Abstract
Crop diseases constitute a serious issue in agriculture, affecting both quality and quantity of agriculture production. Disease control has been a research object in many scientific and technologic domains. Technological advances in sensors, data storage, computing resources and artificial intelligence have shown enormous potential to control diseases effectively. A growing body of literature recognizes the importance of using data from different types of sensors and machine learning approaches to build models for detection, prediction, analysis, assessment, etc. However, the increasing number and diversity of research studies requires a literature review for further developments and contributions in this area. This paper reviews state-of-the-art machine learning methods that use different data sources, applied to plant disease detection. It lists traditional and deep learning methods associated with the main data acquisition modalities, namely IoT, ground imaging, unmanned aerial vehicle imaging and satellite imaging. In addition, this study examines the role of data fusion for ongoing research in the context of disease detection. It highlights the advantage of intelligent data fusion techniques, from heterogeneous data sources, to improve plant health status prediction and presents the main challenges facing this field. The study concludes with a discussion of several current issues and research trends.
Highlights
We review crop disease detection methods based on unimodal data sources using machine learning algorithms
Numerous research studies were carried out to control and monitor crops, and predict plant health based on meteorological characteristics [100,104,107]
This may be due to the high performance of deep learning (DL) models compared to conventional machine learning models [60]
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Disease surveillance can be performed by capturing data from the soil and plant cover or using sensors, such as remote sensing (RS) or ground equipment, as well as with developing and testing machine learning algorithms [7]. Machine learning-based data fusion has undergone important development, and when used on agriculture data would have a great impact on plant protection field, in particular, disease and early disease detection. Several survey papers have been proposed on the use of machine learning approaches for agriculture mainly based on one of the different extracted data, such as IoT data, ground imagery or remote sensing imagery. We review crop disease detection methods based on unimodal data sources (wireless sensor networks, ground imagery, UAV imagery and satellite imagery) using machine learning algorithms. Remote sensing and precision agriculture technologies for crop disease detection and management.
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