A Comprehensive Analysis Of Diverse Image Processing Techniques In Agriculture
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Abstract
Agriculture plays a crucial role in fostering sustainable growth through the integration of various technological advancements such as image processing, artificial intelligence, deep learning, and the Internet of Things (IoT). The global population is increasing on a daily basis. The increasing demand within the agriculture industry has necessitated the collective enhancement of plant cultivation and field productivity. This paper emphasizes the significance of effectively managing the crop during its initial growth phase as well as during the harvesting era. Image processing and artificial neural networks are employed as distinct methodologies for detecting illnesses on leaves. When capturing images using drones, the resulting images undergo a process of segmentation and transformation, resulting in the identification of three distinct vectors that represent diseases. These vectors include colour, texture, and morphology. This paper reviews on various disease classification strategies that can be utilized for the detection of plant diseases.
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References
S. M. Hassan, A. K. Maji, M. Jasiński, Z. Leonowicz, and E. Jasińska, “Identification of Plant-Leaf Diseases using CNN and Transfer-Learning Approach,” Electron., vol. 10, no. 12, pp. 1–19, 2021.
M. P. Vaishnnave and K. S. Devi, “Diseases using KNN classifier,” Proceeding Int. Conf. Syst. Comput. Autom. Netw. ,IEEE, pp. 1–5, 2019.
A. Krishnaswamy Rangarajan and R. Purushothaman, “Disease Classification in Eggplant Using Pre-trained VGG16 and MSVM,” Sci. Rep., vol. 10, no. 1, pp. 1–11, 2020, doi: 10.1038/s41598-020-59108-x.
R. F. Caldeira, W. E. Santiago, and B. Teruel, “Identification of cotton leaf lesions using deep learning techniques,” Sensors, vol. 21, no. 9, 2021, doi: 10.3390/s21093169.
S. G. Sodjinou, V. Mohammadi, A. T. Sanda Mahama, and P. Gouton, “A deep semantic segmentation-based algorithm to segment crops and weeds in agronomic color images,” Inf. Process. Agric., vol. 9, no. 3, pp. 355–364, 2021, doi: 10.1016/j.inpa.2021.08.003.
M. Shireesha, G. Yadav, S. K. Chandra, and M. Kumar Bajpai, “Image Reconstruction Using Deep Convolutional Neural Network,” 2020 Int. Conf. Artif. Intell. Signal Process. AISP 2020, 2020, doi: 10.1109/AISP48273.2020.9073016.
W. Wang and J. Gang, “Proceedings of 2018 International Conference on Information Systems and Computer Aided Education, ICISCAE 2018,” Proc. 2018 Int. Conf. Inf. Syst. Comput. Aided Educ. ICISCAE 2018, pp. 64–70, 2019.
F. Ni and B. Wang, “INTEGRAL CONTOUR ANGLE : AN INVARIANT SHAPE DESCRIPTOR FOR CLASSIFICATION AND RETRIEVAL OF LEAF IMAGES School of Information Engineering , Nanjing University of Finance and Economics , Nanjing 210023 , China,” 2018 25th IEEE Int. Conf. Image Process., pp. 1223–1227, 2018.
N. J. Sairamya, L. Susmitha, S. T. George, and M. S. P. Subathra, Hybrid Approach for Classification of Electroencephalographic Signals Using Time À Frequency Images With Wavelets and Texture Features. Elsevier Inc., 2019.
H. Deng, L. Zhang, and L. Wang, “Global context-dependent recurrent neural network language model with sparse feature learning,” Neural Comput. Appl., 2017, doi: 10.1007/s00521-017-3065-x.
S. Merity, N. S. Keskar, and R. Socher, “Regularizing and Optimizing LSTM Language Models,” arXiv:1708.02182, 2017.
Y. Zhang and L. Wu, “Classification of fruits using computer vision and a multiclass support vector machine,” Sensors (Switzerland), vol. 12, no. 9, pp. 12489–12505, 2012, doi: 10.3390/s120912489.
