Image-based detection of rice straw moisture content in feed production using an attention-enhanced ResNet50 and two-stage regression

A Modified EfficientNetB0-based Deep Learning Model for Accurate Detection and Classification of Groundnut Leaf Diseases

A deep learning-based solution to automate disease detection of groundnut leaf conditions that outperformed existing methods such as ResNet50, CNN with progressive resizing, LeafNet and LeafNet and maintained low training and validation loss throughout training.

WSD-YOLO: A lightweight YOLO-based model with enhanced feature representation for maize pest detection

As a major staple crop with global significance, maize is highly vulnerable to pest infestations throughout its growth cycle, which can substantially limit plant development and reduce yield. In practice, accurate detection remains challenging due to pronounced morphological variations across pest life stages, as well...

Accurate Estimation of Leaf Nitrogen Content in Broomcorn Millet Using Deep Learning, RGB Imagery, and Multi-Source Data Fusion

Nitrogen is a major determinant of crop growth, yield formation, and grain quality, making precise nitrogen (N) management essential for sustainable production. This study integrated field canopy spectra acquired with an ASD FieldSpec 4 spectrometer and unmanned aerial vehicle (UAV)-based RGB imagery to estimate leaf n...

A Multi-class Rice Disease Recognition Method based on Illumination-Robust and Attention-Augmented MobileNetV2

The rice disease identification method proposed in this paper provides a reference for the deployment and application of the method in different light conditions in the field and achieves test average accuracy of 83.9% and a maximum validation accuracy of 88.0% on the testing sets.

A Deep Transfer Learning Approach for Early Identification of Multiple Crop Diseases and Pest Infestations using Leaf and Field Images

Effective and early detection of plant diseases and pest infestations is vital for increasing agricultural efficiency to provide global food security. Nevertheless, traditional diagnosis methods based on manual visual inspections are inefficient, inconsistent, and inaccurate in practical scenarios. In this research, we...

Enhanced tomato leaf disease detection using multi-modal deep-handcrafted feature fusion with optimization-driven learning techniques