Plant Disease Identification Based on Multimodal Learning
Keywords:
Neural network, multimodal, plant diseases, Deep Learning, artificial intelligenceAbstract
Over the last ten years, multimedia learning research has progressed quickly in various fields, especially computer vision. Deep multimodal Learning is becoming more prevalent due to the growing potential of deep learning methodologies and multimodal data streams. This necessitates the development of models that can really process and analyze multidimensional data appropriately. Un-structured real-world data can naturally exist in various modalities, also known as formats, and recovered and reused mixed visual and textual data. Deep learning researchers are still prompted by the desire to extract meaningful patterns from this data. This article explores how to develop deep models that take into consideration integrating and merging heterogeneous visual data from many sensory modalities to enhance the comprehension of deep multimodal learning by the majority of computer vision researchers. Diagnosing plant diseases has become digitalized and data-driven with the rapid growth of intelligent farming, providing enhanced decision support, analysis, and planning. Meanwhile, deep Learning-based advancements in artificial intelligence and computer vision have enabled device-assisted illness detection possible, and smartphone usage is rising rapidly. This research uses a dataset of more than 54,000 controlled images of sick and standard plant leaves to identify 14 plants and associated 26 diseases, creating a deep convolutional neural network for this goal. The model outperforms a test set resistance training accuracy rate of 99.06%. Generally, device-assisted crop disease diagnosis is achieved by the ability to train deep-learning models utilizing large and expanding public image datasets.
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