Smart Agriculture ›› 2020, Vol. 2 ›› Issue (3): 61-74.doi: 10.12133/j.smartag.2020.2.3.202007-SA002
• Topic--Agricultural Artificial Intelligence and Big Data • Previous Articles Next Articles
FLORES Paulo1(), ZHANG Zhao1(
), MATHEW Jithin2, JAHAN Nusrat1, STENGER John1
Received:
2020-07-01
Revised:
2020-08-01
Online:
2020-09-30
Published:
2020-12-09
corresponding author:
Zhao ZHANG
E-mail:paulo.flores@ndsu.edu;zhao.zhang.1@ndsu.edu
About author:
Paulo Flores (1979-), male, assistant professor, research interests: precision agriculture, remote sensing. E-mail: Supported by:
FLORES Paulo, ZHANG Zhao, MATHEW Jithin, JAHAN Nusrat, STENGER John. Distinguishing Volunteer Corn from Soybean at Seedling Stage Using Images and Machine Learning[J]. Smart Agriculture, 2020, 2(3): 61-74.
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1 | CROOKSTON R, KURLE J, COPELAND P, et al. Rotational cropping sequence affects yield of corn and soybean[J]. Agronomy Journal, 1991, 83(1): 108-113. |
2 | BULLOCK G. Crop rotation[J]. Critical Reviews in Plant Sciences, 1992, 11(4): 309-326. |
3 | MEESE G, CARTER R, OPLINGER E S. Corn/soybean rotation effect as influenced by tillage, nitrogen, and hybrid/cultivar[J]. Journal of Production Agriculture, 1991, 4(1): 74-80. |
4 | DE BRUIN L, PORTER M, NICHOLAS J. Use of a rye cover crop following corn in rotation with soybean in the upper Midwest[J]. Agronomy Journal, 2005, 97(2): 587-598. |
5 | LAUER J, PORTER P, OPLINGER E. The corn and soybean rotation effect[J]. Field Crops, 1997, 28: 426-514. |
6 | RHODES C. Why do they do that? Rotating crops kernel description[EB/OL]. (2018-02-12) [2020-06-29]. . |
7 | MARGARET T. Managing plant diseases with crop rotation[EB/OL]. [2020-06-09]. . |
8 | TYLKA G. Soybean cyst nematode [EB/OL]. (1994-12-20) [2020-06-29]. . |
9 | HAROLD V. Crop rotation and soil tilth kernel description[EB/OL]. (2012-08-28)[2020-06-29]. . |
10 | JEFF G, NICOLAI D, STAHL L. Managing the potential for volunteer corn in 2019 [EB/OL]. (2018-10-30) [2020-06-29]. , |
volunteer%20corn%20in%20Enlist%20Corn. | |
11 | MARQUARDT T, TERRY M, JOHNSON G. The impact of volunteer corn on crop yields and insect resistance management strategies[J]. Agronomy, 2013, 3(2): 488-496. |
12 | ALMS J, MOECHNIG M, VOS D. Yield loss and management of volunteer corn in soybean[J]. Weed Technology, 2016, 30(1): 254-262. |
13 | GUNSOLUS J, DAVE N. Managing volunteer corn[EB/OL]. [2020-06-29]. . |
14 | BECKETT H, STOLLER W. Volunteer corn (zea mays) interference in soybeans (glycine max)[J]. Weed Science, 1988, 36(2):159-166. |
15 | CONLEY P, SANTINI B. Crop management practices in Indiana soybean production systems[J]. Crop Management, 2007, 6(1): 1-9. |
16 | ALMS J, MOECHNIG D, DENEKE D. Volunteer corn effect on corn and soybean yield[C]// Annual Meeting of North Central Weed Science Society. Indianapolis, Indiana, USA: North Central Weed Sci. Soc., 2008: 8-11. |
17 | MARQUARDT P, KRUPKE C, JOHNSON W G. Competition of transgenic volunteer corn with soybean and the effect on western corn rootworm emergence[J]. Weed Science, 2012, 60(2): 193-198. |
18 | JHALA A, WRIGHT B. Volunteer corn in soybean: Impact and management kernel description[EB/OL]. (2018-10-20) [2020-06-29]. . |
19 | LINGENFELTER D. Controlling volunteer corn in soybeans[EB/OL]. (2019-06-23) [2020-06-29]. . |
20 | ZHANG Z, HEINEMANN P H, LIU J, et al. The development of mechanical apple harvesting technology: A review[J]. Transactions of the ASABE, 2016, 59(5): 1165-1180. |
