AIMC Topic: Zea mays

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A novel agricultural commodity price prediction model integrating deep learning and enhanced swarm intelligence algorithm.

PloS one
The volatility of agricultural commodity prices significantly affects market stability and financial market dynamics, especially during periods of economic uncertainty and global shocks. Accurate price prediction, however, remains challenging due to ...

Lightweight dual-stage feature refinement for black gram leaf disease classification using ConViTSE.

Scientific reports
Black gram, also known as urad bean, is an economically crucial crop widely cultivated in India, particularly in the central and southern regions. However, black gram is highly prone to multiple leaf diseases, resulting in considerable crop losses an...

High-density Phenotype Data of Intermediate Phenotypes Associated with Stalk Lodging Resistance in Maize.

Scientific data
Stalk lodging causes global maize (Zea mays L.) yield losses exceeding $6 billion annually. The poorly resolved genetic architecture of stalk lodging resistance, a key determinant of the ability of a plant to remain upright, poses a major constraint ...

Hyperparameter optimization ResNet by improved Beluga Whale Optimization.

PloS one
The parameter values of neural networks will directly affect the performance of the network, so it is very important to choose the appropriate parameter tuning method to improve the performance of the neural network. In this paper, the improved belug...

Multi-objective optimization of electromagnetic vibration parameters for corn seed phenotype prediction based on deep learning.

Scientific reports
This study presents a novel framework for adaptive optimization of electromagnetic vibration parameters in corn seed treatment using multi-objective deep learning approaches. A hybrid CNN-LSTM network architecture was developed to process heterogeneo...

When crops fail, forests follow: Agricultural shocks and deforestation in Zambia.

Proceedings of the National Academy of Sciences of the United States of America
As climate change makes agricultural production shocks more frequent and severe, it is vital to understand their effect on farmer welfare, land use, and deforestation. Theoretically, a change in agricultural productivity could increase or decrease de...

Enhanced residual-attention deep neural network for disease classification in maize leaf images.

Scientific reports
Disease classification in maize plant is necessary for immediate treatment to enhance agricultural production and assure global food sustainability. Recent advancements in deep learning, specifically convolutional neural networks, have shown outstand...

A machine learning approach for estimating forage maize yield and quality in NW Spain.

PloS one
Crop models simulate crop growth and development according to different climatic, soil and crop management conditions. The CSM-CERES-Maize model (DSSAT) was adapted to simulate forage maize yields by calibrating the genetic parameters of six cultivar...

YOLO-LeafNet: a robust deep learning framework for multispecies plant disease detection with data augmentation.

Scientific reports
Plant diseases significantly harm crops, resulting in significant economic losses across the globe. In order to reduce the harm that these diseases produce, plant diseases must be diagnosed accurately and timely manner. In this work, a YOLO-LeafNet a...

Risk assessment of corn borer based on feature optimization and weighted spatial clustering: a case study in Shandong Province, China.

Scientific reports
As a typical pest affecting corn yield and safety, corn borer causes serious economic losses worldwide. Climate warming has intensified the occurrence of pest outbreaks in recent years, but the associated risk has not been precisely assessed or under...