H. I. Dino, “Facial Expression Classification Based on SVM , KNN and MLP Classifiers,” 2019 Int. Conf. Adv. Sci. Eng. IEEE, pp. 70–75, 2019.
J. G. Arnal Barbedo, “Plant disease identification from individual lesions and spots using deep learning,” Biosyst. Eng., vol. 180, pp. 96–107, 2019, doi: 10.1016/j.biosystemseng.2019.02.002.
E. Vamsidhar, P. J. Rani, and K. R. Babu, “Plant Disease Identification and Classification using Image Processing,” Int. J. Eng. Adv. Technol. I, vol. 8, no. February, pp. 442–446, 2019.
V. Ashok and D. S. Vinod, “Automatic quality evaluation of fruits using Probabilistic Neural Network approach,” Proc. 2014 Int. Conf. Contemp. Comput. Informatics, IC3I 2014, pp. 308–311, 2014, doi: 10.1109/IC3I.2014.7019807.
B. Mohebali, A. Tahmassebi, A. Meyer-Baese, and A. H. Gandomi, “Probabilistic neural networks: A brief overview of theory, implementation, and application,” Handb. Probabilistic Model., pp. 347–367, 2020, doi: 10.1016/B978-0-12-816514-0.00014-X.
E. Hossain, F. Hossain, and M. A. Rahaman, “A Color and Texture Based Approach for the Detection and Classification of Plant Leaf Disease Using KNN Classifier,” 2019 Int. Conf. Comput. Electr. Commun. Eng. ICCECE 2019, pp. 1–6, 2019.
M. P. Vaishnnave, K. Suganya Devi, and P. Ganeshkumar, “Automatic method for classification of groundnut diseases using deep convolutional neural network,” Soft Comput., vol. 24, no. 21, pp. 16347–16360, 2020, doi: 10.1007/s00500-020-04946-0.
R. A. Saputra, Suharyanto, S. Wasiyanti, D. F. Saefudin, A. Supriyatna, and A. Wibowo, “Rice Leaf Disease Image Classifications Using KNN Based on GLCM Feature Extraction,” J. Phys. Conf. Ser., vol. 1641, no. 1, 2020, doi: 10.1088/1742-6596/1641/1/012080.
S. Abirami and P. Chitra, Energy-efficient edge based real-time healthcare support system, 1st ed. Elsevier Inc., 2019.
S. Islam, M. A. Hannan, H. Basri, A. Hussain, and M. Arebey, “Solid waste bin detection and classification using Dynamic Time Warping and MLP classifier,” Waste Manag., vol. 34, no. 2, pp. 281–290, 2014, doi: 10.1016/j.wasman.2013.10.030.
R. Chatterjee and T. Bandyopadhyay, “EEG based Motor Imagery Classification using SVM and MLP,” 2016 Int. Conf. Comput. Intell. Networks, pp. 84–89, 2016, doi: 10.1109/CINE.2016.22.
K. Fukushima, “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position,” Biol. Cybern., vol. 36, no. 4, pp. 193–202, 1980, doi: 10.1007/BF00344251.
D. Hubel and T. Wiesel, “,” J. Physiol., pp. 215–243, 1968, [Online]. Available: http://onlinelibrary.wiley.com/doi/10.1113/jphysiol.1968.sp008455/abstract?rss=1.
W. Hu, Y. Huang, L. Wei, F. Zhang, and H. Li, “Deep convolutional neural networks for hyperspectral image classification,” J. Sensors, vol. 2015, 2015, doi: 10.1155/2015/258619.
H. C. Shin et al., “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1285–1298, 2016, doi: 10.1109/TMI.2016.2528162.
S. Sankaran, A. Mishra, R. Ehsani, and C. Davis, “A review of advanced techniques for detecting plant diseases,” Comput. Electron. Agric., vol. 72, no. 1, pp. 1–13, 2010, doi: 10.1016/j.compag.2010.02.007.
