Past Detection CNN GB in Python Projects

Past Detection CNN GB in Python Projects

Abstract

Crop pests are a major threat to agricultural productivity, causing significant yield losses worldwide. The project Pest Detection using CNN and Gradient Boosting in Python Projects focuses on developing an intelligent system that detects and classifies crop pests using images of affected plants. Python is chosen as the development platform due to its powerful libraries for image processing, machine learning, and deep learning, including OpenCV, TensorFlow, Keras, Scikit-learn, and NumPy. The system uses Convolutional Neural Networks (CNN) to automatically extract features from pest images, followed by Gradient Boosting classifiers to improve detection accuracy. By providing rapid and accurate pest identification, the system helps farmers implement timely interventions, reduce crop damage, and improve agricultural productivity.

Existing System

Traditional pest detection methods primarily rely on manual inspection by farmers or agricultural experts, which is time-consuming, labor-intensive, and prone to errors. Some digital solutions use basic image recognition techniques or handcrafted feature extraction, but these approaches struggle with varying pest appearances, lighting conditions, and complex backgrounds. Existing machine learning systems often use only one type of model, which limits classification accuracy, especially when dealing with large-scale datasets or multiple pest species. Consequently, manual and semi-automated methods are inefficient for timely pest management in large farms.

Proposed System

The proposed system introduces a Python-based framework combining CNN and Gradient Boosting for pest detection. Images of crops and leaves are collected and preprocessed to remove noise, resize images, and normalize pixel values. CNN architectures are used to extract high-level features automatically from these images, capturing subtle patterns and textures indicative of specific pests. These extracted features are then input into a Gradient Boosting classifier to perform precise classification into multiple pest categories. The system is evaluated using metrics such as accuracy, precision, recall, F1-score, and confusion matrices. By integrating CNN feature extraction with Gradient Boosting classification, the system provides a scalable, efficient, and accurate solution for pest detection, enabling early intervention, reducing crop losses, and supporting sustainable agriculture.