Text Based Smart Answering System in Agriculture using RNN (2021)

Abstract: Agriculture is an important aspect of India’s economy, and the country currently has one of the highest rates of farm producers in the world. Farmers need hand holding with support of technology. A chatbot is a tool or assistant that you may communicate with via instant messages. The goal of this project is to create a Chatbot that uses Natural Language Processing with a Deep Learning model. In this project we have tried implementing Multi-Layer Perceptron model and Recurrent Neural Network models on the dataset. The accuracy given by RNN was 97.83%.

## introduction agriculture contributes around 16 percent of india's gdp and employs about 52 percent of the country's population, making it a significant part of the country's economic growth. according to the farmers' portal, agriculture's rapid expansion is necessary not just for self-sufficiency but also for earning vital foreign exchange. one of the reasons for this is that individuals in the farming industry are relatively sluggish to accept emerging innovations. field officers have traditionally visited farmlands to give training, guidance, and assistance to farmers. the data demonstrates that mobile connection is increasing at an exponential rate, which helps it services promote agricultural information. the government is having difficulty disseminating important agricultural information. furthermore, the difficulties are exacerbated by the dissemination of disinformation. these issues exist as a result of the huge linguistic variety and the rural population's lack of trust in contemporary technologies. in such a situation, using mobile devices to disseminate agricultural information looks to be a viable option foot_0 (k., 2020). chat-bot sys-tems are a type of natural language processing that demands the system to be taught in human language in order to meet the user's demands. agriculture is the most important sector for a country's development. farmers are now unaware of the most modern technology and methods employed in agriculture. the challenge of extracting meaningful answers using machine learning techniques has been researched by numerous machine learning specialists, and sophisticated machine learning approaches have been created. these methods are used to obtain the correct answer. we may name this an agriculture question answering system, since the farmer can ask the system a question, and the system will answer @xcite @xcite . with the advancement of technology, farmers must study and address the challenges. as a result, the goal is to create a chatbot system that delivers accurate responses to queries. according to a major study in the field of chatbot systems, there is no agriculturespecific system that can provide precise and rapid answers to farmers' questions. to solve this issue, the suggested system uses the rnn (recurrent neural network) deep learning method to offer accurate responses to the queries asked. ## problem statement the traceability software from source trace gives you complete visibility into the agricultural value chain. it has an influence on farmers' lifestyles, helps an organization adopt data-driven agriculture, and fosters trust and improved interaction with stakeholders. agriculture employed half of india's workforce and provided 17-18% of the country's gdp. agriculture and related industries such as animal husbandry, forestry, and fisheries accounted for 15.4% of gdp in 2016 and employed around 31% of the workforce in 2014. the goal of this project is to create a chatbot that uses natural language processing to facilitate remote interaction between users/farmers and the agriculture environment. we aim to create a chatbot that can answer basic questions from farmers and give possible agricultural knowledge and solutions. because this chatbot has been educated in natural language processing, it can learn on its own and improvise responses. the study's target audience is agriculturists or farmers. their work will be based on the model established. as a consequence, agriculturists will be able to benefit from the study's findings. it has an influence on farmers' livelihoods, helps organizations adopt data-driven agriculture, and fosters trust and improved interaction with stakeholders. the farmers' favorable reaction suggests that conversational intelligence, as a technology supplied via the omnipresent smartphone, can be a useful tool for improving information access in rural areas for those with low literacy and technological expertise. ## result in this project i have tried implementing multi-layer perceptron model and recurrent neural network models on the dataset. the accuracy given by both the models are comparable, but rnn achieved an accuracy score of 97.83% whereas mlp resulted in an accuracy of 96.97%. the main benefit of rnn over feed forward neural networks is that rnn can represent a collection of records (i.e. time collection), allowing each pattern to be considered to be reliant on the preceding one. in this example, the algorithm is trained using a combination of the knowledge base and behavioral intent scenarios. the performance of both the models are then evaluated by fitting them for predictions in the response processor. response processor was designed in such a way that the intent classification is done using the trained models and it produces tuples with class labels and corresponding accuracy of predictions made. the below figure 6 and figure 7 shows the performance evaluation of the models in the response processor. ## conclusion using natural language technology, this chatbot can help underprivileged areas by answering questions about agriculture, horticulture, and animal husbandry. through a messaging app, the farmer will be able to get agricultural information as well as localized information such as current market prices for specific commodities in his/her district and disease management. a farmer can send a direct message to our intelligent answering system and receive a response. our approach would allow farmers to ask as many questions as they want, at any time, allowing current farming technologies to reach a larger number of farmers faster. agriculture chatbots play a critical role in the agriculture industry, assisting all farmers and others interested in agricultural operations by analyzing queries and providing relevant information. this question-answer system is capable of answering most inquiries without the need for human interaction and with excellent accuracy. this would result in greater human resource use and the avoidance of needless expenditures associated with the establishment of additional contact centres. above all, i believe the method aids in the analysis of farmers' mindsets as well as the structure of india's agricultural sector. while the technology provides a safe communication route for farmers, it also aids policymakers in comprehending their wants and concerns. the data analysis also reveals which sectors or seasons demand special attention from farmers. as a result, the decision support system makes effective use of all available resources to address the problem of lack of awareness and knowledge in india's agricultural industry.