Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Simulated Annealing
Genetic Algorithms and Simulated Annealing
Segmentation and description of natural outdoor scenes
Image and Vision Computing
Crop/weed discrimination in perspective agronomic images
Computers and Electronics in Agriculture
A new vision-based approach to differential spraying in precision agriculture
Computers and Electronics in Agriculture
Selection of the most efficient wavelength bands for discriminating weeds from crop
Computers and Electronics in Agriculture
Verification of color vegetation indices for automated crop imaging applications
Computers and Electronics in Agriculture
Large-scale investigation of weed seed identification by machine vision
Computers and Electronics in Agriculture
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Original paper: Real-time image processing for crop/weed discrimination in maize fields
Computers and Electronics in Agriculture
Towards machine vision based site-specific weed management in cereals
Computers and Electronics in Agriculture
Review: Plant species identification using digital morphometrics: A review
Expert Systems with Applications: An International Journal
Computers and Electronics in Agriculture
| Hi-index | 0.00 |
This work presents several developed computer-vision-based methods for the estimation of percentages of weed, crop and soil present in an image showing a region of interest of the crop field. The visual detection of weed, crop and soil is an arduous task due to physical similarities between weeds and crop and to the natural and therefore complex environments (with non-controlled illumination) encountered. The image processing was divided in three different stages at which each different agricultural element is extracted: (1) segmentation of vegetation against non-vegetation (soil), (2) crop row elimination (crop) and (3) weed extraction (weed). For each stage, different and interchangeable methods are proposed, each one using a series of input parameters which value can be changed for further refining the processing. A genetic algorithm was then used to find the best value of parameters and method combination for different sets of images. The whole system was tested on several images from different years and fields, resulting in an average correlation coefficient with real data (bio-mass) of 84%, with up to 96% correlation using the best methods on winter cereal images and of up to 84% on maize images. Moreover, the method's low computational complexity leads to the possibility, as future work, of adapting them to real-time processing.