A Survey on the usage of numerous ML Models for agriculture

Machine Learning has proved that it has the potential to tremendously increase crop production along with quality by providing insight into the required data. Machine Learning has ensured more accuracy by enabling farmers individually to make decisions related to farming. Machine Learning impacts the efficacy of crop classification and quality, agrochemical production, detection of plant diseases, and prevention of it. Deep learning algorithms can process an enormous amount of raw field data such as how crops have performed in different climates, and how they have inherited certain traits, and this information can be utilized to create a probability model. With such information, machine learning can predict the most beneficial breed. Machine learning is more systematic and helps in making precise decisions. This paper discusses a comprehensive survey on how different authors have adopted Machine Learning (ML) and Deep Learning (DL) algorithms along with insight of agriculture techniques for better results.