Technological Advancements in Automated Crop Pest and Disease Detection: A Review & Ongoing Research

Abstract

Automated crop pests and disease detection have fateful effects on food safety, leading to significant deterioration in agriculture products. The effects of crop diseases and pests can be so severe that a harvest may even be ruined entirely. Therefore, automatic recognition and diagnosis of crop disease is required in the agricultural field. However, Fast and accurate crop disease detection is still a challenging and error-prone task. Earlier, traditional methods were used to detect abnormalities in crops caused by fungus, pests and nutritional deficiency. Moreover, in some cases, it is time-consuming, expensive and impractical. To overcome these issues, experimental research is being performed into the use of image processing techniques for crop disease detection using machine learning, artificial intelligence, deep learning, generative adversarial networks and the internet of things. In this study, a comprehensive literature review of current studies is performed in crop disease and pest recognition using image processing to extract the features and algorithms used in prediction studies. In particular, several models have reported better accuracy on specific data sets. In contrast, in the case of different data sets or field conditions, the performance of the models degraded significantly. Despite this, progress has been encouraging so far. Furthermore, different inputs gained from the literature indicate that the aforementioned techniques provide better accuracy in comparison with existing techniques. Additionally, a detailed study has been performed on several unresolved challenges to develop a framework for automated crop pests and disease detection to use in real field conditions.