Crop extraction from the images captured in the field is a complex task. In this paper, a new crop segmentation method is presented based on a designed lightweight neural network which only has 5-layer. In the proposed method, the lightweight neural network is designed and constructed to deal with the crop color features in the normalized RGB and CIE L*a*b* color spaces to realized the accurate segmentation of crop images. To verify the performance of the proposed method, 120 rice images are utilized to compare the proposed method with four other famous approaches. Experiment demonstrates that our method is robust to the illumination variations in the field and performed better than other approaches. Experiment shows our method can be used to the task of crop segmentation accurately and efficiently.
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.
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.
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.
KEYWORDS: Image segmentation, Image processing, Image processing algorithms and systems, RGB color model, Meteorology, Medical imaging, Agriculture, Detection and tracking algorithms, Digital imaging, Digital cameras
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.
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