A Survey: Machine Learning and Deep Learning in Wheat Disease Detection and Classification

Cereals are a significant and vital source of food for humans. To feed the world’s expanding population, farmers must produce more crops. Plant diseases, however, have an impact on crop production and food quality. Wheat is among the most important crops consumed worldwide. Since Wheat Disease causes production loss, early disease detection and classification are absolutely essential. This research study has conducted a literature survey on studies published from the year 2017 to 2022 and summarized the three main types of Wheat Disease (fungal, bacterial, and insects), Wheat Disease datasets, and the current state of the art from the last six years of research. This research study has examined 24 studies on disease identification and classification by using various Machine Learning (ML) and Deep Learning (DL) algorithms. The proposed research analysis shows that the majority of the literature on Wheat Disease focused on fungal disease, and the majority of the datasets used were self-acquired. Many State-of-the-art models have already produced excellent results, and many more need to be developed.

I. Introduction

One of the most widely cultivated crops in the world is wheat. We can say that the second-largest crop in the world is produced by farmers in their fields and consumed as food by people all around the world. An estimated 35% of people globally depend mostly on wheat [1]. More than two thirds of the wheat produced worldwide is consumed for human consumption, with only one fifth going to animal feed. To feed a projected population of 9.8 billion people by 2050, the global civilization will need to double its current food output [2]. Additionally, more wheat needed to be produced because it is one of the most crucial and significant crops in the world and provides 20% of the calories and proteins that people consume [1, 2].