Integration of Deep Learning with Fox Optimization Algorithm for Early Detection and Classification of Tomato Leaf and Fruit Diseases

Engineering Technology & Applied Science Research, Journal Year: 2025, Volume and Issue: 15(1), P. 19343 - 19348

Published: Feb. 2, 2025

Tomato is a common vegetable crop extensively cultivated in the farming lands India. The hot climate of India perfect for its development, but particular weather conditions along with many other aspects affect growing tomato plants. Apart from these natural disasters and conditions, plant diseases consist major issue production. Precisely classifying leaf fruit plants vital step toward computerizing processes. Traditional disease detection models crops often fall short predictability. To address this, Machine Learning (ML) Deep (DL) have been developed, presenting advanced classification capabilities ability to manage vast variability agricultural data that conventional computer vision struggle with. This work presents an Integration DL Fox Optimization Algorithm (FOA) Recognition Classification Leaf Fruit Diseases (IDLFOA-DCTLFD). objective proposed IDLFOA-DCTLFD model enhance outcomes diseases. At initial stage, Median Filter (MF) used pre-processing Efficient Channel Attention-SqueezeNet (ECA-SqueezeNet) employed feature extraction. For hyperparameter tuning process, technique implements FOA. Finally, Wasserstein Generative Adversarial Network (WGAN) utilized method experimentally examined dataset. experimental validation methodology portrayed superior accuracy value 98.02%, surpassing existing techniques.

Language: Английский

Leveraging a Modified Contrastive Language-Image Pre-training Model to Align Images and Text for Generating Remedy Text for Malus Pumila Lamina Images

Engineering Technology & Applied Science Research, Journal Year: 2025, Volume and Issue: 15(2), P. 21989 - 21997

Published: April 3, 2025

The increasing threat of leaf diseases to the productivity precision farming necessitates systematic, logical, and scalable identification methodologies. Conventional plant disease detection approaches are often slow, inefficient, limited in their applicability, restricting effective management diseases. This research work recommends a hybrid multimodal model that uses different modes activities for can integrate image text data single frame improve accuracy proficiency classification. include custom-generated remedy descriptors specifically designed proposed model. latter combines Machine Learning (ML) techniques, such as OTSU thresholding, Gaussian filtering, modified Contrastive Language-Image Pre-training (mCLIP), classify diseased leaves propose suitable remedial actions. mCLIP label enhance effectiveness multi-class classification description generation. Unlike existing primarily output describing features, generates specific novel approach offers comprehensive solution renders optimistic results real-time automated agricultural practices, facilitating early intervention better crop management. obtained an 98.1%.

Language: Английский