Journal of Chinese Agricultural Mechanization ›› 2025, Vol. 46 ›› Issue (7): 198-205.DOI: 10.13733/j.jcam.issn.2095-5553.2025.07.028
• Facilities Agriculture and Plant Protection Machinery Engineering • Previous Articles Next Articles
He Yushuang, Wang Zhuo, Xiao Jin, Tian Manzhou, Lü Chenghui, Zhang Junfeng
Online:
2025-07-15
Published:
2025-07-02
何雨霜,王琢,肖进,田满洲,吕程辉,张俊峰
基金资助:
CLC Number:
He Yushuang, Wang Zhuo, Xiao Jin, Tian Manzhou, Lü Chenghui, Zhang Junfeng. Research progress of fish intelligent feeding methods based on computer vision[J]. Journal of Chinese Agricultural Mechanization, 2025, 46(7): 198-205.
何雨霜, 王琢, 肖进, 田满洲, 吕程辉, 张俊峰. 基于计算机视觉的鱼类智能投喂方法研究进展[J]. 中国农机化学报, 2025, 46(7): 198-205.
[1] 李贤,刘鹰.水产养殖中鱼类福利学研究进展[J]. 渔业现代化, 2014, 41(1): 40-45. Li Xian, Liu Ying. Current research advances on fish welfare in aquaculture [J].Fishery Modernization, 2014, 41(1): 40-45. [2] Ruohonen K, Vielma J, Grove D J. Effects of feeding frequency on growth and food utilisation of rainbow trout (Oncorhynchus mykiss) fed low-fat herring or dry pellets [J]. Aquaculture, 1998, 165(1-2): 111-121. [3] 李笑天, 刘宝良, 费凡, 等. 投喂策略对水产动物生长生理及行为特征影响研究进展[J]. 渔业现代化, 2020, 47(2): 7-15. Li Xiaotian, Liu Baoliang, Fei Fan, et al. Advances in research on effects of feeding strategies on growth physiology and behavioral characteristics of aquatic animals [J]. Fishery Modernization, 2020, 47(2): 7-15. [4] 李琪, 刘鉴毅, 孙艳秋, 等. 投喂策略对多纹钱蝶鱼幼鱼生长的影响[J].海洋科学, 2022, 46(3): 93-102. Li Qi, Liu Jianyi, Sun Yanqiu, et al. Effects of feeding strategies on the growth of Selenotoca multifasciata [J]. Marine Sciences, 2022, 46(3): 93-102. [5] Zhang Y, Lu R, Qin C, et al. Precision nutritional regulation and aquaculture [J]. Aquaculture Reports, 2020(18): 100496. [6] 郑金存, 赵峰, 林勇, 等. 基于近红外深度图的游泳型鱼类摄食强度实时测量[J]. 上海海洋大学学报, 2021, 30(6): 1067-1078. Zheng Jincun, Zhao Feng, Lin Yong, et al. Evaluation of fish feeding intensity in aquaculture based on near-infrared depth image [J]. Journal of Shanghai Ocean University, 2021, 30(6): 1067-1078. [7] 陈雨琦, 冯德军, 桂福坤, 等. 采用机器视觉和傅里叶频谱特征的循环水养殖鱼类摄食状态判别[J]. 农业工程学报, 2021, 37(14): 155-162. Chen Yuqi, Feng Dejun, Gui Fukun, et al. Discrimination of the feeding status of recirculating aquaculture fish via machine vision and reflective corrugated Fourier spectrum [J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(14): 155-162. [8] 王吉祥. 基于嵌入式机器视觉的浮饵自动投放装置研制[D]. 镇江: 江苏大学, 2016. [9] 穆春华, 范良忠, 刘鹰. 基于计算机视觉的循环水养殖系统残饵识别研究[J]. 渔业现代化, 2015, 42(2): 33-37. Mu Chunhua, Fan Liangzhong, Liu Ying. Research on the residual feeds recognition of recirculating aquaculture systems based on computer vision [J]. Fishery Modernization, 2015, 42(2): 33-37. [10] Atoum Y, Srivastava S, Liu X. Automatic feeding control for dense aquaculture fish tanks [J]. IEEE Signal Processing Letters, 2015, 22(8):1089-1093. [11] 刘淑兰, 孙国祥, 李杰, 等. 投喂频率对大西洋鲑生长和生理指标的影响[J]. 