The detectionof maize seeds and other crops is a key problem in the field of agricultural informatization.In this paper

to enhance detection and improve accuracy rate

we present a detection algorithm for maize seed on the basis of extreme learning machine (ELM) and multi-scale feature fusion. In the first part of this paper

the feature of maize seed is described as the combination of local features and global features. Local features can be described as a multi-scale histogram of oriented gradient

and global features can be described as the color feature of HSV. In the second part

ELM will be used as the detection algorithm against long training period and slow detection speed

which are the characteristics of traditional BP neural network and SVM. Furthermore

the detection model uses the parallel algorithm to significantly decrease the time used in the training of each classifier. Furthermore

the high resolution of the original image can cause long detection periods and consume a large amount of memory. To address this problem

we propose a local means of an image compression algorithm. Finally

considering that the sliding windows of centralized scanning can produce a problem in creating multiple overlapping windows capturing the same object

we propose a local window fusion algorithm based on fuzzy clustering to address this problem. The simulation results show that the method proposed is able to accurately detect maize seeds. The accuracy of detection of maize seeds can reach 97.66% with an error of less than 0.1%. Compared with traditional methods

the no damage method proposed in this paper can improve the speed and accuracy of detection and has no strict hardware requirements.