Several factors associated with disease diagnosis in plants using deep learning techniques
must be considered to develop a robust system for accurate disease management. A …

In the modern era, deep learning techniques have emerged as powerful tools in image
recognition. Convolutional Neural Networks, one of the deep learning tools, have attained …

Traditional imagery—provided, for example, by RGB and/or NIR sensors—has proven to be
useful in many agroforestry applications. However, it lacks the spectral range and precision …

Deep learning (DL), a subset of machine learning approaches, has emerged as a versatile
tool to assimilate large amounts of heterogeneous data and provide reliable predictions of …

Plant Diseases and Pests are a major challenge in the agriculture sector. An accurate and a
faster detection of diseases and pests in plants could help to develop an early treatment …

This work is inspired by Kaggle competition which was part of the Fine-Grained Visual
Categorization workshop at CVPR 2019 (Conference on Computer Vision and Pattern …

Highlights•Challenges of applying deep learning to plant pathology problems are
characterised.•The impact of those challenges on current proposals is discussed.•Possible …

Automatic and accurate estimation of disease severity is essential for food security, disease
management, and yield loss prediction. Deep learning, the latest breakthrough in computer …

Current approaches for accurate identification, classification, and quantification of biotic and
abiotic stresses in crop research and production are predominantly visual and require …

Background Hyperspectral imaging is emerging as a promising approach for plant disease
identification. The large and possibly redundant information contained in hyperspectral data …