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A curated collection of 45 high-quality RGB image datasets for computer vision in agriculture. Features datasets for weed detection, disease identification, and crop monitoring, focusing on natural field scenes. Part of our GIL 2025 survey paper.
'CNN_Sorghum_Weed_Classifier' is an artificial intelligence (AI) based software that can differentiate a sorghum sampling image from its associated weeds images.
Created image classifiers as well as an access model by using Python to create a process for the processing of image data. This pipeline include: • Pre-processing, feature extraction, train classifiers with extracted features and labels from train, test, and val set. • Evaluate models with extracted features from test and val set with Visualisation