Review on convolutional neural network (CNN) applied to plant leaf disease classification
J Lu, L Tan, H Jiang - Agriculture, 2021 - mdpi.com
Crop production can be greatly reduced due to various diseases, which seriously endangers
food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional …
food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional …
[HTML][HTML] Deep learning based computer vision approaches for smart agricultural applications
VG Dhanya, A Subeesh, NL Kushwaha… - Artificial Intelligence in …, 2022 - Elsevier
The agriculture industry is undergoing a rapid digital transformation and is growing powerful
by the pillars of cutting-edge approaches like artificial intelligence and allied technologies …
by the pillars of cutting-edge approaches like artificial intelligence and allied technologies …
Towards paddy rice smart farming: a review on big data, machine learning, and rice production tasks
Big Data (BD), Machine Learning (ML) and Internet of Things (IoT) are expected to have a
large impact on Smart Farming and involve the whole supply chain, particularly for rice …
large impact on Smart Farming and involve the whole supply chain, particularly for rice …
Pre-trained deep neural network-based features selection supported machine learning for rice leaf disease classification
Rice is a staple food for roughly half of the world's population. Some farmers prefer rice
cultivation to other crops because rice can thrive in a wide range of environments. Several …
cultivation to other crops because rice can thrive in a wide range of environments. Several …
Comparative performance of four CNN-based deep learning variants in detecting Hispa pest, two fungal diseases, and NPK deficiency symptoms of rice (Oryza sativa)
Crop production can be significantly increased if stresses are detected at the earliest
possible time to facilitate the implementation of necessary mitigation measures. This present …
possible time to facilitate the implementation of necessary mitigation measures. This present …
Explainable deep learning model for automatic mulberry leaf disease classification
Mulberry leaves feed Bombyx mori silkworms to generate silk thread. Diseases that affect
mulberry leaves have reduced crop and silk yields in sericulture, which produces 90% of the …
mulberry leaves have reduced crop and silk yields in sericulture, which produces 90% of the …
Agri-food 4.0 and innovations: Revamping the supply chain operations
The agri-food sector contributes significantly to economic and social advancements globally
despite numerous challenges such as food safety and security, demand and supply gaps …
despite numerous challenges such as food safety and security, demand and supply gaps …
[PDF][PDF] Deep transfer learning based rice plant disease detection model.
RP Narmadha, N Sengottaiyan… - Intelligent Automation & …, 2022 - cdn.techscience.cn
In agriculture, plant diseases are mainly accountable for reduction in productivity and leads
to huge economic loss. Rice is the essential food crop in Asian countries and it gets easily …
to huge economic loss. Rice is the essential food crop in Asian countries and it gets easily …
Deep learning approaches and interventions for futuristic engineering in agriculture
With shrinking natural resources and the climate challenges, it is foreseen that there will be
an imminent stress in agricultural outputs. Deep learning provides immense possibilities in …
an imminent stress in agricultural outputs. Deep learning provides immense possibilities in …
Detection of impurities in wheat using terahertz spectral imaging and convolutional neural networks
Y Shen, Y Yin, B Li, C Zhao, G Li - Computers and Electronics in Agriculture, 2021 - Elsevier
The aim of this work was to propose a method to rapidly and effectively detect impurities
contained in wheat based on a combination of terahertz spectral imaging and a …
contained in wheat based on a combination of terahertz spectral imaging and a …