A Comprehensive Study on Machine Vision Techniques for an Automatic Weeding Strategy in Plantations DOI
J. Manikandan,

K. Rhikshitha,

G. S. Sathya Sudarsen

et al.

Published: Nov. 14, 2024

Agriculture is an essential occupation to the people of India. It considered as backbone most Indian population. However, one biggest concerns agriculture growth weeds. These weeds have be removed get a fruitful harvest. This process removing weeding, which must done with utmost care without affecting valuable crops. Using agricultural chemicals popular ways manage weed identification challenging parts cultivation, use throughout plantation harmful environment and ecosystem. In addition, manually possible but not entirely practical, considering human error labor charges that paid them. leads demand for alternatives control techniques. Therefore, industries continue seek human-free automated mechanisms are relatively inexpensive. this regard, machine vision comes into action automation. Machine technology uses cameras rather than naked eye identify. recent years, technologies rapidly developed, progress achieved remarkable. has been proven help build automation in resulting cost-effective, highly efficient, high-precision solutions. increased computational power hardware, decreased costs, advancements accuracy efficiency algorithms made it construct feasible practical automatic weeding strategies. chapter focuses on exploration numerous strategies involved their applications, cases, research challenges.

Language: Английский

Artificial Intelligence-based Rice Variety Classification: A State-of-the-Art Review and Future Directions DOI Creative Commons
Md. Masudul Islam, Galib Muhammad Shahriar Himel,

Md. Golam Moazzam

et al.

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100788 - 100788

Published: Jan. 1, 2025

Language: Английский

Citations

2
Utilizing Remote Sensing Data to Ascertain Weed Infestation Levels in Maize Fields DOI Creative Commons
Tetiana Fedoniuk, Petro Pyvovar, Pavlo Topolnytskyi

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(7), P. 711 - 711

Published: March 27, 2025

This study presents the evaluation of tools for weed analysis and management to support agroecological practices in organic farming, emphasizing agriculture digitalization, remote sensing. The main aim was provide techniques monitoring predicting spread using multispectral satellite drone data, without use chemical inputs. Key findings indicate that VV VH channels Sentinel-1 B2, B3, B4, B8 Sentinel-2 are not different regarding tillage, herbicide use, or sowing density. However, RE NIR detected significant variations proved effectiveness weediness monitoring. channel is sensitive agrotechnical factors such as cultivation type, making it valuable field Correlation regression analyses revealed Sentinel-2, most reliable levels. Conversely, showed limited predictive utility. Random effect models confirmed can accurately account site characteristics timing proliferation. Taken together, these effective systems, enabling rapid identification problem areas adjustments agronomic practices.

Language: Английский

Citations

1
High-throughput phenotyping techniques for forage: Status, bottleneck, and challenges DOI Creative Commons
Tao Cheng, Dongyan Zhang, Gan Zhang

et al.

Artificial Intelligence in Agriculture, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0
Resource constraint crop damage classification using depth channel shuffling DOI
Md. Tanvir Islam,

Safkat Shahrier Swapnil,

Md Mashum Billal

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 144, P. 110117 - 110117

Published: Jan. 29, 2025

Language: Английский

Citations

0
Real-time precision crop identification in high weed-density environments for robotic weed control using spectral fluorescence imaging in celery DOI Creative Commons
Rekha Raja,

Wen‐Hao Su,

David C. Slaughter

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 231, P. 110022 - 110022

Published: Jan. 31, 2025

Language: Английский

Citations

0
Machine Vision Systems for Post-Harvest Quality Assessment DOI
A Pavana Kumari, Jaswinder Singh, Sonu Langaya

et al.

Advances in environmental engineering and green technologies book series, Journal Year: 2025, Volume and Issue: unknown, P. 397 - 430

Published: Jan. 3, 2025

The postharvest quality assessment of agricultural commodities such as fruit, vegetables, and cereal are a global concern. complicated process ensuring food safety involves subjective perception which is full biasness labour intensive. These procedures very time-consuming. Nowadays enterprises researchers can benefit immensely from machine vision advancements in increasing the productivity products. Consequently, has widespread use all facets agriculture industry. key function system image processing. Deep learning models be used processing to efficiently determine kind caliber sector for classification different crops fruits, cereals.

Language: Английский

Citations

0
Systemic Uptake of Rhodamine Tracers Quantified by Fluorescence Imaging: Applications for Enhanced Crop–Weed Detection DOI Creative Commons
Yu Jiang,

Masoume Amirkhani,

Ethan Lewis

et al.

