Year: 2024
Type: article
Abstract: In India, agriculture plays a crucial role in the economy, accounting for approximately 18% of GDP and employing nearly half of the nation's workforce. A significant challenge faced by Indian farmers is the selection of crops unsuitable for their land soil conditions, resulting in reduced yields. A potential solution is precision agriculture, which utilizes data-driven methods to tailor crop management to the specific characteristics of each field. Nevertheless, India's agricultural output remains below the global average, potentially correlating with the high rate of farmer suicide in rural regions. This study introduces a yieldprediction model aimed at assisting farmers in making informed decisions regarding crop selection and expected harvests. By inputting the relevant information, the system can recommend the most appropriate crops for a given field and forecast their potential yields. Among the various machine learning techniques evaluated, the random forest algorithm demonstrated the highest accuracy, with a 95% success rate. Furthermore, the system provides guidance on the optimal timing of fertilizer application for enhancing crop productivity
Author: Annaji Kuthe
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4405209021