Chatting with Plants (Orchids) in Automated Smart Farming using IoT, Fuzzy Logic and Chatbot

Abstract

Plants are living organisms that can hear and recognize the environment around them but cannot communicate to inform their needs. Thus, in the past, humans thought that it was impossible to communicate with plants. However, in this modern era, humans can be able to communicate with these plants. In this paper, we propose a model that can interact (Chat) with plants cultivated in the automated farm system based on Internet of Things (IoT) and Fuzzy Logic. According to the communication of plants and humans, we apply a chatbot algorithm for sending/receiving messages between users and automated smart farming. The messages are processed by the natural language processing (NLP) to parse and interpret the meaning of the conversation. The experimented plant in this paper is orchid, namely Dendrobium Sonia (Bomjo). The result from the evaluation shows that the average accuracy (Harmonic mean) of chatting between the user and the orchid is equal to 0.71, the precision and recall are 0.75 and 0.6 respectively.

Application of Smart Techniques, Internet of Things and Data Mining for Resource Use Efficient and Sustainable Crop Production

“…Precision agriculture has been used for years and now researchers are considering the use of variable rate technologies [70], sensor monitoring [16], and management systems to ensure better crop health [27], improved productivity [28], and better quality [71] of the produce. Sensor-and drone-assisted quality monitoring of horticultural crops [21], yield predicting sensors [72] on harvesters of various agronomic crops [73] (Figure 4), and use of the internet and real time data simulators [74] are receiving attention day by day, particularly for their use in large scale crop production. Various environmental components are considered, and their impact on crop production have been evaluated using modern techniques.…”

Smart Farming Revolution: Farmer’s Perception and Adoption of Smart IoT Technologies for Crop Health Monitoring and Yield Prediction in Jizan, Saudi Arabia

“…There are several factors that contribute to the prediction of a plant's or crop's overall yield, including soil properties, weather, seasonal fluctuations, seed quality, harvesting techniques, pest and disease monitoring, nutrient deficiencies, managing water requirements, and managing pest and disease problems. Since precision agriculture has been used for decades, researchers are now considering using sensor monitoring systems [22] and management systems so that crops can be kept healthy [15], productivity can be increased [16], and product quality can be improved in Figure 2 [23] .There has been an increased amount of attention devoted to sensors and drones used to monitor the quality of horticultural crops [24,25] and sensors that predict yields in various agronomic crops [26,27] and can be placed on harvesters for various crops [28], as well as the use of real-time data simulators [29]; moreover, the use of the Internet and real-time data simulators in crop production are gaining increasing attention. A number of studies have been conducted in the past that have shed light on the benefits of the Internet of Things in agriculture.…”

“…Since precision agriculture has been used for decades, researchers are now considering using sensor monitoring systems [22] and management systems so that crops can be kept healthy [15], productivity can be increased [16], and product quality can be improved in Figure 2 [23]. There has been an increased amount of attention devoted to sensors and drones used to monitor the quality of horticultural crops [24,25] and sensors that predict yields in various agronomic crops [26,27] and can be placed on harvesters for various crops [28], as well as the use of real-time data simulators [29]; moreover, the use of the Internet and real-time data simulators in crop production are gaining increasing attention.…”

Lysis chatbot: A virtual assistant for IoT platforms

“…In [6] the authors implement a chatbot integrated with an agricultural plant monitoring system. In their implementation they use fuzzy logic and Natural Language Processing (NLP) to interpret user inputs.…”