Xiaodong Bai

Crop feature extraction from images with probabilistic superpixel Markov random field

A specularity-invariant crop extraction method is first put forward.Our method is resistance to the specularity reflection utilizing markov random field.Our method also manifests the ability to identify crop from the cover of shadow.Our method achieves the highest performances with the lowest variations.This method can be utilized in practical automatic observation system. In the process of agriculture automation, mechanization and intelligentialization, image segmentation for crop extraction plays a crucial role. However, the performance of crop segmentation is closely related to the quality of the captured image, which is easily affected by the variability, randomness, and complexity of the natural illumination. The previously proposed crop extraction approaches produce inaccurate segmentation under natural illumination when highlight occurs. And specularity removal techniques are still hard to improve the crop extraction performance, because of the flaw of their assumption and the high requirement of the experimental configuration. In this paper, we propose a novel crop extraction method resistant to the strong illumination by using probabilistic superpixel Markov random field. Our method is based on the assumption that color changes gradually between highlight areas and its neighboring non-highlight areas and the same holds true for the other regions. This priori knowledge is embedded into the MRF-MAP framework by modeling the local and mutual evidences of nodes. Besides, superpixel and Fisher linear discriminant are utilized to construct the probabilistic superpixel patches. Loopy belief propagation algorithm is adopted in the optimization step. And the label for the crop segmentation is provided in the final iteration result. We also compare our method to the other state-of-the-art approaches. The results demonstrate that our method is resistant to the strong illumination and can be applied to generic species. Moreover, our approach is also capable of extracting the crop from the shadow regions. Statistics from comparative experiments manifest that our crop segmentation method yields the highest mean value of 92.29% with the lowest standard deviation of 4.65%, which can meet the requirement of practical uses in our agriculture automatic vision system.

An automatic detection method to the field wheat based on image processing

The automatic observation of the field crop attracts more and more attention recently. The use of image processing technology instead of the existing manual observation method can observe timely and manage consistently. It is the basis that extracting the wheat from the field wheat images. In order to improve accuracy of the wheat segmentation, a novel two-stage wheat image segmentation method is proposed. Training stage adjusts several key thresholds which will be used in segmentation stage to achieve the best segmentation results, and counts these thresholds. Segmentation stage compares the different values of color index to determine which class of each pixel is. To verify the superiority of the proposed algorithm, we compared our method with other crop segmentation methods. Experiment results shows that the proposed method has the best performance.

Automatic Measurement of Crops Canopy Height Based on Monocular Vision

Computer vision technology has been increasingly used for automatically observing crop growth state, but as one of the key parameters in the field of agro-meteorological observation, crop canopy height is still measured manually in the actual observation process up to now. In order to automatically measure the height based on the forward-and-downward-looking image in the existing monocular vision observation system, a novel method is proposed, that is, to measure the canopy height indirectly by the solving algorithm for the actual height of the vertical objects (SAAH) with the help of the intelligent sensor device. The experiment results verified the feasibility and validity of our method, and that the method could meet the actual observation demand.

Color image segmentation using watershed and Nyström method based spectral clustering

Color image segmentation draws a lot of attention recently. In order to improve efficiency of spectral clustering in color image segmentation, a novel two-stage color image segmentation method is proposed. In the first stage, we use vector gradient approach to detect color image gradient information, and watershed transformation to get the pre-segmentation result. In the second stage, Nyström extension based spectral clustering is used to get the final result. To verify the proposed algorithm, it is applied to color images from the Berkeley Segmentation Dataset. Experiments show our method can bring promising results and reduce the runtime significantly.

An image-based approach for automatic detecting five true-leaves stage of cotton

Cotton, as one of the four major economic crops, is of great significance to the development of the national economy. Monitoring cotton growth status by automatic image-based detection makes sense due to its low-cost, low-labor and the capability of continuous observations. However, little research has been done to improve close observation of different growth stages of field crops using digital cameras. Therefore, algorithms proposed by us were developed to detect the growth information and predict the starting date of cotton automatically. In this paper, we introduce an approach for automatic detecting five true-leaves stage, which is a critical growth stage of cotton. On account of the drawbacks caused by illumination and the complex background, we cannot use the global coverage as the unique standard of judgment. Consequently, we propose a new method to determine the five true-leaves stage through detecting the node number between the main stem and the side stems, based on the agricultural meteorological observation specification. The error of the results between the predicted starting date with the proposed algorithm and artificial observations is restricted to no more than one day.

Morphology based field rice density detection from rice transplant stage to rice jointing stage

Rice yield estimation is an important aspect in the agriculture research field. For the rice yield estimation, rice density is one of its useful factors. In this paper, we propose a new method to automatically detect the rice density from the rice transplanting stage to rice jointing stage. It devotes to detect rice planting density by image low-level features of the rice image sequences taken in the fields. Moreover, a rice jointing stage automatic detection method is proposed so as to terminate the rice density detection algorithm. The validities of the proposed rice density detection method and the rice jointing stage automatic detection method are proved in the experiment.

Specularity-invariant crop extraction with probabilistic super-pixel markov random field

In this paper, we propose a specularity-invariant crop extraction method using probabilistic super-pixel markov random field (MRF). Our method is based on the underlying rule that intensity change gradually between highlight areas and its neighboring non-highlight areas. This prior knowledge is embedded into the MRF-MAP framework by modeling the local and mutual evidences of nodes. The marginal probability of each node in the label field is then iteratively computed by Belief Propagation algorithm which leads to the final solution. Comparing experimental results show that our method outperforms the other commonly used extraction methods in yielding highest performance with the lowest standard deviation.