Crop-saving with AI: latest trends in deep learning techniques for plant pathology

Abstract

Plant diseases pose a major threat to agricultural production and the food supply chain, as they expose plants to potentially disruptive pathogens that can affect the lives of those who are associated with it. Deep learning has been applied in a range of fields such as object detection, autonomous vehicles, fraud detection etc. Several researchers have tried to implement deep learning techniques in precision agriculture. However, there are pros and cons to the approaches they have opted for disease detection and identification. In this survey, we have made an attempt to capture the significant advancements in machine-learning based disease detection. We have discussed prevalent datasets and techniques that have been employed as well as highlighted emerging approaches being used for plant disease detection. By exploring these advancements, we aim to present a comprehensive overview of the prominent approaches in precision agriculture, along with their associated challenges and potential improvements. This paper delves into the challenges associated with the implementation and briefly discusses the future trends. Overall, this paper presents a bird’s eye view of plant disease datasets, deep learning techniques, their accuracies and the challenges associated with them. Our insights will serve as a valuable resource for researchers and practitioners in the field. We hope that this survey will inform and inspire future research efforts, ultimately leading to improved precision agriculture practices and enhanced crop health management.

Harnessing the power of diffusion models for plant disease image augmentation

“…Computer vision, a field that enables computers to gain a high-level understanding from digital images or videos, has been instrumental in automating the process of disease detection and classification in plants. The application of computer vision in plant sciences has been facilitated by the development of Convolutional Neural Networks (CNNs), which have shown remarkable success in image classification tasks [Salman et al (2023)]. The successful application of computer vision technologies in plant sciences is heavily reliant on the existence of broad and diverse datasets.…”