Machine learning in agriculture: a review of crop management applications

Machine learning has created new opportunities for data-intensive study in interdisciplinary domains as a result of the advancement of big data technologies and high-performance computers. Search engines, email spam filters, websites that offer personalized recommendations, banking software that alerts users to suspicious activity, and a plethora of smartphone apps that perform tasks like voice recognition, image recognition, and natural language processing are just a few examples of the online and offline services that have incorporated machine learning in recent years. One of the most crucial areas where machine learning applications still has to be investigated is agriculture, which directly affects people’s well-being. In this article, a literature review on machine learning algorithms used in agriculture is presented. The proposed paper deal with various crop management applications which are categorised into five parts i.e., Weed and pest detection, Plant disease detection, Stress detection in plants, Smart farms or automation in farms and the last one is Crop yield estimation and prediction. The articles’ filtering and categorization show how machine learning may improve agriculture. This article examines machine learning breakthroughs in agriculture. This paper’s findings show that by using novel machine learning approaches, models may achieve improved accuracy and shorter inference time for real-world applications.