Plant classification is vital for ecological conservation and agricultural productivity, enhancing our understanding of plant growth dynamics and aiding species preservation. The advent of deep learning (DL) techniques has revolutionized this field by enabling autonomous feature extraction, significantly reducing the dependence on manual expertise. However, conventional DL models often rely solely on single data sources, failing to capture the full biological diversity of plant species comprehensively. Recent research has turned to multimodal learning to overcome this limitation by integrating multiple data types, which enriches the representation of plant characteristics. This shift introduces the challenge of determining the optimal point for modality fusion. In this paper, we introduce a pioneering multimodal DL-based approach for plant classification with automatic modality fusion. Utilizing the multimodal fusion architecture search, our method integrates images from multiple plant organs-flowers, leaves, fruits, and stems-into a cohesive model. Our method achieves 83.48% accuracy on 956 classes of the PlantCLEF2015 dataset, surpassing state-of-the-art methods. It outperforms late fusion by 11.07% and is more robust to missing modalities. We validate our model against established benchmarks using standard performance metrics and McNemar's test, further underscoring its superiority.

Deep learning is used in agriculture for several tasks such as quality assessment of crop and vegetation, autonomous fruit picking, and the classification and detection of different species. We will focus on classification in this webinar where we will learn to utilise the capability of a deep learning model to automate identification of flowers. From preparing the images to training and evaluating an existing deep neural network, we will explore how efficiently MATLAB handles these assignments. Besides exploring the features of deep neural network, we will deploy the newly trained model in MATLAB to classify more flower images.