Diagnosis Anthracnose of Chili Pepper Using Convolutional Neural Networks Based Deep Learning Models.

ABSTRACT: Chili pepper (Capsicum annuum L.), one of the most economically important vegetable crops globally, faces significant economic risks from anthracnose, leading to yield losses of 10% as well as decreasing marketability. Early and accurate detection is essential for mitigating these effects. Recent advancements in deep learning, particularly in image recognition, offer promising solutions for plant disease detection. This study applies deep learning models-MobileNet, ResNet50v2, and Xception-using transfer learning to diagnose anthracnose in chili peppers. A key challenge is the need for large, labeled datasets, which are costly to obtain. The study aims to identify the minimum dataset size required for accurate and efficient disease diagnosis using limited data. Performance metrics, includi

SUBMITTER: Kim HI 

PROVIDER: S-EPMC11834499 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature