Forecasting Plant and Crop Disease: An Explorative Study on Current Algorithms

Gianni Fenu,Francesca Maridina Malloci

doi:10.3390/bdcc5010002

Abstract

Every year, plant diseases cause a significant loss of valuable food crops around the world. The plant and crop disease management practice implemented in order to mitigate damages have changed considerably. Today, through the application of new information and communication technologies, it is possible to predict the onset or change in the severity of diseases using modern big data analysis techniques. In this paper, we present an analysis and classification of research studies conducted over the past decade that forecast the onset of disease at a pre-symptomatic stage (i.e., symptoms not visible to the naked eye) or at an early stage. We examine the specific approaches and methods adopted, pre-processing techniques and data used, performance metrics, and expected results, highlighting the issues encountered. The results of the study reveal that this practice is still in its infancy and that many barriers need to be overcome.

Highlights

Crop and plant diseases entail serious implications for food security and production losses
The recent employment of new information and communication technologies (ICT) such as the Internet of Things (IoT) [9], remote sensing [10], and cloud computing [11] are incentivizing the diffusion of Precision Agriculture (PA), defined as the application of technologies and principles to manage the spatial and temporal variability associated with all aspects of agricultural production for the purpose of improving crop performance and environmental quality [12]
This review considers crop and plant disease prediction models that adopt Artificial Intelligence (AI), Machine Learning (ML), and DL algorithms to predict symptoms before they appear in the field or in an early stage with mild and small lesions

Summary

Introduction

Crop and plant diseases entail serious implications for food security and production losses. As the Food and Agriculture Organization of the United Nations (FAO) [1] asserts, plant pests and diseases are responsible for losses from 20% to 40% of annual global food production. This means that timely disease management will be necessary in order to address the increased food demand caused by population growth estimated by 2050 [2]. The recent employment of new information and communication technologies (ICT) such as the Internet of Things (IoT) [9], remote sensing [10], and cloud computing [11] are incentivizing the diffusion of Precision Agriculture (PA), defined as the application of technologies and principles to manage the spatial and temporal variability associated with all aspects of agricultural production for the purpose of improving crop performance and environmental quality [12]

Methods

Findings

Discussion

Conclusion