Integrating genomics & AI for precision crop monitoring and adaptive stress management

• Autorzy: Polegopu Rajesh , Vanapalli Satya Sumanth , Vanapalli Sash Vardhan , Diyya Naga Prudvi , Vandila Mounika , Valluri Divya , Peddinti Anjali , Saladi Sowjanya , Pyla Meghana , Gelli Padmini

Identyfikatory

DOI

10.35784/iapgos.7484

Warianty tytułu

PL

Integracja genomiki i sztucznej inteligencji w celu precyzyjnego monitorowania upraw i adaptacyjnego zarządzania stresem

• Języki publikacji: EN

Abstrakty

EN

Advancements in genomics and artificial intelligence are transforming precision agriculture by enabling early stress detection and adaptive crop management. Integrating genomic analysis, image-based stress detection, and real-time environmental monitoring, this approach assesses plant responses to stress factors such as drought and disease. A BERT-based model processes genomic data, while computer vision identifies visual stress indicators like wilting and discoloration. IoT sensors track environmental parameters such as soil moisture, temperature, andhumidity, refining predictionsand optimizing intervention strategies.The system leverages multimodal data fusion to enhance decision-making, improving the accuracy of stress detection and mitigation strategies. Machine learning models continuously adaptby learning from historical and real-time data, making recommendations more precise over time. A web-based platform allows users to upload plant images and environmental data for real-time analysis, generating personalized recommendations for irrigation, fertilization, and disease management. The platform's intuitive interface ensures accessibility for farmers and agricultural experts, facilitating widespread adoption.By combining AI, genomics, and IoT, this system enhances crop health, maximizes yield, and promotes sustainable farming through proactive, data-driven decision-making. Ultimately, it aims to reduce resource waste, mitigate crop losses, and support scalable, technology-driven agricultural solutions.

PL

Postępy w genomice i sztucznej inteligencji przekształcają rolnictwo precyzyjne, umożliwiając wczesne wykrywanie stresu i adaptacyjne zarządzanie uprawami. Integrując analizę genomiczną, wykrywanie stresu na podstawie obrazu i monitorowanie środowiska w czasie rzeczywistym, podejście to ocenia reakcje roślin na czynniki stresowe, takie jak susza i choroby. Model oparty na BERT przetwarza dane genomowe, podczas gdy wizja komputerowa identyfikuje wizualne wskaźniki stresu, takie jak więdnięcie i przebarwienia. Czujniki IoT śledzą parametry środowiskowe, takie jak wilgotność gleby, temperatura i wilgotność, udoskonalając prognozy i optymalizując strategie interwencji. System wykorzystujemultimodalną fuzję danych w celu usprawnienia procesu decyzyjnego, poprawiając dokładność wykrywania stresu i strategii jego łagodzenia. Modele uczenia maszynowego stale dostosowują się, ucząc się na podstawie danych historycznych i danych w czasie rzeczywistym, dzięki czemu zalecenia są z czasem bardziej precyzyjne. Platforma internetowa umożliwia użytkownikom przesyłanie zdjęć roślin i danych środowiskowych do analizy w czasie rzeczywistym, generując spersonalizowane zalecenia dotyczące nawadniania, nawożenia i zarządzania chorobami. Intuicyjny interfejs platformy zapewnia dostępność dla rolników i ekspertów w dziedzinie rolnictwa, ułatwiając powszechne przyjęcie. Łącząc sztuczną inteligencję, genomikę i IoT, system tenpoprawia zdrowie upraw, maksymalizuje plony i promuje zrównoważone rolnictwo poprzez proaktywne podejmowanie decyzji w oparciu o dane. Ostatecznie ma on na celu zmniejszenie marnotrawstwa zasobów, złagodzenie strat w uprawach i wspieranie skalowalnych, opartych na technologii rozwiązańrolniczych.

Słowa kluczowe

EN

precision farming; genomic sequencing; machine learning; crop management

PL

rolnictwo precyzyjne; sekwencjonowanie genomów; uczenie maszynowe; zarządzanie uprawami

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
• Rocznik: 2025
• Tom: T. 15, nr 2
• Strony: 18--26
• Opis fizyczny: Bibliogr. 25 poz., rys., tab., wykr.

Twórcy

autor

Polegopu Rajesh

• Siddhartha Academy of Higher Education, Department of Electronics and Instrumentation Engineering, Vijayawada, India

• https://orcid.org/0000-0003-3440-9296+

autor

Vanapalli Satya Sumanth

• Siddhartha Academy of Higher Education, Department of Computer Science and Engineering,Vijayawada, India

• https://orcid.org/0009-0006-5993-3456+

autor

Vanapalli Sash Vardhan

• Siddhartha Academy of Higher Education, Department of Computer Science and Engineering, Vijayawada, India

• https://orcid.org/0009-0004-1448-6467+

autor

Diyya Naga Prudvi

• Gayatri Vidya Parishad College of Engineering, Department of Computer Science and Engineering, Visakhapatnam, India

• https://orcid.org/0009-0001-1928-5590+

autor

Vandila Mounika

• Vignan's Institute of Engineering for Women, Department of Electronics and Communication Engineering, Visakhapatnam, India

• https://orcid.org/0009-0003-3770-2620+

autor

Valluri Divya

• Vignan's Institute of Engineering for Women, Department of Electronics and Communication Engineering, Visakhapatnam, India

• https://orcid.org/0009-0001-0600-1149+

autor

Peddinti Anjali

• Vignan's Institute of Engineering for Women, Department of Electronics and Communication Engineering, Visakhapatnam, India

• https://orcid.org/0009-0002-7857-4429+

autor

Saladi Sowjanya

• Vignan's Institute of Engineering for Women, Department of Electronics and Communication Engineering, Visakhapatnam, India

• https://orcid.org/0009-0008-6988-1615+

autor

Pyla Meghana

• Vignan's Institute of Engineering for Women, Department of Electronics and Communication Engineering, Visakhapatnam, India

• https://orcid.org/0009-0006-0028-6733+

autor

Gelli Padmini

• Maharaj Vijayaram Gajapathi Raj College of Engineering, Department of Electronicsand Communication Engineering, Vizianagaram, India

• https://orcid.org/0009-0006-0028-6733+

Bibliografia

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