Deep learning has emerged as a potential tool for crop yield prediction, allowing the model
to automatically extract features and learn from the datasets. Meanwhile, smart farming …

To reach the goal of sustainable agriculture, smart farming is taking advantage of the
Unmanned Aerial Vehicles (UAVs) and Internet of Things (IoT) paradigm. These smart farms …

The art of predicting crop production is done before the crop is harvested. Crop output
forecasts will help people make timely judgments concerning food policy, prices in markets …

This paper explores the potential of machine learning algorithms for weed and crop
classification from UAV images. The identification of weeds in crops is a challenging task …

Agriculture is an important source of greenhouse gas emissions. It is one of the economic
sectors that impacts both directly and indirectly towards climate change which contributes to …

Predicting crop yields is a critical issue in agricultural production optimization and
intensification research. Accurate foresights of natural circumstances a year in advance can …

Weeds are among the significant factors that could harm crop yield by invading crops and
smother pastures, and significantly decrease the quality of the harvested crops. Herbicides …

Hawkweeds (Pilosella spp.) have become a severe and rapidly invading weed in pasture
lands and forest meadows of New Zealand. Detection of hawkweed infestations is essential …

Smart farming has the potential to overcome the challenge of 2050 to feed 10 billion people.
Both artificial intelligence (AI) and the internet of things (IoT) have become critical …

Precision agriculture and smart farming have received significant attention due to the
advancements made in remote sensing technology to support agricultural efficiency. In large …