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Conventional to Modern Agriculture Using Artificial Intelligence

Conventional to Modern Agriculture Using Artificial Intelligence

Smrati Tripathi, Prasen Jeet, Rijwan Khan, Akhilesh Kumar Srivastava
Copyright: © 2024 |Pages: 20
ISBN13: 9798369307823|ISBN13 Softcover: 9798369346792|EISBN13: 9798369307830
DOI: 10.4018/979-8-3693-0782-3.ch009
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MLA

Tripathi, Smrati, et al. "Conventional to Modern Agriculture Using Artificial Intelligence." Infrastructure Possibilities and Human-Centered Approaches With Industry 5.0, edited by Mohammad Ayoub Khan, et al., IGI Global, 2024, pp. 142-161. https://doi.org/10.4018/979-8-3693-0782-3.ch009

APA

Tripathi, S., Jeet, P., Khan, R., & Srivastava, A. K. (2024). Conventional to Modern Agriculture Using Artificial Intelligence. In M. Khan, R. Khan, P. Praveen, A. Verma, & M. Panda (Eds.), Infrastructure Possibilities and Human-Centered Approaches With Industry 5.0 (pp. 142-161). IGI Global. https://doi.org/10.4018/979-8-3693-0782-3.ch009

Chicago

Tripathi, Smrati, et al. "Conventional to Modern Agriculture Using Artificial Intelligence." In Infrastructure Possibilities and Human-Centered Approaches With Industry 5.0, edited by Mohammad Ayoub Khan, et al., 142-161. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-0782-3.ch009

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Abstract

Because of increased pollution and population, the agriculture system is being affected drastically. New technologies should be implemented in this sector. The author discussed irrigation techniques and patterns using artificial intelligence that can change our irrigation process and minimize water consumption. Artificial intelligence approaches for soil fertility properties, the connection between soil quality, effect of fertiliser on soil possession and crop fecundity, season's growth, modelling of some soil properties, and estimation of polluted soil are being discussed. One more essential factor that reduces crop growth, quality, and yield is called weeds. Different types of weed control applications using ANN and ANFIS model have been discussed. To minimize monetary losses for farmers, disease control becomes a necessary parameter for better crop production. Image-based strategies using machine learning and deep learning for exact location, order of the disease, for precise and accurate identification have been considered.

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