Year: 2024
Type: article
Abstract: Crop Recommendation Systems based on machine learning have changed the complexion of farming to supply information-driven analysis regarding which crops ought to be planted. SVM has technically been implemented along with the current system thus far due to its ability to predict sound crop suggestions with the consideration of soil health, climate conditions, and other past crop history. Nevertheless, the inclusion of the LSTM algorithm is more efficient and accurate to forecast overall trends due to the occurrence of long-term dependency in data. The effectiveness of LSTM relies on tapping temporal dependencies particularly in sequences where trends and patterns stored in chronological data are utilized to enhance crop recommendations. Moreover, the incorporation of Ant Colony Optimization (ACO) helps determine the most lucrative areas for cultivation by examining the dataset to forecast regions with the greatest potential yield. This integration of sophisticated machine learning methods greatly enhances the overall efficiency of crop recommendation systems, allowing farmers to achieve maximum productivity and profitability.
Author: S. Pariselvam S. Pariselvam
Institution: Sri Manakula Vinayagar Educational Trust
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4410049926