[1]Martinelli F,Scalenghe R,Davino S,et al. Advanced methods of plant disease detection:a review[J]. Agronomy for Sustainable Development,2015,35(1):1-25.
[2]张燕,田国英,杨英茹,等. 基于SVM的设施番茄早疫病在线识别方法研究[J]. 农业机械学报,2021,52(增刊1):125-133,206.
[3]Zahid I,Attique K M,Muhammad S,et al. An automated detection and classification of citrus plant diseases using image processing techniques:a review [J]. Computers and Electronics in Agriculture,2018,153:12-32.
[4]Fang Y,Ramasamy R P.Current and prospective methods for plant disease detection[J]. Biosensors,2015,4:537-561.
[5]Dubey S R,Jalal A S. Apple disease classification using color,texture and shape features from images [J]. Signal Image & Video Processing,2016,10(5):819-826.
[6]Bai X,Li X,Fu Z,et al. A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images [J]. Computers and Electronics in Agriculture,2017,136:157-165.
[7]何前,郭峰林,方皓正,等. 基于改进LeNet-5模型的玉米病害识别[J]. 江苏农业科学,2022,50(20):35-41.
[8]王权顺,吕蕾,黄德丰,等. 基于改进YOLOv4算法的苹果叶部病害缺陷检测研究[J]. 中国农机化学报,2022,43(11):182-187.
[9]李鑫星,朱晨光,白雪冰,等. 基于可见光谱和支持向量机的黄瓜叶部病害识别方法研究[J]. 光谱学与光谱分析,2019,39(7):2250-2256.
[10]Deepthi M B,Sreekantha D K. Application of expert systems for agricultural crop disease diagnoses:a review[C]//International Conference on Inventive Communication & Computational Technologies,2017:222-229.
[11]蒋清健,姚勇,付志军等. 基于改进卷积神经网络算法的番茄叶片病害识别[J]. 江苏农业科学,2022,50(20):29-34.
[12]Sladojevic S,Arsenovic M,Anderla A,et al. Deep neural networks based recognition of plant diseases by leaf image classification[J]. Computational Intelligence and Neuroscience,2016,2016:3289801.
[13]Ma J,Du K,Zheng F,et al. A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network[J]. Computers and Electronics in Agriculture,2018,154:18-24.
[14]Kamilaris A,Prenafeta-Boldu F X. A review of the use of convolutional neural networks in agriculture[J]. The Journal of Agricultural Science,2018,156(3):312-322.
[15]AI-Saffar A A M,Tao H,Talab M A. Review of deep convolution neural network in image classification[C]//Proceedings of 2017 International Conference on Radar,Antenna,Microwave,Electronics,and Telecommunications.Jakarta,Indonesia,2017:26-31.
[16]Zhang S,Zhang S,Zhang C,et al. Cucumber leaf disease identification with global pooling dilated convolutional neural network[J]. Computers and Electronics in Agriculture,2019,162:422-430.
[17]张善文,王振,王祖良. 多尺度融合卷积神经网络的黄瓜病害叶片图像分割方法[J]. 农业工程学报,2020,36(16):149-157.
[18]Fuentes A,Sang C K,Yoon S,et al. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition[J]. Sensors,2017,17(9):2022-2043.
[19]吴赛赛,周爱莲,谢能付,等. 基于深度学习的作物病虫害可视化知识图谱构建[J]. 农业工程学报,2020,36(24):177-185.
[20]董丽丽,程炯,张翔,等. 融合知识图谱与深度学习的疾病诊断方法研究[J]. 计算机科学与探索,2020,14(5):815-824.
[21]刘知远,韩旭,孙茂松. 知识图谱与深度学习[M]. 北京:清华大学出版社,2020:62-69.
[22]王海晏,江涛,王芳,等. 基于知识图谱的目标识别模型[J]. 探测与控制学报,2022,44(6):76-80,86.
[23]王丹丹. 宁夏水稻知识图谱构建方法研究与应用[D]. 银川:北方民族大学,2020:28-40.
