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This project utilizes the YOLOv7 architecture to develop an accurate and efficient leaf object detection model for agricultural applications. By leveraging a curated dataset of annotated images, the model can identify and classify leaves, aiding in crop management, disease detection, and weed control. The project focuses on robustness, efficiency, and interpretability, aiming to empower farmers and researchers with a reliable tool for informed decision-making. Through optimization and evaluation, the model demonstrates its potential to enhance crop productivity and sustainability.

Detecting and classifying objects within images is a fundamental challenge in the field of computer vision, and it finds numerous practical applications across various domains. In particular, accurate and efficient detection of specific objects, such as leaves, is crucial in agricultural settings for tasks like crop management, disease identification, and weed control.

In this project, we present a leaf object detection model based on the state-of-the-art You Only Look Once version 7 (YOLOv7) architecture. YOLOv7 is a deep learning framework that achieves real-time object detection with impressive accuracy. By harnessing the power of convolutional neural networks and advanced computer vision techniques, our model aims to provide an effective and scalable solution for leaf detection in complex agricultural environments. To train and evaluate our YOLOv7 leaf object detection model, we leverage a comprehensive dataset specifically curated for this purpose. The dataset contains diverse images of crops and weeds, annotated with bounding boxes that precisely delineate leaf objects. This rich dataset empowers our model to learn intricate patterns and generalizable representations, enabling it to accurately detect and classify leaves under varying conditions.

Throughout the development of our leaf object detection model, we adhere to the principles of robustness, efficiency, and interpretability. We optimize our model's architecture and hyperparameters to strike a balance between accuracy and computational efficiency, ensuring real-time performance in practical scenarios. Additionally, we focus on interpretable outputs, providing insights into the detected leaf objects and their corresponding bounding boxes.

Our ultimate goal is to empower farmers, researchers, and agricultural professionals with a reliable tool for leaf object detection that can facilitate informed decision-making and enhance crop management practices. By accurately identifying and analyzing leaves, we enable precise monitoring, early detection of diseases, and targeted interventions, ultimately leading to improved yields and sustainable agricultural practices.

• YOLOv7
• Leaf object detection
• Computer vision
• Agricultural applications
• Deep learning

The dataset used in this project is the Crop and Weed Detection Data with Bounding Boxes from Kaggle, which can be accessed here.

• leaf_detection_YOLOv7x.ipynb: This Jupyter Notebook contains the main code for the project, including data preprocessing, training model and downloading the results and model itself.
• results: You can see results, coco.yaml, test batches, test predictions, train batches, RC curve, F1 curve, confusion matrix, hyp.yaml and opt.yaml.
• leaf_detection_YOLOv7x.py: Project codes with python extension.
• README.md: You are currently reading this file, which provides an overview of the project.

If you have something to say to me please contact me:

• Twitter: Doguilmak
• Mail address: [email protected]