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Harnessing Agrosphere Data for Precision Plant Disease Diagnosis
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Author(s): Padmaja Kadiri (Mohan Babu University, Tirupati, India), Suresh Ramayanam (Sri Venkateswara College of Engineering, Tirupati, India), Prakash Putta (Mohan Babu University, Tirupati, India)and C. Lakshmi (Raja Rajeswari College of Engineering, Bangalore, India)
Copyright: 2027
Pages: 16
Source title:
Encyclopedia of Modern Artificial Intelligence
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Founding Editor-in-Chief, Information Resources Management Journal (IRMJ), USA)
DOI: 10.4018/407603
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Abstract
Farmers often deal with social and financial difficulties. Many soil types found in different places make it challenging to choose the best and most profitable crop for a given area. This chapter suggests developing a crop recommendation system using a machine learning (ML) model to overcome this difficulty. To predict the best crop for successful cultivation, this system will look at the variables including the area, soil type, yield, and sale price. In agriculture, plant diseases are common and create difficulties for farmers. It is necessary to detect these diseases, especially in large areas. It is challenging for farmers to move forward amid disease control strategies. Plant production and quality deteriorate if left uncontrolled. Introduction to a machine learning-based crop recommendation system is a possible way to address these issues. To recommend the best crops for farming, this system analyzes various factors including geography, soil type, yield, and market prices.
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