👉 The project involved developing an automated system for detecting and classifying various types of agricultural pests using computer vision and machine learning. We collected a dataset of images from farms, labeled with different pests like aphids, caterpillars, and beetles. Using convolutional neural networks (CNNs), we trained the model to accurately identify these pests in new images. The system was then tested on a separate dataset, achieving high accuracy rates in both detection and classification tasks, enabling farmers to quickly and efficiently monitor their crops for pest infestations. This tool not only saves time but also helps in applying targeted treatments, reducing chemical usage and promoting sustainable farming practices.