21 | ZHANG Z, POTHULA K, LU R. A review of bin filling technologies for apple harvest and postharvest handling[J]. Applied Engineering in Agriculture, 2018, 34(4): 687-703. |
22 | SUNOJ S, SUBHASHREE S N, DHARANI S, et al. Sunflower floral dimension measurements using digital image processing[J]. Computers and Electronics in Agriculture, 2018, 151:403-415. |
23 | CEN H, WAN L, ZHU J, et al. Dynamic monitoring of biomass of rice under different nitrogen treatments using a lightweight UAV with dual image-frame snapshot cameras[J]. Plant Methods, 2019, 15(1): ID 32. |
24 | ABDALLA A, CEN H, El-MANAWY A, et al. Infield oilseed rape images segmentation via improved unsupervised learning models combined with supreme color features[J]. Computers and Electronics in Agriculture, 2019, 162: 1057-1068. |
25 | HASANIJALILIAN O, IGATHINATHANE C, DOETKOTT C, et al. Chlorophyll estimation in soybean leaves infield with smartphone digital imaging and machine learning[J]. Computers and Electronics in Agriculture, 2020, 174: ID 105433. |
26 | ZHANG Z, IGATHINATHANE C, LI J, et al. Technology progress in mechanical harvest of fresh market apples[J]. Computers and Electronics in Agriculture, 2020, 175: ID 105606. |
27 | EL-FAKI S, ZHANG N, PETERSON D E. Weed detection using color machine vision[J]. Transactions of the ASAE, 2000, 43(6): 1969-1978. |
28 | ZHANG Z, HEINEMANN P. Economic analysis of a low-cost apple harvest-assist unit[J]. HortTechnology, 2017, 27(2): 240-247. |
29 | ZHANG Z, POTHULA K, LU R. Economic evaluation of apple harvest and in-field sorting technology[J]. Transactions of the ASABE, 2017, 60(5), 1537-1550. |
30 | ZHANG Z, HEINEMANN P H, LIU J, et al. Design and field test of a low-cost apple harvest-assist unit[J]. Transactions of the ASABE, 2016, 59(5): 1149-1156. |
31 | ZHANG Z, HEINEMANN P H, LIU J, et al. Brush mechanism for distributing apples in a low-cost apple harvest-assist unit[J]. Applied Engineering in Agriculture, 2017, 33(2): 195-201. |
32 | WU L, WEN Y. Weed/corn seedling recognition by support vector machine using texture features[J]. African Journal of Agricultural Research, 2009, 4(9): 840-846. |
33 | WANG A, ZHANG W, WEI X. A review on weed detection using ground-based machine vision and image processing techniques[J]. Computers and Electronics in Agriculture, 2019, 158: 226-240. |
34 | TANG J L, WANG D, ZHANG Z G, et al. Weed identification based on k-means feature learning combined with convolutional neural network[J]. Computers and Electronics in Agriculture, 2017, 135: 63-70. |
35 | FERREIRA S, FREITAS M, SILVA GDA, et al. Weed detection in soybean crops using convnets[J]. Computers and Electronics in Agriculture, 2017, 143: 314-324. |
36 | BAH M D, HAFIANE A, CANALS R. Deep learning with unsupervised data labeling for weed detection in line crops in uav images[J]. Remote Sensing, 2018, 10(11): ID 1690. |
37 | YU J, SHARPE M, SCHUMANN W, et al. Deep learning for image-based weed detection in turfgrass[J]. European Journal of Agronomy, 2019, 104: 78-84. |
38 | LOTTES P, BEHLEY J, MILIOTO A, et al. Fully convolutional networks with sequential information for robust crop and weed detection in precision farming[J]. IEEE Robotics and Automation Letters, 2018, 3(4): 2870-2877. |
39 | VENKATARAMAN D, MANGAYARKARASI N. Computer vision based feature extraction of leaves for identification of medicinal values of plants[C]//2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). Piscataway, New York, USA: IEEE, 2016: 1-5. |
