Identifying and characterizing vascular plants in time and space is required in various
disciplines, eg in forestry, conservation and agriculture. Remote sensing emerged as a key …

The rapid advances in Deep Learning (DL) techniques have enabled rapid detection,
localisation, and recognition of objects from images or videos. DL techniques are now being …

Deep Learning (DL) has been described as one of the key subfields of Artificial Intelligence
(AI) that is transforming weed detection for site-specific weed management (SSWM). In the …

Deep learning (DL) is a robust data-analysis and image-processing technique that has
shown great promise in the agricultural sector. In this study, 129 papers that are based on …

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 …

The developments of information and automation technologies have opened a new era for
weed management to fit physical and chemical control treatments to the spatial and …

Weed detection has become an integral part of precision farming that leverages the IoT
framework. Weeds have become responsible for 45% of the agriculture industry's crop …

Deep learning constitutes a recent, modern technique for image processing and data
analysis, with promising results and large potential. As deep learning has been successfully …

Rapid improvements in deep learning (DL) techniques have made it possible to detect and
recognize objects from images. DL approaches have recently entered various agricultural …

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 …