Exploring Machine Learning in Precision Seeding: A Review
Sovan Sankalp, S R Ruchitha, R - Ruchitha, Sadananada Gowda, J. R. Rashmi · 2024
Precision seeding in agriculture requires accurate placement of seeds to optimize crop yield. This paper surveys the application of machine learning (ML) techniques, including Genetic Algorithms, Support Vector Machines (SVM), Back Propagation, Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM), and Deep Neural Networks (DNN), in enhancing precision seeding processes. It explores ML algorithms for analyzing soil conditions, weather patterns, and historical data to provide actionable insights for farmers. Additionally, it discusses the integration of machine vision and sensor networks for real-time monitoring of field conditions. Case studies and advancements in precision agriculture are highlighted, and the focus is on practically implementing ML models in the development of seeding equipment and autonomous farming systems. Future research directions and challenges in adopting ML for precision seeding are also discussed. This survey serves as a helpful tool for both researchers and practitioners interested in leveraging ML to transform precision seeding practices and promote sustainable agriculture.