A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction

M Rashid, BS Bari, Y Yusup, MA Kamaruddin… - IEEE …, 2021 - ieeexplore.ieee.org
An early and reliable estimation of crop yield is essential in quantitative and financial
evaluation at the field level for determining strategic plans in agricultural commodities for …

Oil palm and machine learning: Reviewing one decade of ideas, innovations, applications, and gaps

N Khan, MA Kamaruddin, UU Sheikh, Y Yusup… - Agriculture, 2021 - mdpi.com
Machine learning (ML) offers new technologies in the precision agriculture domain with its
intelligent algorithms and strong computation. Oil palm is one of the rich crops that is also …

Early detection of plant viral disease using hyperspectral imaging and deep learning

C Nguyen, V Sagan, M Maimaitiyiming… - Sensors, 2021 - mdpi.com
Early detection of grapevine viral diseases is critical for early interventions in order to
prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing …

Synthetic Minority Over-sampling TEchnique (SMOTE) and Logistic Model Tree (LMT)-Adaptive Boosting algorithms for classifying imbalanced datasets of nutrient and …

AD Amirruddin, FM Muharam, MH Ismail… - … and Electronics in …, 2022 - Elsevier
The conventional method to quantify leaf biochemical properties (nutrients and chlorophylls)
is tedious, labour-intensive, and impractical for vast oil palm plantation areas. Spectral …

Estimating leaf chlorophyll content of crops via optimal unmanned aerial vehicle hyperspectral data at multi-scales

W Zhu, Z Sun, T Yang, J Li, J Peng, K Zhu, S Li… - … and Electronics in …, 2020 - Elsevier
Leaf chlorophyll content (LCC) is a crucial indicator of nutrition in crop plants and can be
applied to assess the adequacy of nitrogen (N) fertilizer for crops while reducing N losses to …

The impact of new urbanization and industrial structural changes on regional water stress based on water footprints

Y Wang, Y Zhang, W Sun, L Zhu - Sustainable Cities and Society, 2022 - Elsevier
New urbanization and industrial structural changes have created new pressures on regional
water resources. This study takes 31 Chinese provinces as the research objects and …

Evaluation of different machine learning frameworks to predict CNL-FDC-PEF logs via hyperparameters optimization and feature selection

A Rostamian, E Heidaryan, M Ostadhassan - Journal of Petroleum Science …, 2022 - Elsevier
Although being expensive and time-consuming, petroleum industry still is highly reliant on
well logging for data acquisition. However, with advancements in data science and AI …

Combining random forest and XGBoost methods in detecting early and mid-term winter wheat stripe rust using canopy level hyperspectral measurements

L Huang, Y Liu, W Huang, Y Dong, H Ma, K Wu, A Guo - Agriculture, 2022 - mdpi.com
Appropriate modeling methods and feature selection algorithms must be selected to improve
the accuracy of early and mid-term remote sensing detection of wheat stripe rust. In the …

[HTML][HTML] Expert systems in oil palm precision agriculture: A decade systematic review

XJ Tan, WL Cheor, KS Yeo, WZ Leow - Journal of King Saud University …, 2022 - Elsevier
Abstract Oil palm (Elaeis guineensis Jacq.) is of the most profitable and widespread
commercial high tree crops in the tropical world, typically in Southeastern Asia. The present …

Hyperspectral estimation of chlorophyll content in apple tree leaf based on feature band selection and the CatBoost model

Y Zhang, Q Chang, Y Chen, Y Liu, D Jiang, Z Zhang - Agronomy, 2023 - mdpi.com
Leaf chlorophyll content (LCC) is a crucial indicator of nutrition in apple trees and can be
applied to assess their growth status. Hyperspectral data can provide an important means …