Year: 2025
Type: article
Abstract: The agricultural sector in India, despite its status as a leading producer globally, grapples with low farm productivity, resulting in diminished incomes for farmers. Addressing this challenge requires a strategic approach centered around increasing productivity, thereby enhancing farmer livelihoods. Crucially, farmers must be equipped with the knowledge of which crops are best suited to their specific plots of land to optimize yield potential. This entails consideration of various factors such as temperature, humidity, soil pH, rainfall patterns, and nutrient composition. However, many farmers lack access to this vital information, leaving them uncertain about which crops to cultivate for maximum yield and profit. Thus, the implementation of crop recommendation systems powered by machine learning algorithms presents a promising solution. By leveraging data on environmental conditions and soil properties, these systems can accurately predict suitable crop choices for individual farms, empowering farmers to make informed decisions and ultimately improve their productivity and income levels.
Author: DanielRaj K
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4409957125