Cereal plant heads must be identified precisely and effectively in a range of agricultural
applications, including yield estimation, disease detection, and breeding. Traditional …

During the last decades, researchers have developed novel computing methods to help
viticulturists solve their problems, primarily those linked to yield estimation of their crops …

Deep-learning-based object detection algorithms have significantly improved the
performance of wheat spike detection. However, UAV images crowned with small-sized …

For remote sensing object detection, fusing the optimal feature information automatically and
overcoming the sensitivity to adapt multi-scale objects remains a significant challenge for the …

In wheat breeding, spike number is a key indicator for evaluating wheat yield, and the timely
and accurate acquisition of wheat spike number is of great practical significance for yield …

Visual object detection is an important component in several applications of automated
agriculture. In this paper we consider how to properly apply modern deep networks for …

Deep Learning (DL) has emerged as a pivotal modelling tool in various domains because of
its proficiency in learning distributed representations. Numerous DL algorithms have …

Wheat spike detection has important research significance for production estimation and
crop field management. With the development of deep learning-based algorithms …

Traditional lettuce counting relies heavily on manual labor, which is laborious and time-
consuming. In this study, a simple and efficient method for localization and counting lettuce …

Optical sensors have been widely installed on unmanned aerial vehicle (UAV) to capture
images of all kinds of crops in recent years. The economic and effective way can greatly …