The digitalization of data has resulted in a data tsunami in practically every industry of data-
driven enterprise. Furthermore, man-to-machine (M2M) digital data handling has …

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 …

Crop production can be greatly reduced due to various diseases, which seriously endangers
food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional …

Precision farming is made possible by rapid advances in deep learning (DL) and the internet
of things (IoT) for agriculture, allowing farmers to upgrade their agriculture operations to …

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 …

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 …

The growing population and effect of climate change have put a huge responsibility on the
agriculture sector to increase food-grain production and productivity. In most of the countries …

Conventional weed management approaches are inefficient and non-suitable for integration
with smart agricultural machinery. Automatic identification and classification of weeds can …

In almost every sector, data-driven business, the digitization of the data has generated a
data tsunami. In addition, man-to-machine digital data handling has magnified the …