Deep learning is a branch of artificial intelligence. In recent years, with the advantages of
automatic learning and feature extraction, it has been widely concerned by academic and …

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 …

Traditional target detection methods cannot effectively screen key features, which leads to
overfitting and produces a model with a weak generalization ability. In this paper, an …

In agricultural image analysis, optimal model performance is keenly pursued for better
fulfilling visual recognition tasks (eg, image classification, segmentation, object detection …

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 …

Remote sensing is a useful tool for monitoring spatio-temporal variations of crop
morphological and physiological status and supporting practices in precision farming. In …

Since whole-genome sequencing of many crops has been achieved, crop functional
genomics studies have stepped into the big-data and high-throughput era. However …

Plant diseases affect the growth of their respective species, therefore their early identification
is very important. Many Machine Learning (ML) models have been employed for the …

Computer vision is a field that involves making a machine “see”. This technology uses a
camera and computer instead of the human eye to identify, track and measure targets for …

Plant phenotype plays an important role in genetics, botany, and agronomy, while the
currently popular methods for phenotypic trait measurement have some limitations in …