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Abstract
Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.
Highlights
There are widespread applications of deep learning (DL) around the world.These applications include health care [1], visual data processing [2], social network analysis [3], and audio and speech processing [4]
All leaf images are removed from their plants and shown against a grey background, and the samples have been labelled by experts
Research has revealed that there is a difference in the results of diagnosing models when they are trained with leaf image samples that are removed from the plant and those taken in
Summary
There are widespread applications of deep learning (DL) around the world. These applications include health care [1], visual data processing [2], social network analysis [3], and audio and speech processing (e.g., recognition and enhancement) [4]. Deep learning technology has been successfully employed as a robust tool in image classification [9] and disease detection based on medical images in the biomedical field [10,11]. There are some limitations [19,20] that are still considered as challenges for researchers using unsupervised models These challenges are highlighted in (Figure 1) and discussed
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