Computer vision (CV) combined with a deep convolutional neural network (CNN) has
emerged as a reliable analytical method to effectively characterize and quantify high …

The accumulation of large datasets and increasing data availability have led to the
emergence of data-driven paleontological studies, which reveal an unprecedented picture of …

Weed detection in crops is a new frontier of precision agriculture, which will enable the
distinction between desirable and undesirable plants. Accurate and efficient weed detection …

In precision farming, weed detection is required for precise weedicide application, and the
detection of tobacco crops is necessary for pesticide application on tobacco leaves …

Intelligent farm robots empowered by proper vision algorithms are the new agricultural
machinery that eases weed control with speed and accuracy. Based on the farmland …

Weed and crop segmentation is becoming an increasingly integral part of precision farming
that leverages the current computer vision and deep learning technologies. Research has …

The number of leaves in maize seedlings is an essential indicator of their growth rate and
status. However, manual counting of seedlings is inefficient and limits the scope of the …

This paper focuses on the problems of omission, misclassification, and inter-adhesion due to
overly dense distribution, intraclass diversity, and interclass variability when extracting …

The rapid expansion of the world's population has resulted in an increased demand for
agricultural products which necessitates the need to improve crop yields. To enhance crop …

Introduction Development of weed and crop detection algorithms provides theoretical
support for weed control and becomes an effective tool for the site-specific weed …