An Automatic Insect Detection Framework Using Deep Learning Strategy Based Mask R-CNN Classifier
Keywords:
Deep learning, Agriculture area, Insect identification, CNN, Fast RCNN, Mask RCNNAbstract
Insect infestation affecting fruits and vegetables causes huge production losses and financial losses for the global food and agriculture industries. Farmers are already experiencing a loss in crop productivity for a wide range of reasons, with insects being one of the main issues. This is a sign of lack of information about the condition and the insecticides or pesticides that can be used to control the condition. But in order to manage the condition, it's important to recognize the underlying illness and offer the most effective solutions. The design and development of a model for an efficient insect detection system for agriculture are described in this research paper. The insect related data is gathered from the agricultural area in the form of images. Three phases comprise the proposed methods are input images processing, preprocessing, and mask RCNN-based insect detection. The input data is gathered from the agriculture area, here the insects present in the plants are captured for processing this detection. Then to enhance the quality of the images, pre-processing is performed after data gathering which comprises image resizing, noise filtering and contrast enhancement. In order to achieve better outcomes for the detection of agricultural insects, the final detection is carried out using a deep learning-based Mask RCNN classification model. Some of the prior insect detection techniques applied to evaluate the effectiveness of the proposed technique are RCNN, CNN, Fast RCNN, and Faster RCNN. The presented Mask RCNN classification model obtained 98% accuracy, 93% sensitivity, 90% specificity, 2% error, 95% precision, 4% FalsePositiveRate, 89% F1 Score, and 86% Kappa. Consequently, the deep learning strategy proposed in this paper, comparable studies between Insect and insider manual counting indicate that the method is accurate enough to inform detection systems for integrated insect control of cockroaches.
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Tiwari, V., Gupta, S., Roy, P., Karda, C., Agrawal, S., Rathore, M. S., ...& Pal, A. (2022). Soybean Crop Non-beneficial Insect Identification Using Mask RCNN. In Information and Communication Technology for Competitive Strategies (ICTCS 2020) (pp. 301-311). Springer, Singapore.
Zhao, X., Zhang, J., Huang, Y., Tian, Y., & Yuan, L. (2022). Detection and discrimination of disease and insect stress of tea plants using hyperspectral imaging combined with wavelet analysis. Computers and Electronics in Agriculture, 193, 106717.
Anand, P. P., Seena, S., Peter, J., &ShibuVardhanan, Y. (2022). Detection of geographical specific plasticity and the effect of natural selection pressure on the wing size and shape of Bactrocera dorsalis (Diptera: Tephritidae). Biologia, 77(5), 1347-1371.
Elumalai, P., Yi, X., Chen, Z., Rajasekar, A., de Paiva, T. C. B., Hassaan, M. A., ...& Huang, M. (2022). Detection of Neonicotinoids in agriculture soil and degradation of thiacloprid through photo degradation, biodegradation and photo-biodegradation. Environmental Pollution, 119452.
da Silva Vieira, G., Rocha, B. M., Fonseca, A. U., de Sousa, N. M., Ferreira, J. C., Cabacinha, C. D., &Soares, F. (2022). Automatic detection of insect predation through the segmentation of damaged leaves. Smart Agricultural Technology, 2, 100056.
Zheng, Z., & Zhang, C. (2022). Electronic noses based on metal oxide semiconductor sensors for detecting crop diseases and insect pests. Computers and Electronics in Agriculture, 197, 106988.
Martinazzo, J., Ballen, S. C., Steffens, J., & Steffens, C. (2022). Sensing of pheromones from Euschistusheros (F.) stink bugs by nanosensors. Sensors and Actuators Reports, 4, 100071.
El Sayed, Y. A., Sayed, S., Magdy, A., &Elmenofy, W. (2022). Detection, characterization and virulence analysis of nucleopolyhedrovirus isolated from the cotton leafworm, Spodopteralittoralis (Boisd.)(Lepidoptera: Noctuidae). Egyptian Journal of Biological Pest Control, 32(1), 1-8.
Bereciartua-Pérez, A., Gómez, L., Picón, A., Navarra-Mestre, R., Klukas, C., & Eggers, T. (2022). Insect counting through deep learning-based density maps estimation. Computers and Electronics in Agriculture, 197, 106933.
