One of the main challenges for the implementation of artificial intelligence (AI) in agriculture
includes the low replicability and the corresponding difficulty in systematic data gathering, as …

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 …

Agriculture 5.0 refers to the next phase of agricultural development, building upon the
previous digital revolution in the agrarian sector and aiming to transform the agricultural …

The information that crops offer is turned into profitable decisions only when efficiently
managed. Current advances in data management are making Smart Farming grow …

In the recent years, robotic systems became more advanced and more accessible. This has
led to their slow, but stable integration and use in different processes and applications …

Agriculture is by its nature a complicated scientific field, related to a wide range of expertise,
skills, methods and processes which can be effectively supported by computerized systems …

The aim of the proposed work is to review the various AI techniques (fuzzy logic (FL),
artificial neural network (ANN), genetic algorithm (GA), particle swarm optimization (PSO) …

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 …

Due to the increasing global population and the growing demand for food worldwide as well
as changes in weather conditions and the availability of water, artificial intelligence (AI) such …

This paper presents the state of application of Precision Agricultural enabling Technology
(PAT) in Swiss farms as an example for small-scale, highly mechanised Central European …