MULTI-SPECIES WEED AND CROP CLASSIFICATION COMPARISON USING FIVE DIFFERENT DEEP LEARNING NETWORK ARCHITECTURES

Abstract

The detection of individual weed and crop species from RGB images is a challenging task that becomes even more difficult as the number of species increases. This is because similarities in the phenotypic traits of weeds and crops make it difficult to accurately distinguish one species from another. In this study, five deep learning Convolutional Neural Networks (CNNs) were employed to classify six weed and eight crop species from North Dakota and assess the performance of each model for specific species from a single image. An automated data acquisition system was utilized to collect and process RGB images twice in a greenhouse setting. The first set of data was used to train the CNN models by updating all of its convolutional layers, while the second set was used to evaluate the performance of the models. The results showed that all CNN architectures, except Densenet, demonstrated strong performance, with macro average f1-scores (measurement of model accuracy) ranging from 0.85 to 0.87 and weighted average f1-scores ranging from 0.87 to 0.88. The presence of three weed classes—palmer amaranth, redroot pigweed, and waterhemp, all of which share similar phenotypic traits—negatively affected the model's performance. In conclusion, the results of this study indicate that CNN architectures hold great potential for classifying weed and crop species in North Dakota, with the exception of situations where plants have similar visible characteristics.