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At present, people’s living standard and consumption level are increasing day by day, the demand for edible oil crops is increasing. As the main source of oil crops, rape needs to use more accurate management and cultivation techniques, change backward technology, increase overall yield, and ensure the supply of oil crops. By means of artificial intelligence, the spectral segments of plants were analyzed to determine the species of light and the nutrients produced by plants, the monitoring of development and the prediction of crop yield. According to the spectral information of plant absorption and reflection, the chlorophyll level contained in plants can provide data support for rape planting and management, and monitor the real-time status of its growth and development. Mastering the nutrition and demand of rape in the whole process of growth and reproduction is very beneficial to the management and yield improvement of rape. And can adjust the cultivation method in time, play the advantage of hyperspectral technology.

In the past few years, multi-core processors incorporating four, six, eight, or more cores on a single die have become ubiquitous. Those cores, having their own private caches, often share a higher level cache memory, which leads to compete among different tasks. This can seriously affect the average performance of multi-core systems as the probability of cache hit could be lowered. In realizing this, we study the problem of scheduling bag-of-tasks (BoT) applications with shared cache constraint on multi-core systems. We first use cache space isolation techniques to divide shared caches into partitions. Then, we give a motivational example and outline the shared cache aware scheduling problem of multi-core systems. Finally, to provide an optimum solution for this problem, we propose a heuristic shared cache contention aware scheduling (SCAS) algorithm on multi-core systems. Our extensive simulation performance evaluation study clearly demonstrate that our proposed SCAS algorithm outperforms the existing traditional scheduling algorithm Min-min and the modified algorithm MSCAS in terms of schedule length and average response time.

Effective task scheduling is essential for obtaining high performance in heterogeneous computing systems (HCS). However, finding an effective task schedule in HCS, requires the consideration of the heterogeneity of computation and communication. To solve this problem, we present a list scheduling algorithm, called Heterogeneous Earliest Finish with Duplicator (HEFD). As task priority is a key attribute for list scheduling algorithm, this paper presents a new approach for computing their priority which considers the performance difference in target HCS using variance. Another novel idea proposed in this paper is to try to duplicate all parent tasks and get an optimal scheduling solution. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithm HEFD significantly surpasses other three well-known algorithms.

The plumpness is an important index of crop seed. However, traditional measurements are time-consuming and labor intensive. The computer vision technology, which may offer more efficient and non-destructive methods for measurement, has recently appeared. But it is very difficult to accurately estimate the plumpness of single seed by the ratio between area and perimeter because of the diversity of rapeseed seed’s size. This paper focused on rapeseed seed plumpness recognition and quantification, based on computer vision. A new method, the coefficient of variation of radius (CVR), was used to estimate seed plumpness. The recognition and quantification model for plumpness in single seed were established by using the fuzzy C-means (FCM) clustering and fuzzy math method. The plumpness of the seed is full if plumpness is greater than or equal to 0.6. Some correlative index are calculated and analyzed to verify the validity of this method. The tests show that there is no correlation between plumpness or plumpness ratio, and 1000-seed weight or equivalence diameter. But there are significantly partial correlation between plumpness or plumpness ratio, 1000-seed weight and equivalence diameter. Finally, plumpness ratio index is significantly different among the 12 varieties rapeseed was determined. With the mean value of plumpness ratio of rapeseed variety, the plumpness degree was plotted 10 grades. The results show that the application of computer vision technology is significantly valid for quantitative determination of plumpness in rapeseed seed.

The implementation of new methods for fast classification of seeds is of major technical importance in the large-scale investigation of seeds identification. A few indices with biological significance were used to identify rapeseed type and variety. The plumpness and the plumpness ratio of rapeseed were extracted by using the variation coefficient of radius for fourteen rapeseed varieties at Chinese five locations. The equivalence diameter was extracted by using reference calibration method. The major color ratio and color saturation were extracted by using nine color HSV model and major color method. ANN-BP models were established for rapeseed varieties classification. The equivalent diameter and plumpness ratio were used to identify two classes' rapeseed, B.compestris and B. napus L. with accuracy of 100%. The equivalent diameter, plumpness ratio and color saturation were used to identify six rapeseed varieties, with accuracy of 92.06% to 92.37%,, with average of 92.15%. By machine vision, it is feasible to identify rapeseed type and variety with a few biological indices.

To reduce the error and faster classification by mechanizing in classifying the potato shape and size through machine vision using the extraction of characters procedure to identify the size, and using the shape detection procedure to identify the shape. Test results in potato size detection revealed 40/191 = 0.210mm/pixel as length scale or calibration factor (40/M) where 40 is the table tennis ball size (40mm) and 191 as image pixels table tennis (M); measurement results revealed that between the algorithm results and the manual measurements, the absolute error was