Computer vision in smart agriculture and precision farming: Techniques and applications

Artificial Intelligence in Agriculture, Journal Year: 2024, Volume and Issue: 13, P. 64 - 83

Published: June 26, 2024

The transformation of age-old farming practices through the integration digitization and automation has sparked a revolution in agriculture that is driven by cutting-edge computer vision artificial intelligence (AI) technologies. This not only promises increased productivity economic growth, but also potential to address important global issues such as food security sustainability. survey paper aims provide holistic understanding vision-based intelligent systems various aspects precision agriculture. By providing detailed discussion on key areas digital life cycle crops, this contributes deeper complexities associated with implementation vision-guided challenging agricultural environments. focus explore widely used imaging image analysis techniques being utilized for tasks. first discusses salient crop metrics Then illustrates usage phases crops agriculture, acquisition, stitching photogrammetry, analysis, decision making, treatment, planning. After establishing thorough related terms involved concludes outlining challenges implementing generalized models real-time deployment fully autonomous farms.

Language: Английский

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Computer vision and Generative AI for yield prediction in Digital Agriculture

Published: April 2, 2024

For the cause of evolution agriculture to its next generation, introduction A.I. and data-driven approach is going be an important part agricultural industry that as per our vision would offer numerous economic, environmental social benefits. Controlled Environment Agriculture (CEA) providing more benefits since weather other conditions controlled predictable. In CEA, include enhanced productivity in yield, reduced footprints better resource management. Our solution uses adoption Generative AI visually forecast potential yield a crop using stable diffusion conditional prompts additionally implementing several Adversarial Networks (GAN) architectures for data augmentation super-resolution. proposed system also adopts generative real time monitoring plants. studying their respective autonomous cultivation harvesting patterns. The dataset used contained over 8479 examples different fruits alongside annotations. This was train detection models well categorically feed GANs generation. Data generated by DCGAN has structural similarity 0.5-0.7 whereas pix2pix network returns us 0.7-0.9.

Language: Английский

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Artificial Intelligence Applied to Precision Livestock Farming: a Tertiary Study

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100889 - 100889

Published: March 1, 2025

Language: Английский

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Transformasi Penyuluhan Pertanian Menuju Society 5.0: Analisis Peran Teknologi Informasi dan Komunikasi

Journal of Sustainable Agriculture Extension, Journal Year: 2024, Volume and Issue: 2(1), P. 37 - 47

Published: March 15, 2024

Latar belakang: Penyuluhan pertanian memegang peranan penting dalam mendorong menuju masa depan yang cerdas dan berkelanjutan sesuai dengan Society 5.0. Berperan sebagai penghubung antara penelitian implementasi di lapangan, salah satu tugas penyuluh adalah memperkenalkan inovasi teknologi kepada petani. Makalah ini bertujuan mendeskripsikan peran informasi komunikasi (TIK) transformasi penyuluhan 5.0, fokus pada pengembangan berkelanjutan. Metode: Metode review jurnal digunakan mengkaji tema utama Studi terhadap ilmiah diperoleh dari database Scopus, Web of Science, Google Scholar. Kriteria inklusi untuk pemilihan publikasi penerapan TIK pertanian, khususnya konsep Publikasi dipilih artikel terbit rentang waktu lima tahun terakhir memastikan relevansi aktualitas data. Hasil: Terdapat kesepahaman pandangan sama beberapa kajian literatur terpublikasi terkait era society memerlukan paradigma, kapasitas, kebutuhan tantangan zaman. 5.0 harus mengintegrasikan aspek teknologi, manusia, lingkungan menciptakan solusi berdampak positif bagi petani semua pelaku sektor pertanian. Transformasi respons strategis akan pendekatan lebih dinamis, inovatif, inklusif. Penyuluh Pertanian dituntut menggunakan berbagai metode penyuluhan, meningkatkan pemahaman tentang baru. tidak hanya sumber daya, tetapi juga katalis adopsi dapat mengubah menjadi efisien resiliensi. Kesimpulan: Dalam rangka mencapai visi konteks memiliki krusial memfasilitasi Investasi pelatihan hal sangat penting, mencapainya, dukungan pemerintah, akses terbaru, serta kerjasama institusi pendidikan, penelitian, industri sangatlah diperlukan. Dengan demikian, melalui upaya kolaboratif ini, diharapkan bahwa solusi-solusi relevan secara lokal dikembangkan mewujudkan adaptif

