Abstract
The rapid collection and development of multimedia data and devices allows agricultural monitoring to be automated. Crop classification using aerial images is the challenge of identifying the appropriate crop type planted on a certain area of land. These approaches, however, have several drawbacks that may inhibit the performance owing to a lack of appropriate data and the lack sufficient investigation of automatic feature extraction techniques. To deal with this, an ensemble approach has been investigated using two different deep convolutional neural networks (DCNN), namely Multi-Filter Multi-Scale deep convolutional neural network (MFMS-DCNN) and a pre-trained Inception V3 architecture, for crop classification using aerial images. At the onset, the MFMS-DCNN is constructed using Convolution- batchnormalization-activation units along with max and average pooling layers. Here, input images have been down-sampled using the max- pooling operation instead of traditional image processing approaches which facilitates to extract the important features in multiple levels, and the feature maps are concatenated by embedding a step-by-step fusion approach. The pre-trained Inception V3 model is initially fine-tuned using the three datasets under consideration (publicly available Plant seedling, and two new aerial image datasets obtained from two regions across India.). Finally, two combiners (Mean rule and Product rule) are enforced to ensemble the outputs of MFMS-DCNN and pre-trained Inception V3, to give the final prediction. To validate the feasibility of the proposed method, experimentations have been performed on the considered datasets and with state-of-the-art techniques. The findings derived from these image datasets produce superior performance for the proposed schemes as compared to the state-of-the-art techniques. Apart from that, with average accuracy ≈9% to ≈99% for all datasets, the ensemble methods with both combiners are proven to be more efficient than the two individual proposed schemes (MFMS-DCNN and fine-tuned Inception V3).
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References
Alimboyong C R, Hernandez A A (2019) An improved deep neural network for classification of plant seedling images. In: International colloquium on signal processing & its applications (CSPA). IEEE, pp 217–222
Amara J, Bouaziz B, Algergawy A, et al. (2017) A deep learning-based approach for banana leaf diseases classification. In: BTW (Workshops), pp 79–88
Bansal M, Kumar M, Kumar M (2021) 2d object recognition: a comparative analysis of sift, surf and orb feature descriptors. Multimed Tools Applic 80(12):18839–18857
Bansal M, Kumar M, Kumar M (2021) 2d object recognition techniques: state-of-the-art work. Arch Comput Methods Eng 28(3):1147–1161
Bansal M, Kumar M, Kumar M, Kumar K (2021) An efficient technique for object recognition using shi-tomasi corner detection algorithm. Soft Comput 25(6):4423–4432
Bansal M, Kumar M, Sachdeva M, Mittal A (2021) Transfer learning for image classification using vgg19: Caltech-101 image data set. J Ambient Intell Humaniz Comput, 1–12
Bargoti S, Underwood J (2017) Deep fruit detection in orchards. In: 2017 IEEE international conference on robotics and automation (ICRA). IEEE, pp 3626–3633
Biradar C M, Xiao X (2011) Quantifying the area and spatial distribution of double-and triple-cropping croplands in india with multi-temporal modis imagery in 2005. Int J Remote Sens 32(2):367–386
Chen S W, Shivakumar S S, Dcunha S, Das J, Okon E, Qu C, Taylor C J, Kumar V (2017) Counting apples and oranges with deep learning: a data-driven approach. IEEE Robot Autom Lett 2(2):781–788
Cheng G, Ma C, Zhou P, Yao X, Han J (2016) Scene classification of high resolution remote sensing images using convolutional neural networks. In: International geoscience and remote sensing symposium (IGARSS), vol 2016-Novem. IEEE, pp 767–770
Chew R, Rineer J, Beach R, ONeil M, Ujeneza N, Lapidus D, Miano T, Hegarty-Craver M, Polly J, Temple D S (2020) Deep neural networks and transfer learning for food crop identification in uav images. Drones 4(1):7
Du C, Gao S (2017) Image segmentation-based multi-focus image fusion through multi-scale convolutional neural network. IEEE access 5:15750–15761
Dyrmann M, Jørgensen R N, Midtiby H S (2017) Roboweedsupport-detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network. Adv Anim Biosci 8(2):842–847
Dyrmann M, Karstoft H, Midtiby H S (2016) Plant species classification using deep convolutional neural network. Biosys Eng 151:72–80
Ferentinos K P (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318