AgriEngineering, Journal Year: 2025, Volume and Issue: 7(3), P. 49 - 49

Published: Feb. 20, 2025

Systemic fluorescence tracers introduced into crop plants provide an active signal for crop–weed differentiation that can be exploited precision weed management. Rhodamine B (RB), a widely used tracer seeds and seedlings, possesses desirable properties; however, its application as seed treatment has been limited due to potential phytotoxic effects on seedling growth. Therefore, investigating mitigation strategies or alternative systemic is necessary fully leverage signaling differentiation. This study aimed identify address the phytotoxicity concerns associated with evaluate WT Sulforhodamine alternatives. A custom 2D imaging system, along analytical methods, was developed optimize quality facilitate quantitative characterization of intensity patterns in plant individual leaves, leaf disc samples. compounds were applied treatments in-furrow (soil application). mitigated by growing sand perlite media adsorption RB perlite. Additionally, methods tested their efficacy non-phytotoxic Experimental results demonstrated via pelleting direct most effective approaches. case conducted assess at distance 2.5 cm (1 inch). Results indicated from both clearly detected tissues ~10× higher than neighboring tissues. These findings suggest ap-plied effectively differentiates seedlings weeds reduced phytotoxicity, while offers viable, alternative. In conclusion, combination system presents promising technology WT, when treatment, provides satisfactory alternative, further expanding options fluorescence-based

Language: Английский

Citations

0
Cropland classification and water stress vulnerability assessment in arid environment of Churu district, India using machine learning approach DOI
Zubairul Islam,

Azizur Rehman Siddiqui,

Sudhir Kumar Singh

et al.

Journal of Atmospheric and Solar-Terrestrial Physics, Journal Year: 2025, Volume and Issue: unknown, P. 106483 - 106483

Published: Feb. 1, 2025

Language: Английский

Citations

0
Enhancing Arable Weed Diversity by Reduced Herbicide Use? DOI Creative Commons
Christoph von Redwitz, Sabine Andert, Johanna Bensch

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: 77(2)

Published: March 12, 2025

Abstract Based on a workshop held at the German Weed Science Conference in February 2024, this paper explores strategies for reducing herbicide use arable cropping systems to enhance weed diversity. Although potentially detrimental crop yields, weeds play vital role supporting ecosystem functions such as pollination, nutrient cycling, and microbial The reduction of is regarded an important management strategy preserving biodiversity, which has been declining Europe. Three are discussed: site-specific application, species-specific dose rates, selective herbicides with narrow target spectra. Each evaluated its technical feasibility, agronomic risks, potential benefits While challenges high investment costs, limitations, need precise distribution data remain, emerging technologies like AI-driven detection autonomous robots offer promising solutions. emphasizes importance combining reduced other practices, rotation mechanical weeding, achieve sustainable ecologically beneficial control. A shift farmers’ perspectives “clean fields” more comprehensive guidance ecological value essential widespread adoption these strategies.

Language: Английский

Citations

0
FGBNet: A Bio-Subspecies Classification Network with Multi-Level Feature Interaction DOI Creative Commons
Yang Yuan, Danping Huang,

Bingbin Cai

et al.

Diversity, Journal Year: 2025, Volume and Issue: 17(4), P. 237 - 237

Published: March 27, 2025

Biodiversity is a foundation for maintaining ecosystem health and stability, while precise species identification crucial to monitoring protecting ecosystems. Subspecies of organisms, as carriers genetic diversity, play key roles in stability adaptive evolution. Accurate subspecies helps deepen our understanding distribution, ecological relationships, change trends, providing scientific basis effective protection strategies. Therefore, this study proposes FineGrained-BioNet (FGBNet), deep learning network model specifically constructed fine-grained bio-subspecies image classification. The combines detail information supplement module, multi-level feature interaction, coordinate attention (CA) mechanism improve the accuracy efficiency Through experimentation optimization, ConvNeXt selected backbone FGBNet extraction, effectiveness interaction method verified. Additionally, optimal placement CA within also explored. experimental results show that, compared with ConvNeXt-Tiny, achieved an increase 6.204% by increasing parameter quantity only 5.702%, reaching 90.748%. This indicates that significantly improves classification computational efficiency. proposed facilitates more accurate classification, promoting development biodiversity strong technical support conservation.

Language: Английский

Citations

0