Recognition Method of Corn and Rice Crop Growth State Based on Computer Image Processing Technology

Abstract

The agriculture field is one of the most important fields where computational techniques play an imperative role for decision-making whether it is the automation of watering of plants, controlling of humidity levels, and detection of plant diseases and growth of plants. There are problems in the conventional methods where newer computational techniques and image processing methods are not used to keep track of growth of plants. The traditional image capturing and processing models have problems of large image segmentation error, excessive feature extraction time, and poor recognition output. In order to overcome the problems in the traditional plant growth methods based on image processing automations, computer image processing with computational method has been proposed to analyze the plant growth by utilizing state recognition method for corn and rice crops. An image acquisition platform is established on the basis of CMOS image sensor for crop image acquisition. The binary processing is performed, and then the images are segmented to reduce error of segmentation results in the traditional methods. To extract image features of corn and rice crops, convolution neural network (CNN) with newer architecture is used. According to contour information of images, the block wavelet transform method is used for feature adaptive matching. The binary tree structure is used to divide the growth period of corn and rice crops. The fuzzy mathematical model is also devised to identify the characteristics of crops in different growth periods and to complete the identification of growth state. Experimental results show that the proposed method effectively improves problems of traditional methods with better image recognition effect and reduces the time of feature recognition. The time to extract features by the proposed method is 1.4 seconds, whereas comparative methods such as random forest (RF) take 3.8 s and other traditional techniques take 4.9 s. Segmentation result error of the recognition method is also reduced significantly.