تشخیص آفت حلزون در باغ مرکبات تحت شرایط نورپردازی متفاوت با استفاده از شبکه‌های عصبی عمیق

نویسندگان
1 دانشجوی دکتری، گروه مهندسی مکانیک بیوسیستم، دانشگاه محقق اردبیلی، اردبیل، ایران
2 دانشیار، گروه مهندسی مکانیک بیوسیستم، دانشگاه محقق اردبیلی، اردبیل، ایران
3 پژوهشگر پسادکتری، گروه مهندسی مکانیک بیوسیستم، دانشگاه محقق اردبیلی، اردبیل، ایران.
4 استادیار، گروه مهندسی برق، دانشگاه مازندران، بابلسر، ایران
چکیده
دفع آفات و امراض جزء مهمترین عملیات در مرحله داشت مرکبات محسوب می‌شود. امروزه تحقیقات زیادی در زمینه تشخیص آفات و بیماری‌های گیاهی با به‌کارگیری روش‌های ماشین بینایی انجام شده است. یکی از مشکلاتی که باعث کاهش دقت ماشین برای تشخیص آفات در شرایط مزرعه‌ای می‌شود، وجود عوامل نامساعد از قبیل سایه و تغییرات شدت نور در ساعات مختلف روز می‌باشد. در این پژوهش به‌منظور یافتن شدت نور مناسب در ساعات مختلف روز از نورپردازی به‌وسیله یک لامپ در محل تصویربرداری استفاده شده است. برای تشخیص درختان آلوده به آفت حلزون از روش یادگیری عمیق با سه نوع الگوریتم‌ بهینه‌ساز نسبتا قوی یعنی RMSProp، Adam و SGDm استفاده شد. برای بررسی و آزمون الگوریتم‌های مورد استفاده، تعداد 8000 تصویر در 9 شرایط مزرعه‌ای و یک حالت آزمایشگاهی مورد بررسی قرار گرفت. در شرایط مزرعه‌ای، کمترین مقدار دقت تشخیص الگوریتم‌ها با 32/64 درصد مربوط به تصویربرداری در روز ابری با شدت نور 350 الی 700 لوکس و با استفاده ازالگوریتم RMSPropحاصل شد، ولی با ایجاد شدت نور کنترل شده به‌وسیله لامپ (تقربیا 9000 لوکس)، دقت تشخیص با استفاده از الگوریتم SGDm تا 25/95 درصد بهبود یافت. در شرایط آزمایشگاهی که تصاویر در محیطی کنترل شده با شدت نور ثابت تهیه شده بود، استفاده از الگوریتم SGDm، دقت تشخیص را تا مقدار 73/98 درصد ارتقاء داد.

کلیدواژه‌ها

موضوعات


عنوان مقاله English

Detection of snail pest in citrus orchard under different lighting conditions using deep neural networks

نویسندگان English

Ramzan Hadipour rokni 1
ezzatallah Askari Asli-Ardeh 2
sajad sabzi 3
Iman Esmaili paeen- Afrakoti 4
1 Ph.D. student, Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
2 Associate professor, Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
3 Postdoc Researcher, Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.
4 Assistant Professor, Faculty of Engineering & Technology, University of Mazandaran, Babolsar, Iran.
چکیده English

The control pests and diseases is considered one of the most important operations of Citrus in the protection stage. Today, a lot of research has been done in various fields of agriculture, including the diagnosis of plant pests and diseases by using machine vision methods. One of the problems that reduce the accuracy of the machine for detecting pests in farm conditions is the presence of adverse factors such as shade and changes in light intensity at different times of the day. In this study, in order to find the appropriate light intensity at different times of the day and increase the brightness of the shady parts of the trees, lighting by a lamp at the imaging site has been used. For detect pest-infected trees (in this snail study) has been used to Deep learning method which has been studied and evaluated by various optimization algorithms such as RMSProp, Adam and SGDm. To evaluate and test the algorithm used, 8000 images were examined in 9 farm conditions and one laboratory state In farm conditions, the lowest detection accuracy of algorithms with 64.32% related to imaging in cloudy days with light intensity of 350 to 700 lux was obtained using RMSProp algorithm, which Detection accuracy was improved up to 95.25% using SGDm algorithm by creating a light intensity controlled by a lamp (approximately 9000 lux). In laboratory conditions where the images were prepared in a controlled environment with constant light intensity, the detection accuracy was Obtained 98.73% with SGDm algorithm.

