Objective：A tomato weight detection method based on image processing was established to realize non-contact tomato weight detection. Methods：The binary image of tomato was obtained through image processing. The geometric features of tomato were extracted by pixel statistics and minimum circumscribed rectangle method, and correlation analysis was made between the characteristics and the real value of tomato weight, then the regression model of tomato weight detection with geometric features as parameters was established. Results：Compared with the real size of tomato, the measurement error of transverse and longitudinal diameter of Tomato by minimum external rectangle method was less than 3%. In addition to fruit shape index, other geometric characteristics were linearly correlated with tomato fruit weight, and the correlation between positive characteristics and fruit weight was more significant. Three types of 20 models were established for prediction and evaluation. The multiple regression model with the parameters of tomato front projection area and perimeter, projection area of a side image and tomato transverse diameter had the highest accuracy, the regression coefficient was 0.962, the average relative error of detection value was 0.673%, and the average absolute error was 1.425 g. Conclusion：The model is suitable for the weight detection of tomatoes and other fruits or articles with similar axisymmetric shape characteristics.
Ting-ting, HE; Zhi-wei, LI; Xin, ZHANG; Zhong-li-li, ZHANG; Xue-peng, XIAO; and Jing, DONG
"Tomato weight prediction based on image processing,"
Food and Machinery: Vol. 38:
10, Article 4.
Available at: https://www.ifoodmm.cn/journal/vol38/iss10/4
 杨再雄,吴恋,左建,等.基于人工智能的农产水果分级检测技术综述[J].科技创新与应用,2021,11（22）:41-43.YANG Z X,WU L,ZUO J,et al.A review of agricultural fruit classification detection technology based on artificial intelligence[J].Technology Innovation and Application,2021,11（22）:41-43.
 王业琴.计算机视觉鸭蛋重量检测方法研究[J].安徽农业科学,2011,39（7）:4 259-4 261.WANG Y Q.Study on duckʼs egg weight detection methods based on computer vision[J].Journal of Anhui Agricultural Sciences,2011,39（7）:4 259-4 261.
 郝敏.基于机器视觉的马铃薯外部品质检测技术研究[D].呼和浩特:内蒙古农业大学,2009:28-36.HAO M.Study on potato external chwacter detection technology based on machine vtsion[D].Hohhot:Inner Mongolia Agricultural University,2009:28-36.
 孔彦龙,高晓阳,李红玲,等.基于机器视觉的马铃薯质量和形状分选方法[J].农业工程学报,2012,28（17）:143-148.KONG Y L,GAO X Y,LI H L,et al.Potato grading method of mass and shapes based on machine vision[J].Transactions of the Chinese Society of Agricultural Engineering,2012,28（17）:143-148.
 王红军,熊俊涛,黎邹邹,等.基于机器视觉图像特征参数的马铃薯质量和形状分级方法[J].农业工程学报,2016,32（8）:272-277.WANG H J,XIONG J T,LI Z Z,et al.Potato grading method of weight and shape based on imaging characteristics parameters in machine vision system[J].Transactions of the Chinese Society of Agricultural Engineering,2016,32（8）:272-277.
 HUYNH T,TRAN L,DAO S.Real-Time size and mass estimation of slender axi-symmetric fruit/vegetable using a single top view image[J].Sensors,2020,20（18）:5 406.
 张展硕,刘苗苗,陆雯沁,等.基于图像传感技术的娃娃菜外观品质检测[J].食品安全质量检测学报,2021,12（4）:1 374-1 379.ZHANG Z S,LIU M M,LU W Q,et al.Detection of external quality for baby cabbage by image sensing technology[J].Journal of Food Safety & Quality,2021,12（4）:1 374-1 379.
 赵军,田海韬.利用机器视觉检测马铃薯外部品质方法综述[J].图学学报,2017,38（3）:382-387.ZHAO J,TIAN H T.The applications of potato external quality detection using machine vision[J].Journal of Graphics,2017,38（3）:382-387.
 ASHTIANI S,ROHANI A,AGHKHANI M H.Soft computing-based method for estimation of almond kernel mass from its shell features[J].Scientia Horticulturae,2020,262:21-26.
 DEMIR B,ESKI K,GÜRBÜZ F,et al.Prediction of walnut mass based on physical attributes by artificial neural network（ANN）[J].Erwerbs-Obstbau,2020,62（1）:47-56.
 NYALALA I,OKINDA C,NYALALA L,et al.Tomato volume and mass estimation using computer vision and machine learning algorithms:Cherry tomato model[J].Journal of Food Engineering,2019,263:288-298.
 何微,牛智有,李晓金.基于外部特征信息的番茄果实质量预测模型[J].华中农业大学学报,2013,32（6）:144-148.HE W,NIU Z Y,LI X J.Prediction model of tomato-mass based on external characteristic information[J].Journal of Huazhong Agricultural University,2013,32（6）:144-148.
 SUSOVAN J,RANJAN P,BIJAN S.A De novo approach for automatic volume and mass estimation of fruits and vegetables[J].Optik,2020,200:163 441-163 443.
 LEE J,NAZKI H,BAEK J,et al.Artificial intelligence approach for tomato detection and mass estimation in precision agriculture[J].Sustainability,2020,12（21）:9 138.
 孙亚东,梁燕,吴江敏,等.番茄数量性状与番茄红素相关性分析[J].中国蔬菜,2010（6）:74-76.SUN Y D,LIANG Y,WU J M,et al.Correlation analysis on quantitative traits of tomato germplasm resources[J].China Vegetables,2010（6）:74-76.
 王溯源.基于机器视觉的马铃薯品质检测方法研究[D].徐州:中国矿业大学,2020:41-49.WANG S Y.Research on potato quality detection method based on machine vision[D].Xuzhou:China University of Mining and Technology,2020:41-49.
 王勉.基于机器视觉的马铃薯品质分级系统研究[D].徐州:中国矿业大学,2019:30-35.WANG M.Research on potato quality classification system based on machine vision[D].Xuzhou:China University of Mining and Technology,2019:30-35.
 朱晓林,魏小红,冯悦,等.基于多元统计分析的番茄性状研究[J].江苏农业科学,2020,48（7）:174-181.ZHU X L,WEI X H,FENG Y,et al.Research on tomato traits based on multivariate statistical analysis[J].Jiangsu Agricultural Sciences,2020,48（7）:174-181.
 刘忠超,范灵燕,盖晓华.基于机器视觉的苹果重量检测研究[J].江苏农业科学,2021,49（21）:201-205.LIU Z C,FAN L Y,GAI X H.Research on apple weight detection based on machine vision[J].Jiangsu Agricultural Sciences,2021,49（21）:201-205.