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ISSN 2063-5346
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Farm-assist: Comprehensive Approach to Assist Farmers in the Agricultural Domain

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Bhawna Menghani, Bhawna Jain, Ayush Maithani, Jatin Garg
» doi: 10.31838/ecb/2023.12.sa1.071

Abstract

India’s economy and employment are heavily reliant on agriculture. The most common problem faced by farmers of India today is their inability to select an ideal crop that depends upon the characteristics of region and past yields where they live. It therefore faces a large drop in terms of productivity. The administration has not explored agricultural statistics and projections to the extent necessary to fully understand their significance. The article introduces a portable, intelligent tool that uses machine learning techniques to assist farmers in selecting the best crops based on local climatic and soil conditions as well as other geographical characteristics. The sources available to farmers to answer all of their questions on seeds, fertilisers, market circumstances, storage facilities, government programmes, etc. are insufficient. A chatbot that employs the Deep Learning approach gives the farmers relatively easy access to this data analysis. In order to facilitate farmer contact, it is helpful to provide response to the input queries addressing the agricultural setting in audio format. If the system is unable to respond in any way, the inquiries are forwarded to helpline centres. The system's main function is to serve as a convenient and virtual helper for farmers all year long. It enables them to remain vigilant about every element that can affect agricultural productivity and revenue. Several machine learning algorithms that are built around the data set are used to generate the responses. Even though the system's primary goal is to support farmers more, anyone else who uses it, including children, can utilise it to gain help with all tasks associated with agriculture

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