[HTML][HTML] Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

H Tao, S Xu, Y Tian, Z Li, Y Ge, J Zhang, Y Wang… - Plant …, 2022 - cell.com
Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of
genomics and environment on plants, limiting the progress of smart breeding and precise …

[HTML][HTML] Crop water stress detection based on UAV remote sensing systems

H Dong, J Dong, S Sun, T Bai, D Zhao, Y Yin… - Agricultural Water …, 2024 - Elsevier
Agricultural water accounts for more than 70% of the total global water usage, and the
scarcity of global freshwater resources will largely limit global agricultural production …

[HTML][HTML] Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the …

SA Anees, K Mehmood, WR Khan, M Sajjad… - Ecological …, 2024 - Elsevier
Accurately estimating aboveground biomass (AGB) in forest ecosystems facilitates efficient
resource management, carbon accounting, and conservation efforts. This study examines …

Predicting canopy chlorophyll content in sugarcane crops using machine learning algorithms and spectral vegetation indices derived from UAV multispectral imagery

A Narmilan, F Gonzalez, ASA Salgadoe… - Remote Sensing, 2022 - mdpi.com
The use of satellite-based Remote Sensing (RS) is a well-developed field of research. RS
techniques have been successfully utilized to evaluate the chlorophyll content for the …

High-accuracy detection of maize leaf diseases CNN based on multi-pathway activation function module

Y Zhang, S Wa, Y Liu, X Zhou, P Sun, Q Ma - Remote sensing, 2021 - mdpi.com
Maize leaf disease detection is an essential project in the maize planting stage. This paper
proposes the convolutional neural network optimized by a Multi-Activation Function (MAF) …

Prediction of strawberry dry biomass from UAV multispectral imagery using multiple machine learning methods

C Zheng, A Abd-Elrahman, V Whitaker, C Dalid - Remote Sensing, 2022 - mdpi.com
Biomass is a key biophysical parameter for precision agriculture and plant breeding. Fast,
accurate and non-destructive monitoring of biomass enables various applications related to …

Phenotyping a diversity panel of quinoa using UAV-retrieved leaf area index, SPAD-based chlorophyll and a random forest approach

J Jiang, K Johansen, CS Stanschewski, G Wellman… - Precision …, 2022 - Springer
Given its high nutritional value and capacity to grow in harsh environments, quinoa has
significant potential to address a range of food security concerns. Monitoring the …

Maize yield prediction at an early developmental stage using multispectral images and genotype data for preliminary hybrid selection

MF Danilevicz, PE Bayer, F Boussaid, M Bennamoun… - Remote Sensing, 2021 - mdpi.com
Assessing crop production in the field often requires breeders to wait until the end of the
season to collect yield-related measurements, limiting the pace of the breeding cycle. Early …

Combining vegetation, color, and texture indices with hyperspectral parameters using machine-learning methods to estimate nitrogen concentration in rice stems and …

D Wang, R Li, T Liu, S Liu, C Sun, W Guo - Field Crops Research, 2023 - Elsevier
Context or problem Nitrogen is one of the important elements of crops, which plays a
decisive role in crop growth and development and the formation of yields. Monitoring of rice …

Comparison of machine-learning methods for urban land-use mapping in Hangzhou city, China

W Mao, D Lu, L Hou, X Liu, W Yue - Remote Sensing, 2020 - mdpi.com
Urban land-use information is important for urban land-resource planning and management.
However, current methods using traditional surveys cannot meet the demand for the rapid …