Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 307 - 315
Published: Jan. 1, 2024
Language: Английский
Machines, Journal Year: 2023, Volume and Issue: 11(8), P. 774 - 774
Published: July 25, 2023
Agriculture 5.0 refers to the next phase of agricultural development, building upon previous digital revolution in agrarian sector and aiming transform industry be smarter, more effective, ecologically conscious. Farming processes have already started becoming efficient due development technologies, including big data, artificial intelligence (AI), robotics, Internet Things (IoT), virtual augmented reality. Farmers can make most resources at their disposal thanks this data-driven approach, allowing them effectively cultivate sustain crops on arable land. The European Union (EU) aims food systems fair, healthy, environmentally sustainable through Green Deal its farm-to-fork, soil, biodiversity strategies, zero pollution action plan, upcoming use pesticides regulation. Many historical synthetic are not currently registered EU market. In addition, continuous a limited number active ingredients with same mode scales up pests/pathogens/weed resistance potential. Increasing plant protection challenges as well having fewer chemical apply require innovation smart solutions for crop production. Biopesticides tend pose risks human health environment, efficacy depends various factors that cannot controlled traditional application strategies. This paper disclose contribution robotic ecosystems, highlighting both limitations technology. Specifically, work documents current threats agriculture (climate change, invasive pests, diseases, costs) how robotics AI act countermeasures deal such threats. Finally, specific case studies intelligent analyzed, architecture our decision system is proposed.
Language: Английский
Citations
93
Artificial Intelligence in Agriculture, Journal Year: 2024, Volume and Issue: 12, P. 72 - 84
Published: April 30, 2024
The issue of food security continues to be a prominent global concern, affecting significant number individuals who experience the adverse effects hunger and malnutrition. finding solution this intricate necessitates implementation novel paradigm-shifting methodologies in agriculture sector. In recent times, domain artificial intelligence (AI) has emerged as potent tool capable instigating profound influence on sectors. AI technologies provide advantages by optimizing crop cultivation practices, enabling use predictive modelling precision techniques, aiding efficient monitoring disease identification. Additionally, potential optimize supply chain operations, storage management, transportation systems, quality assurance processes. It also tackles problem loss waste through post-harvest reduction, analytics, smart inventory management. This study highlights that how utilizing power AI, we could transform way produce, distribute, manage food, ultimately creating more secure sustainable future for all.
Language: Английский
Citations
42
IT Professional, Journal Year: 2024, Volume and Issue: 26(1), P. 40 - 47
Published: Jan. 1, 2024
In the evolution from Agriculture 2.0 to 5.0, agricultural sector has witnessed transformative changes. 5.0 represents a revolutionary phase, integrating advanced technologies enhance efficiency and sustainability adapted individual field livestock needs. The integration of robotics, extended reality, 6G marks significant leap, enabling real-time monitoring automation in farming practices. Artificial intelligence big data are pivotal offering insights for decision making predictive analytics. Natural language processing plays crucial role facilitating efficient communication processing. transition should also consider societal challenges, terms technology lock-ins need behavior shifts among stakeholders faster adoption customization solutions. This article presents comprehensive description underscoring potential hurdles reshaping future farming.
Language: Английский
Citations
14
CyTA - Journal of Food, Journal Year: 2024, Volume and Issue: 22(1)
Published: Sept. 9, 2024
Language: Английский
Citations
12
Plant Pathology, Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 27, 2024
Abstract Crop disease detection is important due to its significant impact on agricultural productivity and global food security. Traditional methods often rely labour‐intensive field surveys manual inspection, which are time‐consuming prone human error. In recent years, the advent of imaging technologies coupled with machine learning (ML) algorithms has offered a promising solution this problem, enabling rapid accurate identification crop diseases. Previous studies have demonstrated potential image‐based techniques in detecting various diseases, showcasing their ability capture subtle visual cues indicative pathogen infection or physiological stress. However, rapidly evolving, advancements sensor technology, data analytics artificial intelligence (AI) continually expanding capabilities these systems. This review paper consolidates existing literature using ML, providing comprehensive overview cutting‐edge methodologies. Synthesizing findings from diverse offers insights into effectiveness different platforms, contextual integration applicability ML across types environmental conditions. The importance lies bridge gap between research practice, offering valuable guidance researchers practitioners.
Language: Английский
Citations
9
SN Computer Science, Journal Year: 2025, Volume and Issue: 6(1)
Published: Jan. 6, 2025
Language: Английский
Citations
1
Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)
Published: Feb. 6, 2025
Abstract A standardized representation and sharing of crop disease pest data is crucial for enhancing yields, especially in China, which features vast cultivation areas complex agricultural ecosystems. knowledge graph diseases pests, acting as a repository entities relationships, conceptually achieving unified management. However, there currently lack graphs specifically designed this field. In paper, we propose CropDP-KG, pests leverages natural language processing techniques to analyze from the Chinese image-text database. CropDP-KG covers relevant information on featuring 8 primary such diseases, symptoms, crops, organized into 7 relationships occurrence locations, affected parts suitable temperature. total, it includes 13,840 21,961 relationships. case studies presented research, also show versatile application CropDP, namely service system, have released its codebase under an open-source license. The content paper provides guide users build their own graphs, aiming help them effectively reuse extend they create.
Language: Английский
Citations
1
Food Chemistry, Journal Year: 2024, Volume and Issue: 449, P. 139171 - 139171
Published: April 6, 2024
Language: Английский
Citations
8
Crop Protection, Journal Year: 2024, Volume and Issue: 182, P. 106721 - 106721
Published: May 11, 2024
Accurate weed species identification is crucial for effective site-specific management (SSWM), enabling targeted and timely control measures each in crop field. This study advanced the current approach to species-level during early growth stage by integrating unmanned aerial vehicles (UAVs) imagery with standard convolutional neural networks (CNNs) models such as VGG16, Resnet152 Inception-Resnet-v2. For this, a robust dataset was created 33,467 labels of weeds (Atriplex patula, Chenopodium album, Convolvulus arvensis, Cyperus rotundus, Lolium rigidum, Portulaca oleracea, Salsola kali, Solanum nigrum) crops (maize, tomato), which subjected different training, validation test scenarios. Model inputs were adjusted order align them information represented UAV images. Initially, developed balanced scenarios, gradually increasing label numbers assess their performance. Inception-ResNet-v2 achieved over 90% accuracy 400 labels, while ResNet152 VGG16 required 600 800 respectively, similar accuracy. In more complex realistic scenarios unbalanced datasets, outperformed, likely due its deeper architecture enhanced capability capture intricate features patterns within The emphasized importance minority-to-majority ratio affects minority classification. To prevent misclassification, it determine right number CNN model training validation. Weed maps generated after classification using Faster R-CNN algorithm an object detector. advancement methodology facilitates precise efficient implementation SSWM techniques.
Language: Английский
Citations
6
Signals and communication technology, Journal Year: 2024, Volume and Issue: unknown, P. 131 - 166
Published: Jan. 1, 2024
Language: Английский
Citations
5