Symptom-Labeled Image Dataset of Rice Plants for Stem Borer Infestation Classification

Description:
The Rice Stem Borer Infestation Dataset is an extensive image dataset of paddy plants with symptoms for the purposes of research in precision agriculture and AI based plant health monitoring. It emphasizes on three classes based on the symptoms: Healthy, Dead Heart, and White Head, corresponding to normal growth and two key damage stages caused by the Yellow Stem Borer (Scirpophaga incertulas) and Striped Stem Borer (Chilo suppressalis).

The dataset consists of 2,096 high-resolution RGB images originally taken in the field, under natural lighting, across different crop fields in Bangladesh, and captured with a 64MP Realme 6 smartphone. Photos were captured under different weather, lighting, and time conditions to mimic actual environments.

A certified agricultural expert confirmed the accuracy of symptom classification for each image. In order to enhance the variability and robustness of the dataset for machine learning purposes, a total of 14,672 augmented images were generated through transformations that include rotation, flipping, zoom, addition of noise, brightness, shearing, and height shifting, providing a total of images of 16,768.

Dataset Details:
1. Original Dataset:
Number of Images: 2,096 high-resolution RGB images ( .jpg format )
Classes:
• Healthy: 1106 images
• Dead Heart :439 images
• White Head: 551 images
2. Augmented Dataset:
Number of Augmented Images: 14,672 high-resolution RGB images ( .jpg format )
Classes:
• Healthy: 7742 images
• Dead Heart: 3073 images
• White Head: 3857 images

Use a data augmentation library or tool (e.g. TensorFlow ImageDataGenerator, OpenCV) to apply transformations.
Define the Parameters Used for Data Augmentation:
Horizontal Flip: Image flipped left-to-right.
Rotation: Rotated by 45 degrees.
Zoom: Random zoom between 0.8x and 1.2x.
Add Gaussian Noise: Random noise with mean=0, std=25.
Height Shift: Shift image vertically by 20% of height.
Brightness Adjustment: Random brightness between 0.5x and 1.5x.
Shearing: Shear (distort) the image with shear factor up to 0.5.

Capture Details:
• Device: Realme 6 (64MP camera)
• Resolution: 475×635 pixels
• Collection Period: January–March 2025
• Environmental Diversity: Varied weather (sunny, cloudy, windy), times (morning, noon, afternoon)

Data Collection Location:
• Satrujitpur, Magura Sadar, Khulna, Bangladesh (Latitude: 23°25'07.0"N, Longitude: 89°29'20.0"E)
• Khajura, Bagherpara Upazila, Jessore, Khulana, Bangladesh (Latitude: 23.2764° N, Longitude: 89.2538° E)

Purpose:
The purpose of this dataset is to help automated, rapid identification of the level of infestation from images of rice stem borers through computer vision and artificial intelligence. It helps improve pest detection systems, reduces crop loss to pests by lessening reliance on pesticide use, and provides and encourages development of strong agricultural monitoring tools.