A review of deep learning in multiscale agricultural sensing
Population growth, climate change, and the worldwide COVID-19 pandemic are imposing
increasing pressure on global agricultural production. The challenge of increasing crop yield …
increasing pressure on global agricultural production. The challenge of increasing crop yield …
Advancing precision agriculture: The potential of deep learning for cereal plant head detection
Cereal plant heads must be identified precisely and effectively in a range of agricultural
applications, including yield estimation, disease detection, and breeding. Traditional …
applications, including yield estimation, disease detection, and breeding. Traditional …
Small unopened cotton boll counting by detection with MRF-YOLO in the wild
Q Liu, Y Zhang, G Yang - Computers and electronics in agriculture, 2023 - Elsevier
Accurate detection and counting of unopened cotton bolls at the early stage of cotton
maturation is an effective way to develop crop load management and harvesting strategies …
maturation is an effective way to develop crop load management and harvesting strategies …
Deep learning in plant phenological research: A systematic literature review
Climate change represents one of the most critical threats to biodiversity with far-reaching
consequences for species interactions, the functioning of ecosystems, or the assembly of …
consequences for species interactions, the functioning of ecosystems, or the assembly of …
Advanced technology in agriculture industry by implementing image annotation technique and deep learning approach: A review
The implementation of intelligent technology in agriculture is seriously investigated as a way
to increase agriculture production while reducing the amount of human labor. In agriculture …
to increase agriculture production while reducing the amount of human labor. In agriculture …
Daily monitoring of Effective Green Area Index and Vegetation Chlorophyll Content from continuous acquisitions of a multi-band spectrometer over winter wheat
Green area index (GAI), leaf chlorophyll content (LCC) and canopy chlorophyll content
(CCC) are key variables that are closely related to crop growth. Concurrent and continuous …
(CCC) are key variables that are closely related to crop growth. Concurrent and continuous …
CNN–SVM hybrid model for varietal classification of wheat based on bulk samples
Determining the variety of wheat is important to know the physical and chemical properties
which may be useful in grain processing. It also affects the price of wheat in the food …
which may be useful in grain processing. It also affects the price of wheat in the food …
DeepFlower: a deep learning-based approach to characterize flowering patterns of cotton plants in the field
Background Flowering is one of the most important processes for flowering plants such as
cotton, reflecting the transition from vegetative to reproductive growth and is of central …
cotton, reflecting the transition from vegetative to reproductive growth and is of central …
[HTML][HTML] SegVeg: Segmenting RGB images into green and senescent vegetation by combining deep and shallow methods
Pixel segmentation of high-resolution RGB images into chlorophyll-active or nonactive
vegetation classes is a first step often required before estimating key traits of interest. We …
vegetation classes is a first step often required before estimating key traits of interest. We …
[HTML][HTML] Development of image-based wheat spike counter through a Faster R-CNN algorithm and application for genetic studies
L Li, MA Hassan, S Yang, F Jing, M Yang, A Rasheed… - The Crop Journal, 2022 - Elsevier
Spike number (SN) per unit area is one of the major determinants of grain yield in wheat.
Development of high-throughput techniques to count SN from large populations enables …
Development of high-throughput techniques to count SN from large populations enables …