水产科学, 2019, 38(3): 341-346. Liu Shulan, Sun Guoxiang, Li Jie, et al. Effects of feeding frequency on growth and some physiological indices in Atlantic salmon Salmo salar [J]. Fisheries Science, 2019, 38(3): 341-346. [12] Parsonage K D, Petrell R J. Accuracy of a machine-vision pellet detection system [J]. Aquacultural Engineering, 2003, 29(3-4): 109-123. [13] Li D, Xu L, Liu H. Detection of uneaten fish food pellets in underwater images for aquaculture [J]. Aquacultural Engineering, 2017, 78: 85-94. [14] 赵建, 朱松明, 叶章颖, 等. 循环水养殖游泳型鱼类摄食活动强度评估方法研究[J]. 农业机械学报, 2016, 47(8): 288-293. Zhao Jian, Zhu Songming, Ye Zhangying, et al. Assessing method for feeding activity of swimming fishes in RAS [J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(8): 288-293. [15] Ye Z, Zhao J, Han Z, et al. Behavioral characteristics and statistics-based imaging techniques in the assessment and optimization of tilapia feeding in a recirculating aquaculture system [J]. Transactions of the ASABE, 2016, 59(1): 345-355. [16] 唐宸, 徐立鸿, 刘世晶. 基于光流法的鱼群摄食状态细粒度分类算法[J]. 农业工程学报, 2021, 37(9):238-244. Tang Chen, Xu Lihong, Liu Shijing. Fine-grained classification algorithm of fish feeding state based on optical flow method [J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(9): 238-244. [17] 陈志鹏, 陈明. 基于光流法与图像纹理特征的鱼群摄食行为检测[J]. 南方农业学报, 2019, 50(5): 1141-1148. Chen Zhipeng, Chen Ming. Detection of shoal feeding behavior based on optical flow methods and image texture [J]. Journal of Southern Agriculture, 2019, 50(5): 1141-1148. [18] 黄志涛, 何佳, 宋协法. 基于鱼体运动特征和图像纹理特征的鱼类摄食行为识别与量化[J]. 中国海洋大学学报(自然科学版), 2022, 52(1): 32-41. Huang Zhitao, He Jia, Song Xiefa. Recognition and quantification of fish feeding behavior based on motion feature of fish body and image texture [J]. Periodical of Ocean University of China, 2022, 52(1): 32-41. [19] 刘丽, 匡纲要. 图像纹理特征提取方法综述[J]. 中国图象图形学报, 2009, 14(4): 622-635. Liu Li, Kuang Gangyao. Overview of image texture feature extraction methods [J]. Journal of Image and Graphics, 2009, 14(4): 622-635. [20] 余前帆.《计算机科学技术名词》(第三版)正式公布[J].中国科技术语, 2019, 21(2): 10. [21] Sadoul B, Mengues P E, Friggens N C, et al. A new method for measuring group behaviours of fish shoals from recorded videos taken in near aquaculture conditions [J]. Aquaculture, 2014, 430: 179-187. [22] 王勇平, 聂余满, 谢成军, 等. 基于机器视觉的养殖鱼群智能投饵系统设计与研究[J]. 仪表技术, 2015(1): 1-4. Wang Yongping, Nie Yuman, Xie Chengjun, et al. Design and research of intelligent feeding system for farmed fish based on machine vision [J]. Instrumentation Technology, 2015(1): 1-4. [23] 胡利永, 魏玉艳, 郑堤, 等. 基于机器视觉技术的智能投饵方法研究[J]. 热带海洋学报, 2015, 34(4): 90-95. Hu Liyong, Wei Yuyan, Zheng Di, et al. Research on intelligent bait casting method based on machine vision technology [J]. Journal of Tropical Oceanography, 2015, 34(4): 90-95. [24] 贾成功, 张学良, 陈俊华, 等. 基于鱼群摄食规律的投饵系统研究[J]. 机械工程师, 2017(8): 22-25, 28. Jia Chenggong, Zhang Xueliang, Chen Junhua, et al. Research on bait casting system based on feeding rule of fish [J]. Mechanical Engineer, 2017(8): 22-25, 28. [25] 陈彩文, 杜永贵, 周超, 等. 基于图像纹理特征的养殖鱼群摄食活动强度评估[J]. 