Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming
The digitalization of data has resulted in a data tsunami in practically every industry of data-
driven enterprise. Furthermore, man-to-machine (M2M) digital data handling has …
driven enterprise. Furthermore, man-to-machine (M2M) digital data handling has …
Machine learning bridges omics sciences and plant breeding
J Yan, X Wang - Trends in Plant Science, 2023 - cell.com
Some of the biological knowledge obtained from fundamental research will be implemented
in applied plant breeding. To bridge basic research and breeding practice, machine learning …
in applied plant breeding. To bridge basic research and breeding practice, machine learning …
Remote sensing in field crop monitoring: A comprehensive review of sensor systems, data analyses and recent advances
The key elements that underpin food security require the adaptation of agricultural systems
to support productivity increases while minimizing inputs and the adverse effects of climate …
to support productivity increases while minimizing inputs and the adverse effects of climate …
Machine learning for smart agriculture and precision farming: towards making the fields talk
In almost every sector, data-driven business, the digitization of the data has generated a
data tsunami. In addition, man-to-machine digital data handling has magnified the …
data tsunami. In addition, man-to-machine digital data handling has magnified the …
Cyber-agricultural systems for crop breeding and sustainable production
The cyber-agricultural system (CAS) represents an overarching framework of agriculture that
leverages recent advances in ubiquitous sensing, artificial intelligence, smart actuators, and …
leverages recent advances in ubiquitous sensing, artificial intelligence, smart actuators, and …
Fruit classification using attention-based MobileNetV2 for industrial applications
Recent deep learning methods for fruits classification resulted in promising performance.
However, these methods are with heavy-weight architectures in nature, and hence require a …
However, these methods are with heavy-weight architectures in nature, and hence require a …
[HTML][HTML] Interpretability of deep learning models for crop yield forecasting
Abstract Machine learning models for crop yield forecasting often rely on expert-designed
features or predictors. The effectiveness and interpretability of these handcrafted features …
features or predictors. The effectiveness and interpretability of these handcrafted features …
Mapping smart farming: Addressing agricultural challenges in data-driven era
Abstract Internet of Things (IoT) technology plays an important role in advancing the
transformation of labor-intensive traditional agriculture into data-driven smart farming by …
transformation of labor-intensive traditional agriculture into data-driven smart farming by …
Winter wheat yield prediction using convolutional neural networks from environmental and phenological data
Crop yield forecasting depends on many interactive factors, including crop genotype,
weather, soil, and management practices. This study analyzes the performance of machine …
weather, soil, and management practices. This study analyzes the performance of machine …
Multispectral crop yield prediction using 3D-convolutional neural networks and attention convolutional LSTM approaches
SMM Nejad, D Abbasi-Moghadam… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
In recent years, national economies are highly affected by crop yield predictions. By early
prediction, the market price can be predicted, importing, and exporting plan can be provided …
prediction, the market price can be predicted, importing, and exporting plan can be provided …