Convolution neural network in precision agriculture for plant image recognition and classification

Agriculture is essential to the continued existence of human life as they directly depend on it for the production of food. The exponential rise in population calls for a rapid increase in food with the application of technology to reduce the laborious work and maximize production. Precision Agriculture is thought to be solution required to achieve the production rate required. There has been a significant improvement in the area of image processing and data processing which has being a major challenge previously in the practice of precision Agriculture. A database of images is collected through remote sensing, analyzed and a model is developed to determine the right treatment plans for different crop types and different regions. Features of images from vegetation's need to be extracted, classified, segmented and finally fed into the model. Different techniques have been applied to the processes from the use of the neural network, support vector machine, fuzzy logic approach and recently, the most effective approach generating fast and excellent results using the deep learning approach of convolution neural network for image classifications. Deep Convolution neural network is used in plant images recognition and classification to optimize production on a maize plantation. The experimental results on the developed model yielded results with an average accuracy of 99.58%.