Implementation of Natural Language Processing (NLP) and Convolutional Neural Network (CNN) in the Development of an Agriculture Assistant Chatbot
Abstract
The agricultural sector in Indonesia plays an important role in the country's economy, but farmers often face various challenges in managing their farmland. One solution is to integrate technologies, such as Natural Language Processing (NLP) and Convolutional Neural Networks (CNNs), to provide more precise and efficient information. This research aims to develop a chatbot-based Agricultural Assistant system that can assist farmers in managing agricultural activities. The system provides features such as weather forecasts, fertilization guides, plant recommendations, and pest and disease management. The method used in this study is software development with a Scrum approach, which allows for rapid iteration and effective collaboration in system development. The test results show that this system has an accuracy rate of 89% in plant disease classification using CNNs, with several classes that
Keywords—Agricultural Assistant; Chatbot; Natural Language Processing; Convolutional Neural Network; Classification of Plant Diseases
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