Research on a rapid detection method for seed breakage rate
When the rotary tillage seeder is popularized and appraised,it is necessary to measure the damage rate of seeds manually.In order to improve the detection efficiency,this paper takes wheat seeds as an example to study the rapid detection method of seed breakage rate.The automatic detection platform of seed breakage rate was designed,which could collect 50 g wheat seed images at one time.Based on image processing technology and machine learning methods,13 shape features and 8 texture features of wheat seed images were extracted,and a feature-based seed damage recognition model was established.The relationship between the identified damaged seed images and seed quality was studied,and an image-based broken seed quality prediction model was established,and the rapid detection of wheat seed damage rate was realized according to the requirements of the identification outline.In this study,the seed breakage rate of four wheat varieties as"Danyang 001""A888""Taizhou 014"and"Wuxi 004"in Jiangsu Province was experimentally tested,and each variety was sampled and measured for three times.The experimental results showed that the average relative errors of the automatic detection of seed breakage rate of the four wheat varieties were 0.08%,0.07%,0.06%and 0.08%,respectively,and the relative root mean square error of the detection was 0.08%,and the average detection time was 5.216 s.In this study,the automatic and rapid detection of wheat seed damage rate was realized,which saved the time of agricultural machinery identification and promoted the standardization and intelligence of agricultural machinery identification process.
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