Use of AI for Disaster Risk Reduction in Agriculture

Agriculture, being significantly related to and dependent on natural factors, is most vulnerable to abnormal behaviors of nature. The trends, frequency, and intensity of natural disasters are becoming much more unpredictable with changing climatic and meteorological behaviors. The last decade has seen a significant boom in disastrous events; in turn, the socioeconomic loss incurred by different sectors of society also jumped all-time high. Agriculture tolerates the major parts of these damages and losses among all other sectors. Owing to such rising concerns, sophisticated technologies have made their way through to disaster risk reduction (DRR) and management in agriculture. Artificial intelligence (AI) and its domains, i.e., machine learning (ML) and deep learning (DL), are being increasingly used to predict, forecast, detect, monitor, and assess natural disasters in the context of agriculture. In this study, we have reviewed recently published literature relative to such application of AI in DRR in an agricultural context. An in-depth qualitative examination of recent studies reflected that AI itself is in the progression phase. Newer stand-alone and hybrid algorithms are being developed and applied to agriculture-related real-world problems. Similarly, the effectiveness and modeling accuracy of these models keep on getting better with every innovation and parametric requirement also decreases. We hope to see the pinnacle of AI application and minimal disaster-related losses as its outcomes in the prospect of AI integration in agricultural DRR.