Couliably, S., Kamsu-Foguem, B., Kamissoko, D., &Traore, D. (2022). Explainable deep convolutional neural networks for insect pest recognition. Journal of Cleaner Production, 133638.
Jiao, L., Xie, C., Chen, P., Du, J., Li, R., & Zhang, J. (2022). Adaptive feature fusion pyramid network for multi-classes agricultural pest detection. Computers and Electronics in Agriculture, 195, 106827.
Kalpana, M., &Senguttuvan, K. (2022). Artificial Intelligence for On-Site Detection of Invasive Rugose Spiralling Whitefly in Coconut Plantation. In Smart Intelligent Computing and Applications, Volume 1 (pp. 413-420). Springer, Singapore.
Srujana, S., &Bhagat, D. (2022). Sensors for agricultural pest management using nanotechnology/nanomaterials. Nanotechnology for Environmental Engineering, 1-5.
Huang, X., Chen, A., Zhou, G., Zhang, X., Wang, J., Peng, N., ...& Jiang, C. (2022). Tomato Leaf Disease Detection System Based on FC-SNDPN. Multimedia Tools and Applications, 1-24.
Khan, R., Khan, M. A., Ansari, M. A., Dhingra, N., &Bhati, N. (2022). Machine learning-based agriculture. In Application of Machine Learning in Agriculture (pp. 3-27). Academic Press.
Júnior, T. D. C., Rieder, R., Di Domênico, J. R., & Lau, D. (2022). InsectCV: A system for insect detection in the lab from trap images. Ecological Informatics, 67, 101516.
Ozdemir, D., &Kunduraci, M. S. (2022). Comparison of Deep Learning Techniques for Classification of the Insects in Order Level With Mobile Software Application. IEEE Access, 10, 35675-35684.
Ullah, N., Khan, J. A., Alharbi, L. A., Raza, A., Khan, W., & Ahmad, I. (2022). An Efficient Approach for Crops Pests Recognition and Classification Based on Novel DeepPestNet Deep Learning Model. IEEE Access, 10, 73019-73032.
Albattah, W., Masood, M., Javed, A., Nawaz, M., &Albahli, S. (2022). Custom CornerNet: a drone-based improved deep learning technique for large-scale multiclass pest localization and classification. Complex & Intelligent Systems, 1-18.
Kavitha Lakshmi, R., &Savarimuthu, N. (2022). A Deep Learning Paradigm for Detection and Segmentation of Plant Leaves Diseases. In Computer Visionand Machine Learning in Agriculture, Volume2 (pp. 229-243). Springer, Singapore.
Moursey, A. H., Aboelghar, M., Khattab, M., Awad, M., Hemeida, I. A., & Ahmed, S. S. (2022). Effect of Gryllotalpagryllotalpa (Orthoptera: Gryllotalpidae) on plant and crop characteristics of sugar beet, Beta vulgaris L. and detection of associated damage using hyperspectral remote sensing. International Journal of Tropical InsectScience, 42(2), 1675-1688.
Butera, L., Ferrante, A., Jermini, M., Prevostini, M., &Alippi, C. (2021). Precise Agriculture: Effective Deep Learning Strategies to Detect Pest Insects. IEEE/CAA Journal of AutomaticaSinica, 9(2), 246-258.
Mayya, V., Kulkarni, U., Surya, D. K., & Acharya, U. R. (2022). An empirical study of preprocessing techniques with convolutional neural networks for accurate detection of chronic ocular diseases using fundus images. Applied Intelligence, 1-19.
Sáez, J. A., &Corchado, E. (2022). ANCES: A novel method to repair attribute noise in classification problems. Pattern Recognition, 121, 108198.
Kumar, B. P., Kumar, A., & Pandey, R. (2022). Region-based adaptive single image dehazing, detail enhancement and pre-processing using auto-colour transfer method. Signal Processing: Image Communication, 100, 116532.
Bhardwaj, S., & Agarwal, R. (2022). An efficient speaker identification framework based on Mask R-CNN classifier parameter optimized using hosted cuckoo optimization (HCO). Journal of Ambient Intelligence and Humanized Computing, 1-13.
https://www.kaggle.com/datasets/vencerlanz09/insect-village-synthetic-dataset.
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