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Machine Learning for Crop Image Analysis using the IP102 Dataset
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 84 |
| Year of Publication: 2026 |
| Authors: Narsaiah Battu, Arathi Chitla |
10.5120/ijca2026926475
|
Narsaiah Battu, Arathi Chitla . Machine Learning for Crop Image Analysis using the IP102 Dataset. International Journal of Computer Applications. 187, 84 ( Feb 2026), 44-52. DOI=10.5120/ijca2026926475
Abstract
Considering growing environmental concerns and needs for food security, crop protection, and pest control are essential elements of sustainable agriculture. The publicly accessible IP102 dataset, which includes photos of 102 pest species taken in a variety of climatic and environmental settings, is used in this work to demonstrate how machine-learning approaches can be applied to crop picture analysis. An important obstacle to automated pest categorization in this dataset is the variation in illumination, background, and image quality. A thorough preparation procedure is part of our strategy to improve dataset quality and reduce environmental variability. The foundation for classifying pest species is a convolutional neural network (CNN), which allows the model to extract intricate patterns and characteristics from the data. Data augmentation techniques are also used to strengthen the model's resilience and make it more appropriate for actual agricultural situations. This study lays the groundwork for creating machine learning models that can effectively identify pests, assisting researchers and farmers in putting timely and focused pest control measures into place. This work advances precision farming methods and creates avenues for future research to improve agricultural output and sustainability by investigating the potential of combining machine learning with agriculture.
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