Giselsson T M, Jørgensen R N, Jensen P K, Dyrmann M, Midtiby H S (2017) A public image database for benchmark of plant seedling classification algorithms. arXiv:1711.05458
Grinblat G L, Uzal L C, Larese M G, Granitto P M (2016) Deep learning for plant identification using vein morphological patterns. Comput Electron Agric 127:418–424
Hall D, McCool C, Dayoub F, Sunderhauf N, Upcroft B (2015) Evaluation of features for leaf classification in challenging conditions. In: 2015 IEEE Winter conference on applications of computer vision, pp 797–804
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Conference on computer vision and pattern recognition (CVPR), pp 770–778
Hu J, Chen Z, Yang M, Zhang R, Cui Y (2018) A multiscale fusion convolutional neural network for plant leaf recognition. IEEE Signal Process Lett 25(6):853–857
Huang Z, Pan Z, Lei B (2017) Transfer learning with deep convolutional neural network for sar target classification with limited labeled data. Remote Sens, 9(9)
Imani M, Ghassemian H (2015) Feature extraction using weighted training samples. IEEE Geosci Remote Sens Lett 12(7):1387–1386
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167
Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. In: International conference on multimedia, pp 675–678
Kalita I, Chakraborty S, Roy M (2019) Deep ensemble network for handling class-imbalance problem in land-cover classification. In: 2019 International conference on information technology (ICIT). IEEE, pp 505–509
Kalita I, Roy M (2020) Deep neural network-based heterogeneous domain adaptation using ensemble decision making in land cover classification. IEEE Trans Artif Intell 1(2):167–180
Kamilaris A, Prenafeta-Boldú F X (2018) Deep learning in agriculture: a survey. Comput Electron Agri 147:70–90
Kingma D P, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980
Krizhevsky A, Sutskever I, Hinton G E (2012) Imagenet classification with deep convolutional neural networks. In: International conference on neural information processing systems, vol 1. ACM, pp 1097–1105
Kumar K (2019) Evs-dk: Event video skimming using deep keyframe. J Vis Commun Image Represent 58:345–352
Kumar K, Shrimankar D D (2018) Deep event learning boost-up approach: delta. Multimed Tools Applic 77(20):26635–26655
Kumar M, Kumar M, et al. (2021) Performance comparison of various feature extraction methods for object recognition on caltech-101 image dataset. In: Applications of artificial intelligence and machine learning. Springer, pp 289–303
Kumar M, Kumar M, et al. (2021) Xgboost: 2d-object recognition using shape descriptors and extreme gradient boosting classifier. In: Computational methods and data engineering. Springer, pp 207–222
Kung H-Y, Kuo T-H, Chen C-H, Tsai P-Y (2016) Accuracy analysis mechanism for agriculture data using the ensemble neural network method. Sustainability 8(8):735
Kussul N, Lavreniuk M, Skakun S, Shelestov A (2017) Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci Remote Sens Lett 14(5):778–782
Kuwata K, Shibasaki R (2015) Estimating crop yields with deep learning and remotely sensed data. In: 2015 IEEE International geoscience and remote sensing symposium (IGARSS). IEEE, pp 858–861
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324
Lee S H, Chan C S, Wilkin P, Remagnino P (2015) Deep-plant: plant identification with convolutional neural networks. In: 2015 IEEE International conference on image processing (ICIP), pp 452–456
Liang P, Shi W, Zhang X (2018) Remote sensing image classification based on stacked denoising autoencoder. Rem Sens 10(1):16
Lv Q, Dou Y, Niu X, Xu J, Xu J, Xia F (2015) Urban land use and land cover classification using remotely sensed sar data through deep belief networks. Journal of Sensors, 2015
Mahdianpari M, Rezaee M, Zhang Y, Salehi B (2018) Wetland classification using deep convolutional neural network. In: International geoscience and remote sensing symposium (IGARSS). IEEE, pp 9249–9252
McCool C, Perez T, Upcroft B (2017) Mixtures of lightweight deep convolutional neural networks: applied to agricultural robotics. IEEE Robot Autom Lett 2(3):1344–1351
Milioto A, Lottes P, Stachniss C (2017) Real-time blob-wise sugar beets vs weeds classification for monitoring fields using convolutional neural networks. ISPRS Annals of the photogrammetry, remote sensing and spatial information sciences 4:41
Mohanty S P, Hughes D P, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419
Mortensen A K, Dyrmann M, Karstoft H, Jørgensen R N, Gislum R, et al. (2016) Semantic segmentation of mixed crops using deep convolutional neural network.. In: CIGR-AgEng Conference, 26-29 June 2016, Aarhus, Denmark. Abstracts and Full papers. Organising Committee, CIGR 2016, pp 1–6