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Trends in Application of IoT and AI Technology to Agriculture

Shokubutsu Kankyo Kogaku, Journal Year: 2024, Volume and Issue: 36(2), P. 69 - 75

Published: Jan. 1, 2024

Language: Английский

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Surveillance 5.0: Next-Gen Security Powered by Quantum AI Optimization

Recent Research Reviews Journal, Journal Year: 2024, Volume and Issue: 3(1), P. 113 - 124

Published: June 1, 2024

Surveillance 5.0, powered by Quantum AI Optimization, represents the highpoint of next-generation security, transforming traditional surveillance paradigms through fusion quantum-powered technologies and advanced artificial intelligence. Optimization stands as essential, revolutionizing security operations for enabling real-time threat detection, proactive response approaches, adaptive risk mitigation measures. Moreover, privacy preservation ethical governance plays a major role in ensuring that activities maintain higher rights. From monitoring to emergency coordination, 5.0 empowers organizations across diverse sectors safeguard assets, protect individuals, enhance societal resilience. Lastly, prospective applications underscore limitless potential with emerging such Internet Things (IoT), intelligence (AI), Blockchain, edge computing driving continuous innovation expanding frontiers capabilities. In summary, quantum leap forward harnessing interactions leverage protection, privacy, an increasingly complex interconnected world.

Language: Английский

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Optimizing Engineered SynComs for Controlled Environment Agriculture (CEA): From Theory to Commercialization

International Journal of Horticulture, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

This study synthesizes research findings on the use of Digital Twin architectures, machine learning models, genetic engineering, and automated control systems to optimize SynComs for CEA.Key include effective reinforcement models improve crop management, importance breeding engineering in developing crops suited controlled environments, deployment advanced automation enhance precision environmental control.This also highlights significant improvements energy efficiency through technological advancements lighting climate control.The implications these researchers, policymakers, industry stakeholders are discussed, emphasizing need interdisciplinary collaboration continued fully realize potential CEA.This calls supportive policies, investment state-of-the-art technologies, collaborative efforts drive innovation sustainability environment agriculture.

Language: Английский

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Research on detection of wheat tillers in natural environment based on YOLOv8-MRF

Smart Agricultural Technology, Journal Year: 2024, Volume and Issue: unknown, P. 100720 - 100720

Published: Dec. 1, 2024

Language: Английский

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Exploration of Computer Vision Systems in the Recognition of Characteristics in Parts in an Industrial Environment

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 347 - 359

Published: Nov. 15, 2024

Language: Английский

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Use of YOLOv5 Trained Model for Robotic Courgette Harvesting and Efficiency Analysis

Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi, Journal Year: 2024, Volume and Issue: unknown, P. 669 - 689

Published: Oct. 22, 2024

The utilization of machine learning in vegetable harvesting not only enhances efficiency and precision but also addresses labor shortages improves overall agricultural productivity. In this study, a method was developed for courgette fruit. Courgette is fruit that can take long time to select harvest the area where it grown. YOLOv5 models (nano, small, medium, large) were used as deep method. All metric values analyzed. most successful model one trained with YOLOv5m algorithm using 20 batches 160 epochs 640x640 images. results scores analyzed "metrics/precision", "metrics/recall", "metrics/mAP_0.5" "metrics/mAP_0.5: 0.95". These metrics are key indicators measure recognition success reflect performance respective on validation dataset. data "YOLOv5 medium" proved be higher compared other models. measured = size: 640x640, batch: 20, epoch: 160, algorithm: YOLOv5m. It concluded "YOLOv5m" best robotic separate from branch.

Language: Английский

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