Image‐based crop disease detection using machine learning
Crop disease detection is important due to its significant impact on agricultural productivity
and global food security. Traditional disease detection methods often rely on labour …
and global food security. Traditional disease detection methods often rely on labour …
Modeling and Optimization with Artificial Intelligence in Nutrition
Featured Application Artificial intelligence offers supreme opportunities for advancement
and application in nutrition. Abstract The use of mathematical modeling and optimization in …
and application in nutrition. Abstract The use of mathematical modeling and optimization in …
Image‐based detection and classification of plant diseases using deep learning: State‐of‐the‐art review
M Bagga, S Goyal - Urban Agriculture & Regional Food …, 2024 - Wiley Online Library
Plant diseases are assumed to be one of the primary causes regulating food manufacturing
and reducing deficits in crop yield, and it is crucial that plant diseases have rapid spotting …
and reducing deficits in crop yield, and it is crucial that plant diseases have rapid spotting …
A deep learning approach for early detection of drought stress in maize using proximal scale digital images
Neural computing methods pose an edge over conventional methods for drought stress
identification because of their ease of implementation, accuracy, non-invasive approach …
identification because of their ease of implementation, accuracy, non-invasive approach …
[HTML][HTML] Semantic segmentation for plant leaf disease classification and damage detection: A deep learning approach
R Polly, EA Devi - Smart Agricultural Technology, 2024 - Elsevier
Agriculture sustains the livelihoods of a significant portion of India's rural population, yet
challenges persist in manual practices and disease management. To address these issues …
challenges persist in manual practices and disease management. To address these issues …
Streamlining plant disease diagnosis with convolutional neural networks and edge devices
In addressing labor-intensive process of manual plant disease detection, this article
introduces an innovative solution—the lightweight parallel depthwise separable …
introduces an innovative solution—the lightweight parallel depthwise separable …
A new large dataset and a transfer learning methodology for plant phenoty** in Vertical Farms
Vertical farming has emerged as a solution to enhance crop cultivation efficiency and
overcome limitations in conventional farming methods. Yet, abiotic stresses significantly …
overcome limitations in conventional farming methods. Yet, abiotic stresses significantly …
Enhanced hybrid attention deep learning for avocado ripeness classification on resource constrained devices
S Nuanmeesri - Scientific Reports, 2025 - nature.com
Attention mechanisms such as the Convolutional Block Attention Module (CBAM) can help
emphasize and refine the most relevant feature maps such as color, texture, spots, and …
emphasize and refine the most relevant feature maps such as color, texture, spots, and …
Comparative Study of Deep Learning LSTM and 1D-CNN Models for Real-time Flood Prediction in Red River of the North, USA
The Red River of the North has a history of flooding, dating back to the late 1800s. Flooding
in the Red River is caused by a combination of factors, including heavy snowfall, heavy …
in the Red River is caused by a combination of factors, including heavy snowfall, heavy …
[HTML][HTML] Coffee Rust Severity Analysis in Agroforestry Systems Using Deep Learning in Peruvian Tropical Ecosystems
Agroforestry systems can influence the occurrence and abundance of pests and diseases
because integrating crops with trees or other vegetation can create diverse microclimates …
because integrating crops with trees or other vegetation can create diverse microclimates …