[24]李颀,赵洁,杨柳,等. 基于GA-BP神经网络和特征向量优化组合的黄瓜叶片病斑识别[J]. 浙江农业学报,2019,31(3):487-495.
[25]王志彬,王开义,王书锋,等. 基于动态集成的黄瓜叶部病害识别方法[J]. 农业机械学报,2017,48(9):46-52.
[26]牛学德,高丙朋,南新元,等. 基于改进DenseNet卷积神经网络的番茄叶片病害检测[J]. 江苏农业学报,2022,38(1):129-134.
[27]王献锋,张善文,王震,等. 基于叶片图像和环境信息的黄瓜病害识别方法[J]. 农业工程学报,2014,3(14):148-153.
[1]梁万杰,曹宏鑫.基于卷积神经网络的水稻虫害识别[J].江苏农业科学,2017,45(20):241.
Liang Wanjie,et al.Identification of rice insect pests based on CNN model[J].Jiangsu Agricultural Sciences,2017,45(15):241.
[2]赵建敏,李艳,李琦,等.基于卷积神经网络的马铃薯叶片病害识别系统[J].江苏农业科学,2018,46(24):251.
Zhao Jianmin,et al.Potato leaf disease identification system based on convolutional neural network[J].Jiangsu Agricultural Sciences,2018,46(15):251.
[3]杜军,寇佳丽,赵培阳.基于CiteSpace的我国精准扶贫研究热点与前沿趋势分析[J].江苏农业科学,2019,47(19):6.
Du Jun,et al.Analysis of hotspots and frontier trends of Chinas precision poverty alleviation research based on CiteSpace[J].Jiangsu Agricultural Sciences,2019,47(15):6.
[4]张梅,王萌,马中.基于CiteSpace的中国农产品区域品牌研究知识图谱分析[J].江苏农业科学,2020,48(03):5.
Zhang Mei,et al.Knowledge map analysis of Chinas agricultural product regional brand research based on CiteSpace[J].Jiangsu Agricultural Sciences,2020,48(15):5.
[5]魏青迪,范昊,张承明.基于ECLDeeplab模型提取华北地区耕地的方法[J].江苏农业科学,2020,48(04):209.
Wei Qingdi,et al.A method for extracting cultivated land in North China based on ECLDeeplab model[J].Jiangsu Agricultural Sciences,2020,48(15):209.
[6]陈峰,谷俊涛,李玉磊,等.基于机器视觉和卷积神经网络的东北寒地玉米害虫识别方法[J].江苏农业科学,2020,48(18):237.
Chen Feng,et al.Recognition method of corn pests in northeast cold region based on machine vision and convolutional neural network[J].Jiangsu Agricultural Sciences,2020,48(15):237.
[7]严陶韬,高婷,周之栋,等.基于文献计量的生物炭土壤效应分析[J].江苏农业科学,2021,49(4):191.
Yan Taotao,et al.Analysis of biochar soil effect based on bibliometrics[J].Jiangsu Agricultural Sciences,2021,49(15):191.
[8]陈旭君,王承祥,孙福,等.基于改进Faster R-CNN的田间植株幼苗检测方法[J].江苏农业科学,2021,49(4):159.
Chen Xujun,et al.Detection method for plant seedlings in fields based on improved Faster R-CNN[J].Jiangsu Agricultural Sciences,2021,49(15):159.
[9]黎振,陆玲,熊方康.基于k-means分割和迁移学习的番茄病理识别[J].江苏农业科学,2021,49(12):156.
Li Zhen,et al.Tomato pathological recognition based on k-means segmentation and transfer learning[J].Jiangsu Agricultural Sciences,2021,49(15):156.
[10]范宏,刘素红,陈吉军,等.基于深度学习的白喉乌头与牧草高精度分类研究[J].江苏农业科学,2021,49(12):173.
Fan Hong,et al.Study on high-precision classification of Aconitum leucostomum Worosch and pasture based on deep learning[J].Jiangsu Agricultural Sciences,2021,49(15):173.