A novel hybrid inception-xception convolutional neural network for efficient plant disease classification and detection.

ABSTRACT: Plants are essential at all stages of living things. Plant pests, diseases, and symptoms are most regularly visible in plant leaves and fruits and sometimes within the roots. Yet, their diagnosis by experts in the laboratory is expensive, tedious, and time-consuming if the samples involve laboratory analysis. Failure to detect early plant symptoms and diseases is the core biotic cause of increased plant stresses, structure, health, reduced subsistence farming, and threats to global food security. To mitigate these problems at a social, economic, and environmental level, inappropriate herbicide application reduction and early plant disease detection and classification (PDDC) are significant solutions in this case. Advancements in transfer learning techniques have resulted in effective resul

SUBMITTER: Shafik W 

PROVIDER: S-EPMC11785716 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature