Abstract
Identification, classification, and quantification of crop defects are of paramount interest to the farmers for preventive measures and decrease the yield loss through necessary remedial actions. Due to the vast agricultural field, manual inspection of crops is tedious and time-consuming. UAV based data collection, observation, identification, and quantification of defected leaves area are considered to be an effective solution. The present work attempts to estimate the percentage of affected groundnut leaves area across five regions of Andhra Pradesh using image processing techniques. The proposed method involves color space transformation combined with a thresholding technique to perform the segmentation. The calibration measures are performed during acquisition with respect to UAV capturing distance, angle, and other relevant camera parameters. Finally, our method can estimate the consolidated leaves and defected area. The image analysis results across these five regions reveal that around 14–28% of leaves area is affected across the groundnut field, and thereby yield will be diminished correspondingly. Hence, it is recommended to spray the pesticides on the areas affected alone across the field to improve the plant growth and thereby yield will be increased.
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References
Al-Hiary, H., Bani-Ahmad, S., Reyalat, M., Braik, M., Alrahamneh, Z.: Fast and accurate detection and classification of plant diseases. Int. J. Comput. Appl. 17(1), 31–38 (2011)
Arivazhagan, S., Shebiah, R.N., Ananthi, S., Varthini, S.V.: Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agricultural Engineering International: CIGR J. 15(1), 211–217 (2013)
Rastogi, A., Arora, R., & Sharma, S.: Leaf disease detection and grading using computer vision technology & fuzzy logic. In 2015 2nd international conference on signal processing and integrated networks (SPIN) (pp. 500–505). IEEE (2015)
Singh, V., Misra, A.K.: Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf. Process. Agric. 4(1), 41–49 (2017)
Jeyalakshmi, S., & Radha, R.: A review on diagnosis of nutrient deficiency symptoms in plant leaf image using digital image processing. ICTACT J. Image Video Process. 7(4) (2017)
Zhang, S., You, Z., Wu, X.: Plant disease leaf image segmentation based on superpixel clustering and EM algorithm. Neural Comput. Appl. 31(2), 1225–1232 (2019)
Sahithya, V., Saivihari, B., Vamsi, V. K., Reddy, P. S., & Balamurugan, K.: GUI based detection of unhealthy Lleaves using image processing techniques. In 2019 International Conference on Communication and Signal Processing (ICCSP) (pp. 0818–0822). IEEE (2019)
Rasti, P., Ahmad, A., Samiei, S., Belin, E., Rousseau, D.: Supervised image classification by scattering transform with application to weed detection in culture crops of high density. Remote Sensing 11(3), 249 (2019)
Reddy, N. H., Kumar, E. R., Reddy, M. V., Reddy, K. R., & Valli, G. S.: Bioinformatics and image processing—detection of plant diseases. In First International Conference on Artificial Intelligence and Cognitive Computing (pp. 149–154). Springer, Singapore (2019)
Altınbaş, M. D., & Serif, T.: Detecting Defected Crops: Precision Agriculture Using Haar Classifiers and UAV. In International Conference on Mobile Web and Intelligent Information Systems (pp. 27–40). Springer, Cham (2019)
Osroosh, Y., Khot, L.R., Peters, R.T.: Economical thermal-RGB imaging system for monitoring agricultural crops. Comput. Electron. Agric. 147, 34–43 (2018)
Khamparia, A., Saini, G., Gupta, D., Khanna, A., Tiwari, S., de Albuquerque, V.H.C.: Seasonal crops disease prediction and classification using deep convolutional encoder network. Circuits Syst. Signal Process. 39(2), 818–836 (2020)
Poornima, S., Kavitha, S., Mohanavalli, S., & Sripriya, N.: Detection and classification of diseases in plants using image processing and machine learning techniques. In AIP Conference Proceedings (Vol. 2095, No. 1, p. 030018). AIP Publishing LLC (2019)
Dat, D. T., Hai, N. D. X., & Thinh, N. T.: Detection and classification defects on exported banana leaves by computer vision. In 2019 International Conference on System Science and Engineering (ICSSE) (pp. 609–613). IEEE (2019)
Dhingra, G., Kumar, V., Joshi, H.D.: Study of digital image processing techniques for leaf disease detection and classification. Multimedia Tools Appl. 77(15), 19951–20000 (2018)
Wang, G., Sun, Y., Wang, J.: Automatic image-based plant disease severity estimation using deep learning, Computational intelligence and neuroscience (2017)
Camargo, A., Smith, J.S.: An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosys. Eng. 102(1), 9–21 (2009)
Acknowledgements
We would like to thank the Department of Bio-Technology, Govt. of India for providing the funding support under the project Expansion of Activities of Biotech- KISAN Hub in Three Aspirational Districts (Kadapa, Vizaingaram, and Visakhapatnam) of Andhra Pradesh.
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Mahmud, A., Esakki, B., Seshathiri, S. (2021). Quantification of Groundnut Leaf Defects Using Image Processing Algorithms. In: Kaiser, M.S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of International Conference on Trends in Computational and Cognitive Engineering. Advances in Intelligent Systems and Computing, vol 1309. Springer, Singapore. https://doi.org/10.1007/978-981-33-4673-4_53
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DOI: https://doi.org/10.1007/978-981-33-4673-4_53
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