Legume Cowpea Leaves Classification for Crop Phenotyping Using Deep Learning and Big Data

Crop phenotyping plays a critical role in precision agriculture by enabling the accurate assessment of plant traits, supporting improved crop management, breeding programs, and yield optimization. However, cowpea leaves present unique challenges for automated phenotyping due to their diverse shapes, complex vein structures, and variations caused by environmental conditions. This research presents a deep learning-based approach for the classification of cowpea leaf images to support crop phenotyping tasks. Given the limited availability of annotated datasets, data augmentation techniques were employed to artificially expand the original small dataset while preserving essential leaf characteristics. Various image processing methods were applied to enrich the dataset, ensuring better feature representation without significant information loss. A deep neural network, specifically the MobileNet architecture, was utilized for its efficiency in capturing multi-scale features and handling image data with limited computational resources. The performance of the model trained on the augmented dataset was evaluated, achieving an accuracy of 94.12% on the cowpea leaf classification task. These results demonstrate the effectiveness of data augmentation in enhancing model generalization and learning capabilities.