Abstract
Agriculture plays an essential role for individuals as it is the fundamental requirement for everyone’s life. Most of the time crops are identified with disease due to which there is a loss in agricultural productivity. Also because of lack of knowledge and skills cultivators are finding it difficult in detecting and rectifying the disease of crops. In India every year 17.5% of crops are lost as it gets diseased because of the usage of pests. In recent days the technology has advanced that new device has come up that are much faster and smart enough in recognizing and detecting disease in leaf that is helping the farmers in the process of monitoring the farms which reduces the job of a farmer. Hence, this approach mainly concentrates on detecting disease in the leaf using “Mask R-CNN” a deep learning technique.
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References
Udawant, P., Srinath, P.: Cotton leaf disease detection using instance segmentation. J. Cases Informat. Technol. (JCIT) 24(4), 1–10 (2022)
Afzaal, U., Bhattarai, B., Pandeeya, Y.R., Lee, J.: An instance segmentation model for strawberry diseases based on mask R-CNN. Sensors 21(19), 6565 (2021)
Su, W.-H., et al.: Automatic evaluation of wheat resistance to fusarium head blight using dual mask-RCNN deep learning frameworks in computer vision. Remote Sensing 13(1), 26 (2020)
Anandhan, K., Singh, A.S.: Detection of paddy crops diseases and early diagnosis using faster regional convolutional neural networks. In: 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 898–902. IEEE (2021)
Rehman, Z., et al.: Recognizing apple leaf diseases using a novel parallel real-time processing framework based on MASK RCNN and transfer learning: an application for smart agriculture. IET Image Process. 15(10), 2157–2168 (2021)
Wang, S., Sun, G., Zheng, B., Yawen, D.: A crop image segmentation and extraction algorithm based on Mask RCNN. Entropy 23(9), 1160 (2021)
Pallapothu, T., Singh, M., Sinha, R., Nangia, H., Udawant, P.: Cotton leaf disease detection using mask RCNN. AIP Conf. Proc. 2393, 020114 (2022). https://doi.org/10.1063/5.0074814
Almazaydeh, L., Alsalameen, R., Elleithy, K.: Herbal leaf recognition using masl-region convolutional neural network (MASK R-CNN). J. Theoret. Appli. Informat. Technol. 100(11) (2022)
Storey, G., Meng, Q., Li, B.: Leaf disease segmentation and detection in apple orchards for precise smart spraying in sustainable agriculture. Sustainability 14(3), 1458 (2022)
Baireddy, S.: Leaf Tar Spot Detection Using RGB Images. arXiv preprint arXiv:2205.00952 (2022)
Yao, N., Ni, F., Wu, M., Wang, H., Li, G., Sung, W.-K.: Deep learning-based segmentation of Peach diseases using convolutional neural network. Front. Plant Sci. 13 (2022)
Harish, H., Sreenivasa Murthy, A.: Identification of lane lines using advanced machine learning. In: 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS), vol. 1. IEEE (2022)
Harish, H., Sreenivasa Murthy, A.: Identification of lane line using PSO segmentation. In: 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE (2022)
Das, S., Roy, D., Das, P.: Disease feature extraction and disease detection from paddy crops using image processing and deep learning technique. In: Computational Intelligence in Pattern Recognition, pp. 443–449. Springer, Singapore (2020)
Kumar, D., Kukreja, V.: Image-based wheat mosaic virus detection with mask-RCNN model. In: 2022 International Conference on Decision Aid Sciences and Applications (DASA), pp. 178–182. IEEE (2022)
Felfeliyan, B., et al.: Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation. arXiv preprint arXiv:2207.11191 (2022)
Nandhini, M., Kala, K.U., Thangadarshini, M., Madhusudhana Verma, S.: Deep Learning model of sequential image classifier for crop disease detection in plantain tree cultivation. Comput. Elect. Agricult. 197, 106915 (2022). https://doi.org/10.1016/j.compag.2022.106915, ISSN 0168–1699
Harish, H., Sreenivasa Murthy, A.: Edge discerning using improved PSO and Canny algorithm. In: Communication, Network and Computing (CNC-2022). CCIS. Springer Nature. https://doi.org/10.1007/978-3-031-43140-1_17
Raj, N., Perumal, S., Singla, S., Sharma, G.K., Qamar, S., Chakkaravarthy, A.P.: Computer aided agriculture development for crop disease detection by segmentation and classification using deep learning architectures. Comput. Elect. Eng. 103, 108357 (2022). ISSN 0045–7906, https://doi.org/10.1016/j.compeleceng.2022.108357. (https://www.sciencedirect.com/science/article/pii/S0045790622005742)
Kurmi, Y., Saxena, P., Kirar, B.S.: Deep CNN model for crops’ diseases detection using leaf images. Multidim Syst Sign. Process 33, 981–1000 (2022). https://doi.org/10.1007/s11045-022-00820-4
Harish, H., Bharathi, D.S., Pratibha, M., . Holla, D., Ashwini, K.B., Keerthana, K.R., Particle swarm optimization for predicting Breast Cancer. In: 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES), Chickballapur, India, pp. 1–5 (2022). https://doi.org/10.1109/ICKECS56523.2022.10060690
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Bharathi, D.S., Harish, H., Shruthi, M.G., Mamatha, M., Ashwitha, U., Manasa, A. (2024). Detection of Leaf Disease Using Mask Region Based Convolutional Neural Network. In: Das, P., Begum, S.A., Buyya, R. (eds) Advanced Computing, Machine Learning, Robotics and Internet Technologies. AMRIT 2023. Communications in Computer and Information Science, vol 1953. Springer, Cham. https://doi.org/10.1007/978-3-031-47224-4_2
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