In recent years, deep learning (DL) has been the most popular computational approach in
the field of machine learning (ML), achieving exceptional results on a variety of complex …

The field of machine learning (ML) is sufficiently young that it is still expanding at an
accelerating pace, lying at the crossroads of computer science and statistics, and at the core …

The idea of environment friendly and affordable renewable energy resources has prompted
the industry to focus on the set up of biorefineries for sustainable bioeconomy …

Agricultural Big Data is a set of technologies that allows responding to the challenges of the
new data era. In conjunction with machine learning, farmers can use data to address …

The agricultural sector faces various challenges and barriers in transitioning from linear
resource consumption to a circular economy. Based on the literature, four key areas of …

Food security, land degradation, climate change, and a growing population are
interconnected challenges and key issues for sustainable agriculture. In this context, the …

A low power wireless sensor network based on LoRaWAN protocol was designed with a
focus on the IoT low-cost Precision Agriculture applications, such as greenhouse sensing …

Simple Summary In a pig farm, it is challenging for the farm caretaker to monitor the health
and well-being status of all animals in a continuous manner throughout the day. Automated …

Precision agriculture represents the new age of conventional agriculture. This is made
possible by the advancement of various modern technologies such as the internet of things …

Artificial intelligence and machine learning have been increasingly applied for prediction in
agricultural science. However, many models are typically black boxes, meaning we cannot …