Potential Application of Machine Learning on Agriculture and Capture Fisheries

Abstract

Disease and pests have been reported to deteriorate the yield of crops and aquatic farming. These deteriorating effects should be alleviated because these sectors are important, not only for supporting the national economy but also providing enough food for all. Some methods have been implemented to increase the agricultural output such as the application of insecticide, fungicide and some chemical medicine, but fewer approaches based on digitalization have been applied. The previous naked eye diagnostics are not error-free; therefore, a more advanced technique is required to feel the gap. Adapting a machine learning process for the detection of pests and diseases on crop plants and aquatic animals should be initiated. Artificial intelligence (AI) has helped many farmers to detect diseases and pests in the field. Therefore, this paper analyzes the current and future effects of climate change dealing with the prevention of disease and pest outbreaks in agricultural systems and fisheries, various practical applications of machine learning (ML) tools in agriculture and capture fisheries domains as well as a perspective of approaching climate-smart system for agriculture and fishery farming.