An extensive review on crop/weed classification models

Abstract

Crop and weed identification remains a challenge for unmanned weed control. Due to the small range between the chopping tine and the important crop location, weed identification against the annual crops must be extremely exact. This study endeavor included a literature evaluation, which included the most important 50 research publications in IEEE, Science Direct, and Springer journals. From 2012 until 2022, all of these papers are gathered. In fact, the diagnosis steps include: preprocessing, feature extraction, and crop/weed classification. This research analyzes the 50 research articles in several aspects, such as the dataset used for evaluations, different strategies used for pre-processing, feature extraction, and classification to get a clear picture of them. Furthermore, each work’s high performance in accuracy, sensitivity, and precision is demonstrated. Furthermore, the present hurdles in crop and weed identification are described, which serve as a benchmark for upcoming researchers.

Keywords

Crop/weed identification pre-processing feature extraction classification algorithm

Get full access to this article

View all access options for this article.

References

Abouzahir,

Sadik and

Sabir, Enhanced approach for weeds species detection using machine vision, 2018.

Adams,

Qiu,

Xu and

J.C.

Schnable, Plant segmentation by supervised machine learning methods, 2020.

Ahmed,

Abdullah Al-Mamun,

A.S.M.

Hossain Bari,

Hossain and

Kwanb, Classification of crops and weeds from digital images: A support vector machine approach, Crop Protection (2012).

Alam,

Shahab Alam,

Roman,

Tufail,

Umer Khan and

Tahir Khan, Real-time machine-learning based crop/weed detection and classification for variable-rate spraying in precision agriculture, 2020.

E.A.

Alsherif, Cereal weeds variation in middle Egypt: Role of crop family in weed composition, Saudi Journal of Biological Sciences (2020).

Ashrafa and

Niaz Khanb, Weed density classification in rice crop using computer vision, Computers and Electronics in Agriculture (2020).

Bakhshipour and

Jafari, Evaluation of support vector machine and artificial neural networks in weed detection using shape features, Computers and Electronics in Agriculture (2017).

Bakhshipour,

Jafari,

Mehdi Nassiri and

Zare, Weed segmentation using texture features extracted from wavelet sub-images, 2017.

Baraibar,

D.A.

Mortensen,

M.C.

Hunter et al., Growing degree days and cover crop type explain weed biomass in winter cover crops, Agron. Sustain. Dev. 38 (2018), 65. doi:10.1007/s13593-018-0543-1.

10.

Bogaard,

Hodgson,

Nitsch et al., Combining functional weed ecology and crop stable isotope ratios to identify cultivation intensity: A comparison of cereal production regimes in Haute Provence, France and Asturias, Spain. Veget Hist Archaeobot 25 (2016), 57–73. doi:10.1007/s00334-015-0524-0.

11.

Bosilj,

Duckett and

Cielniak, Analysis of morphology-based features for classification of crop and weeds in precision agriculture, 2018.

12.

Das and

Bais, DeepVeg: Deep learning model for segmentation of weed, canola, and canola flea beetle damage, 2021.

13.

Dasgupta,

Saha,

Venkatasubbu and

Ramasubramanian, AI crop predictor and weed detector using wireless technologies: A smart application for farmers, Research Article-Computer Engineering And Computer Science (2020).

14.

F.-M.

De Rainville,

Durand,

F.-A.

Fortin,

Tanguy,

Maldague,

Panneton and

M.-J.

Simard, Bayesian classification and unsupervised learning for isolating weeds in row crops, Industrial And Commercial Application (2014).

15.

Di Cicco,

Potena,

Grisetti and

Pretto, Automatic model based dataset generation for fast and accurate crop and weeds detection, 2017.

16.

Espejo-Garcia,

Mylonas,

Athanasakos,

Fountas and

Vasilakoglou, Towards weeds identification assistance through transfer learning, Computers and Electronics in Agriculture (2020).

17.

Farooq,

Hu and

Jia, Analysis of spectral bands and spatial resolutions for weed classification via deep convolutional neural network, 2018.

18.

Fawakherji,

Potena,

Pretto,

D.D.

Bloisi and

Nardi, Multi-spectral image synthesis for crop/weed segmentation in precision farming, Robotics and Autonomous Systems (2021).

