The Study On Rice Growth Monitoring Based On Image Processing

It is of great significance to control the growth factors according to the rice growth conditions information for saving energy and making high-yielding. The traditional manual observation is inefficient and the observed result can be easily effected by subjective factor. Remote sensing monitoring and remote monitoring are limited by the monitoring scope and cannot be used for real-time monitoring and small regions monitoring on individual farms. So this paper proposes method of rice growth monitoring based on image processing.The rice images were got by digital camera and the corresponding image processing methods were proposed from the experiments in Nanjing Agricultural University farm in this paper. The feasibility of judging the rice growth conditions based on single rice plant morphological parameters of the images was proved. The relationship between the leaf area and leaf area index in several growth periods and the relationship between the leaf area and leaf area index obtained by measurements was also studied. The main contents are as follows:This paper proposes the using of local gray linear transform for the rice image enhancement since the leaf tip of rice plants images were difficult to be segmented; A better method was used to eliminate the burr of the rice images appeared in thinning images; In addition, by the comparison of effectiveness of denoising of rice image for several median filtering filter window using median filter method, the cross-median filter was chosen to denoise during the image processing. It is proposed that the using color characteristics.The plant height and leaf basic angle are taken as the standard parameters to evaluate the rice growth conditions because that the plant height, leaf tip distance and leaf basic angle are the main factors to describe rice growth conditions. And rice growth status are also estimated by the leaf tip distance average datum.The leaf area index is the main factor to describe rice growth conditions. It is found that it's feasible to obtain the rice growth condition information from leaf area index in several growth periods. There was little difference between the growth parameters obtained from the leaf area index model and the verity value. So, it can conclude that it's feasible to obtain rice growth characteristics using image processing.