Technological revolutions in smart farming: Current trends, challenges & future directions DOI
Vivek Sharma, Ashish Kumar Tripathi, Himanshu Mittal

et al.

Computers and Electronics in Agriculture, Journal Year: 2022, Volume and Issue: 201, P. 107217 - 107217

Published: Aug. 13, 2022

Language: Английский

Citations

156
Applications of deep learning in precision weed management: A review DOI Creative Commons
Nitin Rai, Yu Zhang, Billy G. Ram

et al.

Computers and Electronics in Agriculture, Journal Year: 2023, Volume and Issue: 206, P. 107698 - 107698

Published: Feb. 10, 2023

Deep Learning (DL) has been described as one of the key subfields Artificial Intelligence (AI) that is transforming weed detection for site-specific management (SSWM). In last demi-decade, DL techniques have integrated with ground well aerial-based technologies to identify weeds in still image context and real-time setting. After observing current research trend DL-based detection, are advancing by assisting precision weeding make smart decisions. Therefore, objective this paper was present a systematic review study involves available SSWM. To accomplish study, comprehensive literature survey performed consists 60 closest technical papers on detection. The findings summarized follows, a) transfer learning approach widely adopted technique address majority work, b) less focus navigated towards custom designed neural networks task, c) based pretrained models deployed test dataset, no specific model can be attributed achieved high accuracy multiple field images pertaining several studies, d) inferencing resource-constrained edge devices limited number dataset lagging, e) different versions YOLO (mostly v3) detecting scenario, f) SegNet U-Net semantic segmentation task multispectral aerial imagery, g) open-source acquired using drones, h) lack exploring optimization generalization identification images, i) ways design consume training hours, low-power consumption parameters during or inferencing, j) slow-moving advances optimizing domain adaptation approach. conclusion, will help researchers, experts, scientists, farmers, technology extension specialist gain updates area

Language: Английский

Citations

112
A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture DOI Creative Commons
Jalal Uddin Md Akbar, Syafiq Fauzi Kamarulzaman, Abu Jafar Md Muzahid

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 4485 - 4522

Published: Jan. 1, 2024

With the escalating global challenges of food security and resource sustainability, innovative solutions like deep learning computer vision are transforming agricultural practices by enabling data-driven decision-making. This paper provides a focused review recent advancements in learning-enabled techniques tailored specifically for greenhouse environments. First, fundamentals briefly introduced. Over 100 studies from 2020 to date then comprehensively reviewed which these technologies were applied within greenhouses growth monitoring, disease detection, yield estimation, other tasks. The techniques, datasets, models, overall performance results reported literature analyzed. Tables figures showcase real-world implementations synthesized current research. Key also outlined related aspects model adaptability, lack sufficient labeled data, computational constraints, need multi-modal sensor fusion, areas needing further investigation. Future trends prospects discussed provide guidance researchers exploring niche domain. By condensing prior work elucidating state-of-the-art, this timely aims promote continued progress smart agriculture. analysis, on environments, fills gap compared previous surveys. Overall, highlights immense potential driving emergence data-driven, farming worldwide.

Language: Английский

Citations

18
Estimation of the Extent of the Vulnerability of Agriculture to Climate Change Using Analytical and Deep-Learning Methods: A Case Study in Jammu, Kashmir, and Ladakh DOI Open Access
Irtiqa Malik, Muneeb Ahmed, Yonis Gulzar

et al.