农业工程学报, 2017, 33(5): 232-237. Chen Caiwen, Du Yonggui, Zhou Chao, et al. Evaluation of feeding activity of shoal based on image texture [J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(5): 232-237. [26] 陈彩文, 杜永贵, 周超, 等. 基于支持向量机的鱼群摄食行为识别技术[J]. 江苏农业科学, 2018, 46(7): 226-229. [27] 袁超, 朱瑞金. 基于KPCA的多特征融合的支持向量机鱼群摄食行为检测研究[J]. 水产养殖, 2020, 41(12): 17-21. Yuan Chao, Zhu Ruijin. Research on fish school feeding behavior detection based on KPCA multi-feature fusion support vector machine [J]. Journal of Aquaculture, 2020, 41(12): 17-21. [28] 张重阳, 陈明, 冯国富, 等.基于多特征融合与机器学习的鱼类摄食行为的检测[J]. 湖南农业大学学报(自然科学版), 2019, 45(1): 97-102. Zhang Chongyang, Chen Ming, Feng Guofu, et al. Detection method of fish feeding behavior based onthe multi-feature fusion and the machine learning [J]. Journal of Hunan Agricultural University (Natural Sciences), 2019, 45(1): 97-102. [29] 陈明, 张重阳, 冯国富, 等. 基于特征加权融合的鱼类摄食活动强度评估方法[J]. 农业机械学报, 2020, 51(2): 245-253. Chen Ming, Zhang Chongyang, Feng Guofu, et al. Intensity assessment method of fish feeding activities based on feature weighted fusion [J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(2): 245-253. [30] 周超, 徐大明, 吝凯, 等. 基于近红外机器视觉的鱼类摄食强度评估方法研究[J]. 智慧农业, 2019, 1(1): 76-84. Zhou Chao, Xu Daming, Lin Kai, et al.Evaluation of fish feeding activity in aquaculture based on near infrared machine vision [J]. Smart Agriculture,2019, 1(1): 76-84. [31] 朱逢乐, 严霜, 孙霖, 等. 基于深度学习多源数据融合的生菜表型参数估算方法[J]. 农业工程学报, 2022, 38(9): 195-204. Zhu Fengle, Yan Shuang, Sun Lin, et al. Estimation method of lettuce phenotypic parameters using deep learning multi-source data fusion [J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(9):195-204. [32] 张璐, 李道亮, 曹新凯, 等. 基于深度可分离卷积网络的粘连鱼体识别方法[J]. 农业工程学报, 2021, 37(17): 160-167. Zhang Lu, Li Daoliang, Cao Xinkai, et al. Recognition method for adhesive fish based on depthwise separable convolution network [J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(17): 160-167. [33] 李东升, 胡文泽, 兰玉彬, 等. 深度学习在杂草识别领域的研究现状与展望[J]. 中国农机化学报, 2022, 43(9): 137-144. Li Dongsheng, Hu Wenze, Lan Yubin, et al. Research status and prospect of deep learning in weed recognition [J]. Journal of Chinese Agricultural Mechanization, 2022, 43(9): 137-144. [34] Mly H, Aamodt A, Misimi E. A spatio-temporal recurrent network for salmon feeding action recognition from underwater videos in aquaculture [J]. Computers and Electronics in Agriculture, 2019, 167: 105087. [35] 张佳林, 徐立鸿, 刘世晶. 基于水下机器视觉的大西洋鲑摄食行为分类[J]. 农业工程学报, 2020, 36(13): 158-164. Zhang Jialin, Xu Lihong, Liu Shijing. Classification of Atlantic salmon feeding behavior based on underwater machine vision [J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(13): 158-164. [36] 朱明, 张镇府, 黄凰, 等. 基于轻量级神经网络MobileNetV3—Small的鲈鱼摄食状态分类[J]. 农业工程学报, 2021, 37(19):165-172. Zhu Ming, Zhang Zhenfu, Huang Huang, et al. Classification of perch ingesting condition using lightweight neural network MobileNetV3—Small [J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(19): 165-172. [37] 徐立鸿, 黄薪, 刘世晶. 基于改进LRCN的鱼群摄食强度分类模型[J]. 