A Multi-Feature Deep Learning Framework for Early Disease Detection of Ginger Plants Using CNN and YOLOV3
Main Article Content
Abstract
Pest infestation and plant diseases significantly reduce agricultural productivity
and pose a serious threat to food security and farmers livelihoods. Conventional disease identification
techniques, which depend on expert manual examination, are often labor-intensive, time-consuming,
and prone to human mistake, particularly in large-scale cultivation. Therefore, early and precise plant
disease identification is essential for efficient crop management and better yields.This study suggests
an intelligent deep learning-based system for autonomous disease detection and classification using
leaf images. The main goal is to create a reliable and effective model that combines sophisticated
computer vision techniques for accurate diseases localization and identification with convolutional
neural networks (CNNs) for deep feature extraction. CNNs can learn discriminative and hierarchical
features from pixel-level inputs, which makes them ideal for visual pattern identification. The
proposed system incorporates the You Only Look Once (YOLO) technique to enhance real-time
detection performance. Because it better balances accuracy and detection speed, the YOLOv3 model
is specifically employed. The proposed Conv-YOLOv3 architecture enables simultaneous disease
localization and classification in the leaves of ginger plants. The primary disease indicators that the
system is designed to identify are soft rot disease, insect infestation patterns, and indications of
nutrient deficits.. The proposed model's performance is evaluated in terms of inference time,
computational complexity, detection efficiency, and classification accuracy. According to
experimental results, the Conv-YOLOv3 model has a 93.16% overall accuracy, demonstrating its
excellentcapacity for accurate disease identification in practical settings.The suggested system helps
farmers and agricultural specialists make timely decisions by providing a scalable, affordable, and
automated precision agriculture solution. This methodology supports more productive agriculture and
sustainable crop management by facilitating early disease identification and focused action.
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