Application of a Novel PNN Evaluation Algorithm to a Greenhouse Monitoring System

Abstract

Evaluating the quality of environments within greenhouses has been an important aspect of greenhouse agriculture. However, the existing greenhouse monitoring systems are generally unable to evaluate the quality of greenhouse environments, and at present, it is evaluated manually, which leads to the failure of timely and effective evaluation. Therefore, how to evaluate the greenhouse environment quality accurately, quickly, and automatically is an urgent issue to be tackled. Inspired by the optimization algorithm, clustering algorithm, and probabilistic neural network (PNN), this article proposes the application of a novel PNN evaluation algorithm to a decentralized greenhouse monitoring system to evaluate the environment quality in real time. First, the original training samples are clustered by an improved K-means clustering algorithm (called K-means- α), and the training samples near the cluster centroids are used as new ones to establish the PNN structure. Then, the particle swarm optimization (PSO) algorithm (whereby the fitness function is defined as the PNN's classification error rate on the test samples) is used to iteratively optimize smoothing factors adopted by different classes of pattern units in the PNN. The optimized PNN is finally obtained (called α-PSO-M-PNN). The experimental results demonstrate that compared with conventional PNN-based algorithms, the optimized PNN algorithm has the advantages of a simpler network structure, higher classification accuracy, and requiring fewer training samples. In addition, it is more suitable for the microprocessor-based monitoring system because its computational and storage requirements are within the limits of the microprocessor.