农业机械学报, 2022, 53(10):236-241. Xu Lihong, Huang Xin, Liu Shijing. Recognition of fish feeding intensity based on improved LRCN[J]. Transactions of the Chinese Society for Agricultural Machinery, 2022, 53(10):236-241. [38] 黄平, 郑金存, 庞毅. 基于深度学习的鱼类摄食状态识别算法[J]. 电子设计工程, 2023, 31(18):1-5.Huang Ping, Zheng Jincun, Pang Yi. Deep learning-based fish feeding state recognition algorithm [J]. Electronic Design Engineering, 2023, 31(18):1-5. [39] 刘世晶, 涂雪滢, 钱程, 等. 基于帧间光流特征和改进RNN的草鱼摄食状态分类[J]. 水生生物学报, 2022, 46(6): 914-921. Liu Shijing, Tu Xueying, Qian Cheng, et al. Feeding state classification of grass carp based on optical flow and improved RNN [J]. Acta Hydrobiologica Sinica, 2022, 46(6): 914-921. [40] 孙龙清, 王新龙, 王泊宁, 等. 基于ResNet—CA的鱼群饱腹程度识别方法[J]. 农业机械学报, 2022, 53(S2): 219-225, 277. Sun Longqing, Wang Xinlong, Wang Boning, et al. Identification method of fish satiation level based on ResNet—CA [J]. Transactions of the Chinese Society for Agricultural Machinery, 2022,53(S2):219-225, 277. [41] 冯双星,王丁弘,潘良,等.基于轻量型 S3D 算法的鱼类摄食强度识别系统设计与试验[J].渔业现代化,2023, 50(3):79-86. Feng Shuangxing, Wang Dinghong, Pan Liang, et al. Implementation of fish feeding intensity identification system using light-weight S3D algorithm [J]. Fishery Modernization, 2023, 50(3):79-86. [42] 曹晓慧,刘晃.养殖鱼类摄食行为的特征提取研究与应用进展[J].渔业现代化,2021,48(2):1-8. Cao Xiaohui, Liu Huang. Advances in the study and application of feature extraction in feeding behavior of cultured fish [J]. Fishery Modernization, 2021,48(2):1-8. [43] Mohanty S P, Hughes D P, Salathé M. Using deep learning for image-based plant disease detection [J]. Frontiers in Plant Science, 2016, 7: 1419. [44] 胥婧雯,于红,张鹏,等.基于声音与视觉特征多级融合的鱼类行为识别模型U—FusionNet—ResNet50+SENet [J].大连海洋大学学报, 2023, 38(2):348-356. Xu Jingwen, Yu Hong, Zhang Peng, et al. A fish behavior recognition model based on multi-level fusion of sound and vision U—fusionNet—ResNet50+SENet [J].Journal of Dalian Ocean University, 2023, 38(2):348-356. [45] 胡学龙,朱文韬,杨信廷,等.基于水质—声音—视觉融合的循环水养殖鱼类摄食强度识别[J].农业工程学报, 2023, 39(10):141-150. Hu Xuelong, Zhu Wentao, Yang Xinting, et al. Identification of feeding intensity in recirculating aquaculture fish using water quality-sound-vision fusion [J]. Transactions of the Chinese Society of Agricultural Engineering, 2023, 39(10):141-150. [46] 陈冲.水的光学特性及其对水下成像的影响分析[J].内燃机与配件,2017(6):146-147. [47] 张重阳, 陈明. 基于计算机视觉的鱼类摄食行为研究现状及展望[J]. 江苏农业科学, 2020, 48(24):31-36. |
[1] | Xie Yiheng, Zhang Yanbin, , Du Jianmin, Bi Yuge, Li Wenjing, Gao Xinchao. Research on extraction method of fractional vegetation coverage in desert steppe based on LW—CBAM [J]. Journal of Chinese Agricultural Mechanization, 2025, 46(7): 111-117. |
[2] | Chen Ping, Gulbahar Tohti, , Zhang Guohui, Mamtimin Geni. Maturity detection method of black plum in the natural environment based on improved YOLOv7 [J]. Journal of Chinese Agricultural Mechanization, 2025, 46(7): 124-130. |
