Journal of Computer Science


Ahmed M. Tobal and Sahar A. Mokhtar

DOI : 10.3844/jcssp.2014.1355.1361

Journal of Computer Science

Volume 10, Issue 8

Pages 1355-1361


In a world reached a population of six billion humans increasingly demand it for food, feed with a water shortage and the decline of agricultural land and the deterioration of the climate needs 1.5 billion hectares of agricultural land and in case of failure to combat pests needs about 4 billion hectares. Weeds represent 34% of the whole pests while insects, diseases and the deterioration of agricultural land present the remaining percentage. Weeds Identification has been one of the most interesting classification problems for Artificial Intelligence (AI) and image processing. The most common case is to identify weeds within the field as they reduce the productivity and harm the existing crops. Success in this area results in an increased productivity, profitability and at the same time decreases the cost of operation. On the other hand, when AI algorithms combined with appropriate imagery tools may present the right solution to the weed identification problem. In this study, we introduce an evolutionary artificial neural network to minimize the time of classification training and minimize the error through the optimization of the neuron parameters by means of a genetic algorithm. The genetic algorithm, with its global search capability, finds the optimum histogram vectors used for network training and target testing through a fitness measure that reflects the result accuracy and avoids the trial-and-error process of estimating the network inputs according to the histogram data.


© 2014 Ahmed M. Tobal and Sahar A. Mokhtar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.