19.

Fawakherji,

Youssef,

D.D.

Bloisi,

Pretto and

Nardi, Crop and weeds classification for precision agriculture using context-independent pixel-wise segmentation, 2019.

20.

Gao,

Nuyttens,

Lootens,

He and

J.G.

Pieters, Recognising weeds in a maize crop using a random forest machine-learning algorithm and near-infrared snapshot mosaic hyperspectral imagery, 2018.

21.

Gée,

Denimal,

Merienne et al., Evaluation of weed impact on wheat biomass by combining visible imagery with a plant growth model: Towards new non-destructive indicators for weed competition, Precision Agric 22 (2021), 550–568. doi:10.1007/s11119-020-09776-6.

22.

Hamuda,

Mc Ginley,

Glavin and

Jones, Automatic crop detection under field conditions using the HSV colour space and morphological operations, Computers and Electronics in Agriculture (2017).

23.

Haug,

Michaels,

Biber and

Ostermann, Plant classification system for crop/weed discrimination without segmentation, 2014.

24.

Herrmann,

Shapira,

Kinast,

Karnieli and

D.J.

Bonfil, Ground-level hyperspectral imagery for detecting weeds in wheat fields, 2013.

25.

M.A.J.

Hofmeijer,

Melander,

Salonen,

Lundkvist,

Zarina and

Gerowitt, Crop diversification affects weed communities and densities in organic spring cereal fields in northern Europe, Agriculture, Ecosystems and Environment (2020).

26.

Imran Moazzam,

U.S.

Khan,

W.S.

Qureshi,

M.I.

Tiwana Nasir Rashid,

W.S.

Alasmary,

Iqbal and

Hamza, A patch-image based classification approach for detection of weeds in sugar beet crop, 2021.

27.

Inacio Patricio and

Rieder, Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review, Computers and Electronics in Agriculture (2018).

28.

S.V.

Jansi Rani,

Senthil Kumar,

Priyadharsini,

Jahnavi Srividya and

Harshana, Automated weed detection system in smart farming for developing sustainable agriculture, 2021.

29.

Jiang,

Yong,

Garshasbi,

Shen,

Jiang and

Zhou, Crop and weed classification based on AutoML, 2021.

30.

Jianga,

Zhanga,

Qiaob,

Zhang,

Zhang and

Song, CNN feature based graph convolutional network for weed and crop recognition in smart farming, 2020.

31.

Jin,

Che and

Chen, Weed identification using deep learning and image processing in vegetable plantation, 2021.

32.

Kamath,

Balachandra,

Vardhan and

Maheshwari, Classification of paddy crop and weeds using semantic segmentation, 2022.

33.

Khan,

Tufail,

M.T.

Khan,

Z.A.

Khan and

Anwar, Deep learning-based identification system of weeds and crops in strawberry and pea fields for a precision agriculture sprayer, 2021.

34.

Kounalakis,

G.A.

Triantafyllidis and

Nalpantidis, Image-based recognition framework for robotic weed control systems, 2018.

35.

Kounalakis,

G.A.

Triantafyllidisy and

Nalpantidis, Weed recognition framework for robotic precision farming, 2016.

36.

Krizhevsky,

Sutskever and

G.E.

Hinton, ImageNet classification with deep convolutional neural networks, 2012.

37.

B.A.

Lease,

W.K.

Wong,

Gopal and

C.W.R.

Chiong, Pixel-level weed classification using evolutionary selection of local binary pattern in a stochastic optimised ensemble, 2020.

38.

Lemessa and

Wakjira, Cover crops as a means of ecological weed management in agroecosystems, J. Crop Sci. Biotechnol. 18 (2015), 123–135. doi:10.1007/s12892-014-0085-2.

39.

Lopez-Granados,

Torres-Sanchez,

A.-I.

De Castro,

Serrano-Perez,

F.-J.

Mesas-Carrascosa and

J.-M.

Pena, Object-based early monitoring of a grass weed in a grass crop using high resolution UAV imagery, 2016.

40.

Lottes,

Behley,

Milioto and

Stachniss, Fully convolutional networks with sequential information for robust crop and weed detection in precision farming, 2018.