Sustainability, Journal Year: 2023, Volume and Issue: 15(14), P. 11465 - 11465

Published: July 24, 2023

Climate stress poses a threat to the agricultural sector, which is vital for both economy and livelihoods in general. Quantifying its risk food security, livelihoods, sustainability crucial. This study proposes framework estimate impact climate on agriculture terms of three objectives: assessing regional vulnerability (exposure, sensitivity, adaptive capacity), analysing variability, measuring performance under climatic stress. The twenty-two sub-regions Jammu, Kashmir, Ladakh assessed using indicators determine collective susceptibility change. An index-based approach with min–max normalization employed, ranking districts based their relative performances across indicators. work assesses socio-economic growth benchmark Ricardian approach. parameters function are estimated linear combination exposure variables. Lastly, forecasted trends variables examined long short-term memory (LSTM)-based recurrent neural network, providing an annual variability. results indicate negative minimum temperature decreasing land holdings GDP, while cropping intensity, rural literacy, credit facilities have positive effects. Budgam, Ganderbal, Bandipora exhibit higher due factors such as low literacy rates, high population density, extensive rice cultivation. Conversely, Kargil, Rajouri, Poonch show lower density level institutional development. We observe increasing trend region. proposed LSTM synthesizes predictive five essential average overall root mean squared error (RMSE) 0.91, outperforming ARIMA exponential-smoothing models by 32–48%. These findings can guide policymakers stakeholders developing strategies mitigate enhance resilience.

Language: Английский

Citations

25
Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications DOI Creative Commons
Claudia Arellano, Joseph Govan

Agronomy, Journal Year: 2024, Volume and Issue: 14(2), P. 341 - 341

Published: Feb. 7, 2024

Nanotechnology, nanosensors in particular, has increasingly drawn researchers’ attention recent years since it been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change sustainability, have promoted pushed forward the use of agroindustry environmental applications. However, issues with noise confounding signals make these tools non-trivial technical challenge. Great advances artificial intelligence, more particularly machine learning, provided new that allowed researchers improve quality functionality nanosensor systems. This short review presents latest work analysis data from using learning agroenvironmental It consists an introduction topics application field nanosensors. The rest paper examples techniques utilisation electrochemical, luminescent, SERS colourimetric classes. final section discussion conclusion concerning relevance material discussed future sector.

Language: Английский

Citations

12
YOLO-CWD: A novel model for crop and weed detection based on improved YOLOv8 DOI
Chong Ma,

Ge Chi,

Xueping Ju

et al.

Crop Protection, Journal Year: 2025, Volume and Issue: unknown, P. 107169 - 107169

Published: Feb. 1, 2025

Language: Английский

Citations

1
Navigation path extraction for garden mobile robot based on road median point DOI Creative Commons
Wei Li

EURASIP Journal on Advances in Signal Processing, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Feb. 27, 2025

Language: Английский

Citations

1
A comprehensive review of segment drying (vesicle granulation and collapse) in citrus fruit: Current state and future directions DOI
Chunlian Huang,

Jiao Hou,

Meizhu Huang

et al.

Scientia Horticulturae, Journal Year: 2022, Volume and Issue: 309, P. 111683 - 111683

Published: Nov. 11, 2022

Language: Английский

Citations

31
Commentary on the review articles of spectroscopy technology combined with chemometrics in the last three years DOI

Xuesong Huo,

Pu Chen,

Jingyan Li

et al.

Applied Spectroscopy Reviews, Journal Year: 2023, Volume and Issue: 59(4), P. 423 - 482

Published: May 5, 2023

In recent years, spectral analysis methods have developed rapidly. A key feature is the use of chemometric to process data for performing qualitative and quantitative complex mixtures. The coupling spectroscopic techniques led distinct advantages in speed, cost, efficiency, automation, portability compared traditional agriculture, food, pharmaceutical, petroleum, chemical, environmental, medical fields. This paper comments on review papers published during past three years (2020–2022) topic combination methods. development status, existing challenges, future direction this field discussed.

Language: Английский

Citations

21
Advances in intelligent detection, monitoring, and control for preserving the quality of fresh fruits and vegetables in the supply chain DOI

Xiaolong Zhong,

Min Zhang,

Tiantian Tang

et al.

Food Bioscience, Journal Year: 2023, Volume and Issue: 56, P. 103350 - 103350

Published: Nov. 14, 2023

Language: Английский

Citations

21