A5064694358- Smart Agriculture and AI
- Remote Sensing in Agriculture
- Spectroscopy and Chemometric Analyses
- Leaf Properties and Growth Measurement
- Greenhouse Technology and Climate Control
- Agriculture and Biological Studies
- Botany and Plant Ecology Studies
- Agriculture, Plant Science, Crop Management
- Agroforestry and silvopastoral systems
- Remote-Sensing Image Classification
- Date Palm Research Studies
- Advanced Measurement and Detection Methods
- Soil and Environmental Studies
- Remote Sensing and LiDAR Applications
- Plant and soil sciences
- Online Learning and Analytics
- Ruminant Nutrition and Digestive Physiology
- Agricultural Productivity and Crop Improvement
- Soil Science and Environmental Management
Universidad Complutense de Madrid
2012-2018
Software (Spain)
2016
This paper proposes a new method, oriented to image real-time processing, for identifying crop rows in maize fields the images. The vision system is designed be installed onboard mobile agricultural vehicle, that is, submitted gyros, vibrations, and undesired movements. images are captured under perspective, being affected by above effects. processing consists of two main processes: segmentation row detection. first one applies threshold separate green plants or pixels (crops weeds) from...
Machine vision systems are becoming increasingly common onboard agricultural vehicles (autonomous and non-autonomous) for different tasks. This paper provides guidelines selecting machine-vision optimum performance, considering the adverse conditions on these outdoor environments with high variability illumination, irregular terrain or plant growth states, among others. In this regard, three main topics have been conveniently addressed best selection: (a) spectral bands (visible infrared);...
In Precision Agriculture, images coming from camera-based sensors are commonly used for weed identification and crop line detection, either to apply specific treatments or vehicle guidance purposes. Accuracy of detection is an important issue be addressed in image processing. There two main types parameters affecting the accuracy images, namely: (a) extrinsic, related sensor's positioning tractor; (b) intrinsic, sensor specifications, such as CCD resolution, focal length iris aperture, among...
In this article, a new strategy to detect green plants in maize crop has been developed. This is based on five main stages: segmentation, reduction of dimensionality PCA, Otsu thresholding, threshold combination and final thresholding. Images are taken RGB color model tr ansformed grayscale image using basic vegetation index PCA method reduce the data. After step, thresholds by designed order generate binarize previous image. The performance proposed validated with an set compared other...