Negi A, Kumar K (2021) Classification and detection of citrus diseases using deep learning. In: Data science and its applications. Chapman and Hall/CRC, pp 63–85
Negi A, Kumar K, Chauhan P (2021) Deep neural network-based multi-class image classification for plant diseases. Agricultural Informatics: automation using the IoT and machine learning, 117–129
Niculescu S, Ienco D, Hanganu J (2018) Application of deep learning of multi-temporal Sentinel-1 images for the classification of coastal vegetation zone of the danube delta. In: International archives of the photogrammetry, remote sensing and spatial information sciences (ISPRS), vol 42. ISPRS, pp 1311–1318
Pantazi X E, Moshou D, Alexandridis T, Whetton R L, Mouazen A M (2016) Wheat yield prediction using machine learning and advanced sensing techniques. Comput Electron Agric 121:57–65
Penatti Otavio AB, Nogueira K, Dos Santos J A (2015) Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?. In: International conference on computer vision and pattern recognition workshops, pp 44–51
Postadjian T, Le-Bris A, Sahbi H, Malle C (2018) Domain adaptation for large scale classification of very high resolution satellite images with deep convolutional neural networks. In: International geoscience and remote sensing symposium (IGARSS). IEEE, pp 3623–3626
Potena C, Nardi D, Pretto A (2016) Fast and accurate crop and weed identification with summarized train sets for precision agriculture. In: International conference on intelligent autonomous systems, pp 105–121
Rahnemoonfar M, Sheppard C (2017) Deep count: fruit counting based on deep simulated learning. Sensors 17(4):905
Ramos PJ, Prieto F A, Montoya EC, Oliveros C E (2017) Automatic fruit count on coffee branches using computer vision. Comput Electron Agric 137:9–22
Rebetez J, Satizábal H F, Mota M, Noll D, Büchi L, Wendling M, Cannelle B, Pérez-Uribe A, Burgos S (2016) Augmenting a convolutional neural network with local histograms-a case study in crop classification from high-resolution uav imagery. In: ESANN
Reyes A K, Caicedo J C, Camargo J E (2015) Fine-tuning deep convolutional networks for plant recognition. CLEF (Working Notes), 1391
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252
Sa I, Ge Z, Dayoub F, Upcroft B, Perez T, McCool C (2016) Deepfruits: a fruit detection system using deep neural networks. Sensors 16(8):1222
Sameen M I, Pradhan B, Aziz O S (2018) Classification of very high resolution aerial photos using spectral-spatial convolutional neural networks. Journal of Sensors, 2018
Scott G J, Marcum R A, Davis C H, Nivin T W (2017) Fusion of deep convolutional neural networks for land cover classification of high-resolution imagery. IEEE Geosci Remote Sens Lett 14(9):1638–1642
Sengupta S, Lee W S (2014) Identification and determination of the number of immature green citrus fruit in a canopy under different ambient light conditions. Biosys Eng 117:51–61
Senthilnath J, Dokania A, Kandukuri M, Ramesh KN, Anand G, Omkar SN (2016) Detection of tomatoes using spectral-spatial methods in remotely sensed rgb images captured by uav. Biosyst Eng 146:16–32
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience, 2016
Sørensen R A, Rasmussen J, Nielsen J, Jørgensen R N (2017) Thistle detection using convolutional neural networks. In: 2017 EFITA WCCA CONGRESS, p 161
Su Y-, Xu H, Yan L- (2017) Support vector machine-based open crop model (sbocm): case of rice production in china. Saudi J Biol Sci 24(3):537–547
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: International conference on computer vision and pattern recognition, pp 2818–2826
Tran T, Choi J, Le T H, Kim J (2019) A comparative study of deep cnn in forecasting and classifying the macronutrient deficiencies on development of tomato plant. Appl Sci 9(8):1601
Xinshao W, Cheng C (2015) Weed seeds classification based on pcanet deep learning baseline. In: 2015 Asia-Pacific signal and information processing association annual summit and conference (APSIPA), pp 408–415
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This work is supported by a project-grant (with file number: ECR/2016/001227) from Science and Engineering Research Board, Department of Science and Technology, Government of India.
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Kalita, I., Singh, G.P. & Roy, M. Crop classification using aerial images by analyzing an ensemble of DCNNs under multi-filter & multi-scale framework. Multimed Tools Appl 82, 18409–18433 (2023). https://doi.org/10.1007/s11042-022-13946-1
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DOI: https://doi.org/10.1007/s11042-022-13946-1