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 …

Precision Agriculture (PA) promises to boost crop productivity while reducing agricultural
costs and environmental footprints, and therefore is attracting ever-increasing interests in …

The agriculture industry is undergoing a rapid digital transformation and is growing powerful
by the pillars of cutting-edge approaches like artificial intelligence and allied technologies …

The demand for intelligent agriculture is increasing due to the continuous impact of world
food and environmental crises. Focusing on fruit detection, with the rapid development of …

Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …

Modern agriculture and food production systems are facing increasing pressures from
climate change, land and water availability, and, more recently, a pandemic. These factors …

Investment in technological research is imperative to stimulate the development of
sustainable solutions for the agricultural sector. Advances in Internet of Things, sensors and …

Timely and reliable information about crop management, production, and yield is considered
of great utility by stakeholders (eg, national and international authorities, farmers …

Accurate count of fruits is important for producers to make adequate decisions in production
management. Although some algorithms based on machine vision have been developed to …

In recent years unmanned aerial vehicles (UAVs) have emerged as a popular and cost-
effective technology to capture high spatial and temporal resolution remote sensing (RS) …