TỔNG QUAN CÁC PHƯƠNG PHÁP KẾT HỢP HỌC SÂU VÀ KỸ THUẬT HÌNH ẢNH TRONG ĐÁNH GIÁ SINH TRƯỞNG THỰC VẬT=A REVIEW OF UTILIZING DEEP LEARNING AND IMAGING FOR PLANT GROWTH ASSESSMENT

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Tác giả: Đức Hà Chu, Quang Hưng Hà, Minh Triển Phạm, Minh Trung Vũ

Ngôn ngữ: vie

Ký hiệu phân loại:

Thông tin xuất bản: Tạp chí Khoa học & Công nghệ - Trường Đại học Công nghiệp Hà Nội, 2024

Mô tả vật lý: tr.69

Bộ sưu tập: Metadata

ID: 479303

Thêm vào giỏ

Nghiên cứu này đánh giá các ứng dụng của học sâu và chụp ảnh đa bước sóng trong việc giám sát sinh trưởng thực vật. Sự phát triển nhanh chóng của công nghệ chọn tạo giống khiến việc tích hợp các nền tảng phân tích kiểu hình thông lượng cao sử dụng ảnh là bước tiến quan trọng để nghiên cứu các đặc tính phức tạp của thực vật. Học sâu với khả năng phân loại hình ảnh hiệu quả vẫn gặp thách thức trong đánh giá sinh trưởng thực vật như yêu cầu dữ liệu được gán nhãn lớn và khả năng xử lý thông tin không gian - thời gian. Bài viết đề cập nhu cầu phát triển phần mềm và kỹ thuật mới để cải thiện khảnăng phân tích dữ liệu hướng tới kết quả phù hợp với mô hình sinh lý thực vật. Sự phát triển của học sâu và kỹ thuật hình ảnh hứa hẹn về việc cung cấp thông tin chi tiết hơn về kiểu hình thực vật, tăng tốc độ phân tích và cải thiện hiểu biết về sự phát triển thực vật trong môi trường đa dạng. Nghiên cứu này không chỉ tổng quan về hai lĩnh vực trên với những công bố cập nhật đến thời điểm hiện tại mà còn đưa ra nhận định về nhu cầu phát triển công nghệ và đề xuất các hướng nghiên cứu trong tương lai.This study evaluate advancements in the application of deep learning and multi-wavelength imaging techniques for monitoring and phenotyping plant growth. With the rapid development of plant breeding technology, effectively integrating high-throughput phenotyping platforms, utilizing conventional imaging to tomographic imaging, which represent a significant step forward in researching complex traits related to plant growth and adaptability. Although deep learning methods have demonstrated breakthrough capabilities of image classification in various fields, their application in plant growth monitoring presents challenges such as the need for extensive data annotation and the ability to process spatial and temporal information simultaneously. This article emphasizes the necessity of developing new softwares and techniques to improve data interpretability and achieves results that align with plant physiological models. Progress in both deep learning and image techniques areas promises to provide more detailed insights into plant phenotypes, accelerate analysis, and enhance our understanding of plant development in diverse environments. This research not only reviews a new state-of-the-art deep learning and image techniques but also provides comments on the need for technology development and proposes future research directions.

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