Improving spatial transferability of deep learning models for small-field crop yield prediction

Pencarian berdasarkan :

SEMUA Pengarang Subjek ISBN/ISSN Pencarian Spesifik

Pencarian terakhir:

Image of Improving spatial transferability of deep learning models for small-field crop yield prediction

Text

Improving spatial transferability of deep learning models for small-field crop yield prediction

Stefan Stiller - Nama Orang; Kathrin Grahmann - Nama Orang; Gohar Ghazaryan - Nama Orang; Masahiro Ryo - Nama Orang;

Predicting crop yield using deep learning (DL) and remote sensing is a promising technique in agriculture. In smallholder agriculture (

Ketersediaan

Perpustakaan BIG (Eksternal Harddisk) 621.3678

Tersedia

Informasi Detail

Judul Seri: ISPRS Open Journal of Photogrammetry and Remote Sensing
No. Panggil: 621.3678
Penerbit: Amsterdam : Elsevier., 2024
Deskripsi Fisik: 11 hlm PDF, 7.586 KB
Bahasa: Inggris
ISBN/ISSN: 1872-8235
Klasifikasi: 621.3678
Tipe Isi: text
Tipe Media: -
Tipe Pembawa: -
Edisi: Vol.12, April 2024
Subjek: Convolutional neural network
UAV
Smallholder farming
Crop yield
Spatial cross validation
External validation
Info Detail Spesifik: -
Pernyataan Tanggungjawab: -

Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas

Improving spatial transferability of deep learning models for small-field crop yield prediction
Predicting crop yield using deep learning (DL) and remote sensing is a promising technique in agriculture. In smallholder agriculture (
Other Resource Link

Komentar

Anda harus masuk sebelum memberikan komentar