Good and Bad Classification of Pigweed leaf (Chenopodium album)
Description
The project focuses on classifying pigweed leaves into "good" and "bad" categories using a dataset comprising more than 500 images. All images were captured using a Samsung F62 mobile camera, ensuring a consistent and standardized data collection process. The leaves were photographed against a black background in daylight conditions to minimize environmental variations. Dataset Composition: Good Samples (Healthy): Over 500 images showcase healthy pigweed leaves, capturing their vibrant colors, normal growth patterns, and overall well-being. These images serve as positive examples for training the machine learning model. Bad Samples (Unhealthy): An equivalent number of images feature pigweed leaves affected by diseases, pests, nutritional deficiencies, or environmental stressors. This negative class provides diverse instances of unhealthy leaves for model training and evaluation. Data Collection Setup: Images were captured using the Samsung F62 mobile camera to ensure high-quality and consistent visual representation. The use of a black background enhances the contrast, making it easier for the model to focus on leaf characteristics. Lighting and Environmental Conditions: All images were taken under daylight conditions to maintain natural lighting, mimicking real-world scenarios. This setup aims to enhance the model's robustness by exposing it to variations in lighting that might occur in practical field applications. Image Characteristics: The dataset includes pigweed leaves exhibiting various shapes, sizes, and degrees of damage. This diversity ensures that the model can generalize well and accurately classify leaves under different conditions. Data Annotation: Each image in the dataset is meticulously annotated to indicate whether it falls into the "good" or "bad" category. These annotations serve as the ground truth for model training, validation, and testing. Data Preprocessing: Preprocessing steps involve resizing, normalization, and background standardization to optimize the input for the machine learning algorithm. These steps contribute to the model's ability to handle variations and improve overall performance. Objective: The project aims to develop a robust machine learning model capable of accurately classifying pigweed leaves as "good" or "bad" based on visual cues captured by the Samsung F62 mobile camera. The application of this model can aid farmers in early detection of issues affecting pigweed crops, facilitating timely intervention for improved crop management. Outcome: The project seeks to deliver a trained model with practical applications in agriculture, specifically assisting farmers in making informed decisions about their pigweed crops. The performance of the model will be rigorously evaluated to ensure its accuracy and effectiveness in real-world scenarios, contributing to advancements in precision agriculture.