40 | ARSENOVIC M, KARANOVIC M, SLADOJEVIC S, et al. Solving current limitations of deep learning based approaches for plant disease detection[J]. Symmetry, 2019, 11(7): ID 939. |
41 | AZLAH M A F, CHUA L S, RAHMAD F R, et al. Review on techniques for plant leaf classification and recognition[J]. Computers, 2019, 8(4): ID 77. |
42 | KADIR A, NUGROHO L E, SUSANTO A, et al. A comparative experiment of several shape methods in recognizing plants[J]. International Journal of Computer Science & Information Technology, 2011, 3(3): 256-263. |
43 | PEREZ A J, LOPEZ F, BENLLOCH J V, et al. Colour and shape analysis techniques for weed detection in cereal fields[J]. Computers and Electronics in Agriculture, 2000, 25(3): 197-212. |
44 | LIU T, CHEN W, WU W, et al. Detection of aphids in wheat fields using a computer vision technique[J]. Biosystems Engineering, 2016, 141: 82-93. |
45 | HAMUDA E, GINLEY BMC, GLAVIN M, et al. Automatic crop detection under field conditions using the HSV colour space and morphological operations[J]. Computers and Electronics in Agriculture, 2017, 133:97-107. |
46 | SUNOJ S, IGATHINATHANE C, SALIENDRA N, et al. Color calibration of digital images for agriculture and other applications[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 146: 221-234. |
47 | LIU T, LI R, ZHONG X, et al. Estimates of rice lodging using indices derived from UAV visible and thermal infrared images[J]. Agricultural and Forest Meteorology, 2018, 252: 144-154. |
48 | TAMURA H, MORI S, YAMAWAKI T. Textural features corresponding to visual perception[J]. IEEE Transactions on Systems Man and Cybernetics, 1978, 8(6): 460-473. |
49 | ZHANG B, HUANG W, GONG L, et al. Computer vision detection of defective apples using automatic lightness correction and weighted RVM classifier[J]. Journal of Food Engineering, 2015(146): 143-151. |
50 | SUN Y. Iterative relief for feature weighting: Algorithms, theories, and applications[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(6): 1035-1051. |
51 | ZHANG Z, FLORES P, IGATHINATHANE C, et al. Wheat lodging detection from UAS imagery using machine learning algorithms[J]. Remote Sensing, 2020, 12(11): ID 1838. |
52 | ZHOU Z. Ensemble methods: Foundations and algorithms[M]. CRC Press: Boca Raton, FL, USA, 2012. |
53 | HAN S, CAO Q, MENG H. Parameter selection in SVM with RBF kernel function[C]// World Automation Congress 2012. Piscataway, New York, USA: IEEE, 2012. |
54 | AMARI S, WU S. Improving support vector machine classifiers by modifying kernel functions[J]. Neural Networks, 1999, 12(6): 783-789. |
55 | NAIK D L, KIRAN R. Identification and characterization of fracture in metals using machine learning based texture recognition algorithms[J]. Engineering Fracture Mechanics, 2019, 219: ID 106618. |
56 | SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014. |
57 | BHARATH RAJ. A simple guide to the versions of the inception network kernel description[EB/OL]. [2020-06-29]. . |
58 | SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]// Proceedings of the IEEE conference on computer vision and pattern recognition. Piscataway, New York, USA: IEEE, 2015: 1-9. |
59 | BROWNLEEJASON. How to develop VGG, conception and ResNet modules from scratch in keras kernel description[EB/OL] [2020-06-29]. . |
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