Intelligent technologies and their transformative role in modern agriculture: A comparative approach DOI Creative Commons
Karishma Behera, Anita Babbar,

R. G. Vyshnavi

et al.

Environment Conservation Journal, Journal Year: 2024, Volume and Issue: 25(3), P. 870 - 880

Published: April 22, 2024

The escalating global demand for food, propelled by a burgeoning population and the unpredictable shifts in climatic conditions, presents challenge that traditional plant breeding alone struggles to address. In response this pressing need, infusion of intelligent technologies emerges as pivotal solution, poised not only boost production but also meet demand. This transformative approach encompasses spectrum cutting-edge tools, including Remote Sensing GIS, Aeroponics, Drone Technology, Biotechnology, Artificial Intelligence, Machine Learning, and, ultimately, Robotics. synergistic integration these will enhance agricultural monitoring facilitating precise crop surveillance, early detection mitigation diseases pests, optimization water resources, accurate mapping land use types, comprehensive environmental monitoring, real-time weather climate tracking, efficient nutrient management, irrigation spraying practices, reliable yield prediction, advanced forecasting, genetic analysis, informed decision-making processes. amalgamation with modern methodologies signifies significant advancement towards achieving more sustainable practices. convergence addresses immediate need increased food sets stage resilient future-ready landscape. era integration, we witness harmonious coexistence tradition innovation, paving way abundant secure future.

Language: Английский

Automatic plant phenotyping analysis of Melon (Cucumis melo L.) germplasm resources using deep learning methods and computer vision DOI Creative Commons
Shan Xu, Jia Shen, Yuzhen Wei

et al.

Plant Methods, Journal Year: 2024, Volume and Issue: 20(1)

Published: Oct. 30, 2024

Language: Английский

Citations

1
The use of artificial intelligence in the production of genetically modified (GM) crops: a recent promising strategy for enhancing the acceptability of GM products ? DOI Creative Commons
Gideon Sadikiel Mmbando

Deleted Journal, Journal Year: 2024, Volume and Issue: 6(11)

Published: Nov. 1, 2024

The acceptance of genetically modified (GM) crops is still a controversial topic that presents major obstacles to their general use. Few studies, nevertheless, have emphasized the use artificial intelligence (AI) in forecasting dangers GM crops. This review delves into emerging field applying AI forecast hazards linked and examines how it could increase public products. algorithms, predictive modeling approaches examine enormous datasets include genetic, environmental, agronomic factors. Utilizing AI, researchers may accelerate risk assessment procedures on safety effectiveness In addressing concerns skepticism, AI-generated assessments foster transparency confidence among consumers, regulators, stakeholders, thereby might fostering greater Although lack available data genetic modifications or developing crop varieties, amount training validation needed for algorithms before they can be trusted, complexity models, ethical issues about like privacy algorithm bias, present difficulties precise AI-driven assessment. outlines recent developments future directions utilizing as promising strategy enhance acceptability

Language: Английский

Citations

1
High-throughput plant phenotyping analysis of Melon (Cucumis melo L.) germplasm resources using deep learning methods and computer vision DOI Creative Commons
Shan Xu, Jia Shen, Yuzhen Wei

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: May 9, 2024

Abstract Cucumis melo L., commonly known as melon, is a crucial horticultural crop. The selection and breeding of superior melon germplasm resources play pivotal role in enhancing its marketability. However, current methods for appearance phenotypic analysis rely primarily on expert judgment intricate manual measurements, which are not only inefficient but also costly. Therefore, to expedite the process we analyzed images 117 varieties from two annual years utilizing artificial intelligence (AI) technology. By integrating semantic segmentation model Dual Attention Network (DANet), object detection RTMDet, keypoint RTMPose, Mobile-Friendly Segment Anything Model (MobileSAM), deep learning algorithm framework was constructed, capable efficiently accurately segmenting fruit pedicel. On this basis, series feature extraction algorithms were designed, successfully obtaining 11 traits melon. Linear fitting verification results selected demonstrated high correlation between algorithm-predicted values manually measured true values, thereby validating feasibility accuracy algorithm. Moreover, cluster using all revealed consistency classification genotypes. Finally, user-friendly software developed achieve rapid automatic acquisition phenotypes, providing an efficient robust tool breeding, well facilitating in-depth research into genotypes phenotypes.

Language: Английский

Citations

0
Advancing plant biology through deep learning-powered natural language processing DOI
Shuang Peng, Loïc Rajjou

Plant Cell Reports, Journal Year: 2024, Volume and Issue: 43(8)

Published: Aug. 1, 2024

Language: Английский

Citations

0
Intelligent technologies and their transformative role in modern agriculture: A comparative approach DOI Creative Commons
Karishma Behera, Anita Babbar,

R. G. Vyshnavi

et al.

Environment Conservation Journal, Journal Year: 2024, Volume and Issue: 25(3), P. 870 - 880

Published: April 22, 2024

The escalating global demand for food, propelled by a burgeoning population and the unpredictable shifts in climatic conditions, presents challenge that traditional plant breeding alone struggles to address. In response this pressing need, infusion of intelligent technologies emerges as pivotal solution, poised not only boost production but also meet demand. This transformative approach encompasses spectrum cutting-edge tools, including Remote Sensing GIS, Aeroponics, Drone Technology, Biotechnology, Artificial Intelligence, Machine Learning, and, ultimately, Robotics. synergistic integration these will enhance agricultural monitoring facilitating precise crop surveillance, early detection mitigation diseases pests, optimization water resources, accurate mapping land use types, comprehensive environmental monitoring, real-time weather climate tracking, efficient nutrient management, irrigation spraying practices, reliable yield prediction, advanced forecasting, genetic analysis, informed decision-making processes. amalgamation with modern methodologies signifies significant advancement towards achieving more sustainable practices. convergence addresses immediate need increased food sets stage resilient future-ready landscape. era integration, we witness harmonious coexistence tradition innovation, paving way abundant secure future.

Language: Английский

Citations

0