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 …

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 …

Plant diseases often reduce crop yield and product quality; therefore, plant disease
diagnosis plays a vital role in farmers' management decisions. Visual crop inspections by …

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 …

Alternative non-chemical or chemical-reduced weed control tactics are critical for future
integrated weed management, especially for herbicide-resistant weeds. Through weed …

Digital Precision Agriculture (DPA) is a comprehensive approach to agronomic management
that utilizes advanced technologies, such as sensor data analysis and automation, to …

Background and aim: Machine learning methods are examined by many researchers to
identify weeds in crop images captured by drones. However, metaheuristic optimization is …

Weeds are one of the main hazards affecting the yield and quality of rice. In farmland
ecosystem, weeds compete with rice for resources such as light, water, soil and space, and …

Abstract Machine learning and deep learning are subsets of Artificial Intelligence that have
revolutionized object detection and classification in images or videos. This technology plays …

Recent advances in Information and Communication Technologies have a significant impact
on all sectors of the economy worldwide. Digital Agriculture appeared as a consequence of …