Y. Bengio, “Deep learning of representations: Looking forward,” in Statistical Language and Speech Processing, 2013, pp. 1–37.
A. Fuentes, S. Yoon, S. C. Kim, and D. S. Park, “A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition,” Sensors (Switzerland), vol. 17, no. 9, 2017, doi: 10.3390/s17092022.
J. Long, E. Shelhamer, and T. Darrell, “Imperforated Anus in Calves and its Surgical Treatment,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)., pp. 3431–3440, 2015.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst., vol. 1, pp. 1097–1105, 2012, doi: 10.1201/9781420010749.
C. Shorten and T. M. Khoshgoftaar, “A Survey on Image Data Augmentation for Deep Learning,” J. Big Data, vol. 6, no. 60, pp. 1–48, 2019, doi: 10.1186/s40537-019-0197-0.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?,” Adv. Neural Inf. Process. Syst., vol. 4, no. January, pp. 3320–3328, 2014.
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, “Learning Deep Features for Discriminative Localization Bolei,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)., pp. 2921–2929, 2018, doi: 10.5465/ambpp.2004.13862426.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137–1149, 2017, doi: 10.1109/TPAMI.2016.2577031.
S. He, H., Bai, Y., Garcia, E., & Li, “ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In IEEE International Joint Conference on Neural Networks, 2008,” IJCNN 2008.(IEEE World Congr. Comput. Intell., no. 3, pp. 1322– 1328, 2008.
T. G. Dietterich, Ensemble methods in machine learning. 2000.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization,” Proc. IEEE Int. Conf. Comput. Vis., vol. 2017-Octob, pp. 618–626, 2017, doi: 10.1109/ICCV.2017.74.
W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge Computing: Vision and Challenges,” IEEE Internet Things J., vol. 3, no. 5, pp. 637–646, 2016, doi: 10.1109/JIOT.2016.2579198.
K. Golhani, S. K. Balasundram, G. Vadamalai, and B. Pradhan, A review of neural networks in plant disease detection using hyperspectral data, vol. 5, no. 3. China Agricultural University, 2018.
G. I. Parisi, R. Kemker, J. L. Part, C. Kanan, and S. Wermter, “Continual lifelong learning with neural networks: A review,” Neural Networks, vol. 113, pp. 54–71, 2019, doi: 10.1016/j.neunet.2019.01.012.
J. Chen, Q. Liu, and L. Gao, “SS symmetry Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model,” Symmetry (Basel)., vol. 11, 2019, doi: 10.3390/sym11030343.
S. Qadri et al., “Machine vision approach for classification of citrus leaves using fused features,” Int. J. Food Prop., vol. 22, no. 1, pp. 2072–2089, 2019, doi: 10.1080/10942912.2019.1703738.
P. Goyal and J. Gill, “RECOGNITION OF KINNOW PLANT DISEASES USING DEEP CONVOLUTIONAL NEURAL NETWORKS,” J. Asiat. Soc. Mumbai, vol. 97, no. 9, pp. 1–12, 2023.
J. Ma, K. Du, F. Zheng, L. Zhang, Z. Gong, and Z. Sun, “Original papers A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network,” Comput. Electron. Agric., vol. 154, no. June, pp. 18–24, 2018, doi: 10.1016/j.compag.2018.08.048.
K. R. Aravind, P. Raja, R. Aniirudh, K. V Mukesh, R. Ashiwin, and G. Vikas, Grape Crop Disease Classification Using Transfer Learning Approach. Springer International Publishing, 2019.
T. S. Xian and R. Ngadiran, “Plant Diseases Classification using Machine Learning,” J. Phys. Conf. Ser., pp. 1–13, 2021, doi: 10.1088/1742-6596/1962/1/012024.
M. Timothy, O. A. A. John, and B. Adebisi, “Detection and Classification System for Cashew Plant Diseases using Convolutional Neural Network ICFNDS 2021,” 5th Int. Conf. Futur. Networks Distrib. Syst., pp. 225–232, 2022, doi: 10.5897/sre2014.5953.2