[3] | Chen Si, Li Junqiu, Kong Dexiao, Zhang Qinghui, Sun Wenshuo. Research on green orange detection algorithm based on YOLOv8n [J]. Journal of Chinese Agricultural Mechanization, 2025, 46(7): 145-152. |
[4] | Yang Yi, Wu Yijing, Jiang Xueqin, Zhang Jie, Wan Xuefen, . Peach picking recognition in agritourism based on deep learning [J]. Journal of Chinese Agricultural Mechanization, 2025, 46(7): 153-163. |
[5] | Zou Xiaobo, Gao Wenjian, Shi Jiyong, Shi Yongqiang, Shen Tingting. Food image classification technology based on improved DenseNet and transfer learning [J]. Journal of Chinese Agricultural Mechanization, 2025, 46(6): 77-84. |
[6] | Zhen Youchen, Wang Jiayu, Wang Ning, Liu Shengping, Lin Kejian, Li Yanyan. Grassland locust species intelligent identification APP system based on improved ResNet50 [J]. Journal of Chinese Agricultural Mechanization, 2025, 46(5): 68-78. |
[7] | Wang Fengjiang, Wang Mengfei, Zhou Jie. Research and application of key algorithms for agricultural machinery vision navigation based on swarm intelligence [J]. Journal of Chinese Agricultural Mechanization, 2025, 46(5): 92-99. |
[8] | Li Sijin, , Li Jinyang, , Zhang Wei, . Research on soybean canopy leaf recognition method based on improved YOLOv5s [J]. Journal of Chinese Agricultural Mechanization, 2025, 46(5): 100-105. |
[9] | Liu Bo, , Wang Bincheng, , Tao Xu, Guo Nawei, , Ma Yinchi, . Tomato leaf disease recognition method based on enhanced graph structure [J]. Journal of Chinese Agricultural Mechanization, 2025, 46(5): 125-132. |
[10] | Liu Yuping, Liu Chengfei, Zhao Pingwei. Detection method of rice leaf disease based on DeepLabv3—Faster R—CNN [J]. Journal of Chinese Agricultural Mechanization, 2025, 46(4): 108-113. |
[11] | Ding Zhanbo, Xu Jian, Zhu Yaolin, Zhang Yongjin, Liu Chenyu. Research on prickly ash cluster detection based on lightweight YOLO model [J]. Journal of Chinese Agricultural Mechanization, 2025, 46(4): 120-125. |
[12] | Chen Haoyu, Miao Yubin. Citrus internal quality analysis based on GASF transformation and deep learning [J]. Journal of Chinese Agricultural Mechanization, 2025, 46(4): 133-138. |
[13] | Zhang Dongdong, Wang Shuchun, Xu Jinfeng, Sun Guodong, Cai Guangyi, Bao Qihang. Design and experiment of vaccine injection machine for spindle shaped fish based on visual technology#br# [J]. Journal of Chinese Agricultural Mechanization, 2025, 46(4): 163-170. |
[14] | Zhao Yuqing, , , , Jiao Yujie, , , Li Hong, Wang Tianyun, , Li Jiashun, , Zhang Yue, . Research on coffee beans grading based on the improved ShuffleNet V1 [J]. Journal of Chinese Agricultural Mechanization, 2025, 46(4): 194-203. |
[15] | Lü Zongwang, , Wang Tiantian, , Sun Fuyan, , Zhu Yuhua, . Improved YOLOv8m-based grain insect detection method for wheat storage [J]. Journal of Chinese Agricultural Mechanization, 2025, 46(3): 108-114. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||
Copyright © 2021 Journal of Chinese Agricultural Mechanization
Address:100 Liuying, Zhongshan Menwai, Xuanwu District, Nanjing Code: Tel: 025-84346270,84346296