Abstract:
"In the agricultural sector, identifying nutrient deficiencies in crops such as black pepper plants remain a critical yet challenging task. Without timely detection and intervention, these deficiencies can significantly impact crop yield and quality. However, existing methods for identifying nutrient deficiencies often rely on manual inspection or rudimentary image processing techniques, which can be time-consuming, subjective, and prone to errors. This research addresses this problem by proposing a novel solution in the form of a mobile application designed to accurately detect and classify nutrient deficiencies in black pepper plants based on user-uploaded leaf images. By leveraging advanced deep learning models and image processing algorithms, the application provides farmers and agricultural experts with a user-friendly tool for rapid, reliable, and precise nutrient deficiency diagnosis, empowering them to take proactive measures to mitigate crop damage and optimize yield.
To develop the proposed mobile application, a comprehensive methodology was employed, starting with the collection and curation of a diverse dataset comprising healthy and diseased black pepper leaf images. Subsequently, state-of-the-art deep learning models, such as ResNet, were trained on this dataset to effectively classify different types of nutrient deficiencies. Additionally, advanced image processing techniques were applied to preprocess and enhance the uploaded leaf images, ensuring optimal input quality for the classification model. The application's user interface was carefully designed to facilitate seamless image uploading, classification, and result visualization, prioritizing simplicity and usability for end users. Through iterative testing and refinement, the developed solution demonstrates promising results in accurately identifying nutrient deficiencies in black pepper plants, offering a valuable tool for farmers and agricultural stakeholders to enhance crop management practices and improve overall yield and quality."
