Novel Biomimicry Techniques for Detecting Plant Diseases

Abstract

Biomimicry techniques (BmT) can be regarded as crop management concepts deployed for detecting, measuring, and responding to diseases during agricultural production. In this paper, various approaches of the BmT for the efficient management of unhealthy plants during preharvest and postharvest operations were reviewed. The remote sensors, machine vision, and robotics BmT approaches are mostly applied during preharvest operations for detecting plant diseases. Certain diseases, such as white-soft, gray-mold, and Sclerotium rots, may attack vegetables after harvest, thus requiring a high resolution BmT, like neural network and support vector machine to manage. To the best of our knowledge, this technology is mostly applied for protecting tropical and temperate fruits and vegetables but have not been tested for cereal and legume plants disease management. Also, the resulting image from the BmT can sometimes be misleading due to bad weather and other factors related to equipment performance. Thus, this study will help provide meaningful insights for the advancement of the technology on plant management practice during preharvest and postharvest processing and storage.