41.

Lottes,

Hoeferlin,

Sander,

M¨uter,

Schulze Lammers and

Stachniss, An Effective Classification System for Separating Sugar Beets and Weeds for Precision Farming Applications, 2016.

42.

Louargant,

Villette,

Jones,

Vigneau,

J.N.

Paoli and

Ge, Weed detection by UAV: Simulation of the impact of spectral mixing in multispectral images, 2017.

43.

Luo,

Zhao,

Gu,

Zhang,

Qiao,

Tian and

Han, Classification of weed seeds based on visual images and deep learning, Information Processing In Agriculture (2021).

44.

Montalvo,

Pajares,

J.M.

Guerrero,

Romeo,

Guijarro,

Ribeiro,

J.J.

Ruz and

J.M.

Cruz, Automatic detection of crop rows in maize fields with high weeds pressure, in: Expert Systems with Applications, 2012.

45.

Nichterlein,

Matzk,

Kordas et al., Yield of glyphosate-resistant sugar beets and efficiency of weed management systems with glyphosate and conventional herbicides under German and Polish crop production, Transgenic Res 22 (2013), 725–736. doi:10.1007/s11248-012-9678-z.

46.

Nowak,

Nobis et al., Caucalido platycarpi-Vicietum michauxii – a new weed association from crop fields of Kyrgyzstan (Middle Asia), cent. eur. j. biol. 9 (2014), 189–199. doi:10.2478/s11535-013-0256-z.

47.

Osorio,

Puerto,

Pedraza,

Jamaica and

Rodríguez, A deep learning approach for weed detection in lettuce crops using multispectral images, 2020.

48.

Raja,

T.T.

Nguyen,

D.C.

Slaughter and

S.A.

Fennimore, Real-time weed-crop classification and localisation technique for robotic weed control in lettuce, 2020.

49.

Raja,

T.T.

Nguyen,

V.L.

Vuong,

D.C.

Slaughter and

S.A.

Fennimore, RTD-SEPs: Real-time detection of stem emerging points and classification of crop-weed for robotic weed control in producing tomato, 2020.

50.

Razfar,

True,

Bassiouny,

Venkatesh and

Kashef, Weed detection in soybean crops using custom lightweight deep learning models, Journal of Agriculture and Food Research (2022).

51.

M.Z.

Schuster,

S.B.C.

Lustosa,

Pelissari et al., Optimizing forage allowance for productivity and weed management in integrated crop-livestock systems, Agron. Sustain. Dev. 39 (2019), 18. doi:10.1007/s13593-019-0564-4.

52.

Skov Midtiby,

Astrand,

Jørgensen and

Nyholm Jorgensen, Upper limit for context based crop classification in robotic weeding applications, 2016, Special Issue: Robotic Agriculture.

53.

Strothmann,

Ruckelshausen,

Hertzberg,

Scholz and

Langsenkamp, Plant classification with In-Field-Labeling for crop/weed discrimination using spectral features and 3D surface features from a multi-wavelength laser line profile system, Computers and Electronics in Agriculture (2017).

54.

Tang,

Wang,

Zhang,

He,

Xin and

Xu, Weed identification based on K-means feature learning combined with convolutional neural network, Computers and Electronics in Agriculture (2017).

55.

Tao and

We, A hybrid CNN–SVM classifier for weed recognition in winter rape field, 2022.

56.

Wanga,

Zhang and

Wei, A review on weed detection using ground-based machine vision and image processing techniques, Computers and Electronics in Agriculture (2019).

57.

Xu,

Li,

Cao,

Zhu,

Chen and

Ni, Recognition of weeds in wheat fields based on the fusion of RGB images and depth images, 2020.

58.

You,

Liu and

Lee, A DNN-based semantic segmentation for detecting weed and crop, Computers and Electronics in Agriculture (2020).

59.

Zhang,

Guo and

Wang, Combing K-means clustering and local weighted maximum discriminant projections for weed species recognition, 2019.

60.

Zhang,

M.F.

Hansen,

T.N.

Volonakis,

Smith,

Wilson and

Ralston, Broad-leaf weed detection in pasture, 2018.