Convolutional neural network (CNN)-based deep learning (DL) is a powerful, recently
developed image classification approach. With origins in the computer vision and image …

Crop counting is a crucial step in crop yield estimation. By counting, crop growth status can
be accurately detected and adjusted, improving crop yield and quality. In recent years, with …

Unmanned aerial vehicles (UAVs) equipped with lightweight sensors, such as RGB cameras
and LiDAR, have significant potential in precision agriculture, including object detection …

Detecting and counting on road vehicles is a key task in intelligent transport management
and surveillance systems. The applicability lies both in urban and highway traffic monitoring …

Accurate detection and counting of unopened cotton bolls at the early stage of cotton
maturation is an effective way to develop crop load management and harvesting strategies …

Convolutional neural network (CNN)-based deep learning (DL) has a wide variety of
applications in the geospatial and remote sensing (RS) sciences, and consequently has …

The advancement of unmanned aerial vehicles (UAVs) offers precise and accurate spectral
and spatial information about crops and plays a pivotal role in precision agriculture. This …

Plant stand count helps in estimating the yield and evaluating the planter's efficiency and
seed quality. Traditional methods of counting the plants by manual measurement are time …

Counting crop seedlings is a time-demanding activity involved in diverse agricultural
practices like plant cultivating, experimental trials, plant breeding procedures, and weed …

Corn is an important part of the Mexican diet. The crop requires constant monitoring to
ensure production. For this, plant density is often used as an indicator of crop yield, since …