TL;DR
FruScab-E3Net, a Vision Transformer (ViT)-based method that employs a convolutional neural network (CNN), specifically a transfer learning model based on ResNet50, demonstrates better ability in capturing disease-relevant features for a reliable and rapid diagnosis of scab disease.
The most important thing in minimizing crop losses and maximizing crop yields is accurate and timely diagnosis of diseases in pomegranate fruit. Though the recent deep learning methods have demonstrated encouraging results, issues like field noise, variability in illumination, complexity of the background, and similari...
This work demonstrates ResNet50’s performance for bean leaf disease classification tasks, providing important insights for future research and practical applications in agricultural disease control.
A dataset-centric benchmark of deep learning methods for grape leaf disease classification and detection that emphasizes dataset provenance, realistic field evaluation, annotation compatibility, and external validation for reliable vineyard disease recognition is presented.
world’s important cereal it often falls victim to diseases like Fusarium Head Blight (FHB) and rust. These diseases can seriously reduce wheat yields and threaten food supplies. Traditionally, identifying these diseases relies on experts carefully examining the plants by hand can be quite subjective. As a result, early...
Global citrus production is significantly impacted by diseases, leading to economic losses and requiring accurate diagnostic tools. The system proposed here presents a deep learning system leveraging the EfficientNetB3 architecture for automated detection of Citrus Greening (HLB), Black Spot, Canker, and healthy instan...
A comparative analysis of five deep learning models, namely Basic CNN, VGG16, ResNet50, VGG32, and the proposed CFNET, which is based on EfficientNetB3, for binary pest detection in medicinal plants confirms CFNET's suitability for mobile and edge-based agricultural monitoring systems.
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