The digital transformation of agriculture has evolved various aspects of management into
artificial intelligent systems for the sake of making value from the ever-increasing data …

Smart agriculture techniques have recently seen widespread interest by farmers. This is
driven by several factors, which include the widespread availability of economically-priced …

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 …

Solanum rostratum Dunal is a common invasive alien weed that can damage native
ecosystems and biodiversity. Detecting Solanum rostratum Dunal at an early stage of growth …

Features are the vital factor for image classification in the field of machine learning. The
advancement of deep convolutional neural network (CNN) shows the way for identification …

Weeds are one of the most important factors affecting agricultural production. The waste and
pollution of farmland ecological environment caused by full-coverage chemical herbicide …

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 …

Reducing the use of pesticides through selective spraying is an important component
towards a more sustainable computer-assisted agriculture. Weed identification at early …

Smart farming is a new concept that makes agriculture more efficient and effective by using
advanced information technologies. The latest advancements in connectivity, automation …