کلیدواژه‌ها English

Keyword: Citrus
snail pest
intelligent detection
Image processing
deep learning
[1] Officer, P. (2016). Food and agriculture organization of the United Nations. FAO, Italy.
[2] Lee, S. H., Goëau, H., Bonnet, P., & Joly, A. (2020). New perspectives on plant disease characterization based on deep learning. Computers and Electronics in Agriculture, 170, 105220
[3] Lee, H. Y., Kim, D. H., & Park, K. R. (2019). Pest diagnosis system based on deep learning using collective intelligence. The International Journal of Electrical Engineering & Education, 0020720919833052.1-15. ‌
[4] Arel, I., Rose, D. C., & Karnowski, T. P. (2010). Deep machine learning-a new frontier in artificial intelligence research [research frontier]. IEEE computational intelligence magazine, 5(4), 13-18.
[5] Turkoglu, M., Hanbay, D., & Sengur, A. (2019). Multi-model LSTM-based convolutional neural networks for detection of apple diseases and pests. Journal of Ambient Intelligence and Humanized Computing, 1-11. ‌
[6] Sepasi, M., Damavandian, M. R., & Amiri Besheli, B. (2019). Mineral oil barrier is an effective alternative for suppression of damage by white snails. Acta Agriculturae Scandinavica, Section B—Soil & Plant Science, 69(2), 114-120. ‌
[7] Kheirodin, A., Damav, M. R., & Sarailoo, M. H. (2012). Mineral oil as a repellent in comparison with other control methods for citrus brown snail, Caucasotachea lencoranea. African Journal of Agricultural Research, 7(42), 5701-5707.
[8] Abu-Saqer, M. M., Abu-Naser, S. S., & Al-Shawwa, M. O. (2020). Type of Grapefruit Classification Using Deep Learning. ‌
[9] Zheng, Y. Y., Kong, J. L., Jin, X. B., Wang, X. Y., Su, T. L., & Zuo, M. (2019). CropDeep: The crop vision dataset for deep-learning-based classification and detection in precision agriculture. Sensors, 19(5), 1058.
[10] Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
[11] Karabag, C., Verhoeven, J., Miller, N., & Reyes-Aldasoro, C. C. (2019). Texture Segmentation: An Objective Comparison between Traditional and Deep-Learning Methodologies. Preprints.
[12] Sun, X., Wu, P., & Hoi, S. C. (2018). Face detection using deep learning: An improved faster RCNN approach. Neurocomputing, 299, 42-50. ‌
[13] Majumder, A., Behera, L., & Subramanian, V. K. (2016). Automatic facial expression recognition system using deep network-based data fusion. IEEE transactions on cybernetics, 48(1), 103-114. ‌
[14] Ashqar, B. A., Abu-Nasser, B. S., & Abu-Naser, S. S. (2019). Plant Seedlings Classification Using DeepLearning. ‌
[15] Marani, R., Milella, A., Petitti, A., & Reina, G. (2019). Deep learning-based image segmentation for grape bunch detection. In Precision agriculture’19 (pp. 3320-3328). Wageningen Academic Publishers.
[16] Fooladi, S., Farsi, H., & Mohamadzadeh, S. (2019). Detection and classification of skin cancer using deep learning. J Birjand Univ Med Sci, 26(1), 44-53. ‌
[17] Rangarajan, A. K., Purushothaman, R., & Ramesh, A. (2018). Tomato crop disease classification using pre-trained deep learning algorithm. Procedia computer science, 133, 1040- 1047. ‌
[18] Too, E. C., Yujian, L., Njuki, S., & Yingchun, L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture, 161, 272-279.
[19] Barbedo, J. G. A. (2019). Plant disease identification from individual lesions and spots using deep learning. Biosystems Engineering, 180, 96-107.
‌[20] Tavakoli, N., Hemmat, A., & Nazari, B. (2013). Preventing spread of downy mildew in greenhouse cucumber with machine vision system. In Proceeding of National Conference of Passive Defense in Agriculture.
[21] Xing, S., & Lee, M. (2020). Classification Accuracy Improvement for Small-Size Citrus Pests and Diseases Using Bridge Connections in Deep Neural Networks. Sensors, 20(17), 4992.
[22] da Costa, A. Z., Figueroa, H. E., & Fracarolli, J. A. (2020). Computer vision based detection of external defects on tomatoes using deep learning. Biosystems Engineering, 190, 131-144.
[23] Csillik, O., Cherbini, J., Johnson, R., Lyons, A., & Kelly, M. (2018). Identification of citrus trees from unmanned aerial vehicle imagery using convolutional neural networks. Drones, 2(4), 39.
[24] Sowmya, G., & Srikanth, J. (2017). Automatic weed detection and smart herbicide spray robot for corn fields. Int J Sci Eng Technol Res, 6(1), 131-137.
[25] Postalcıoğlu, S. (2020). Performance Analysis of Different Optimizers for Deep Learning-Based Image Recognition. International Journal of Pattern Recognition and Artificial Intelligence, 34(02), 2051003.
[26] Alruwaili, M., Alanazi, S., Abd El-Ghany, S., & Shehab, A. (2019). An Efficient Deep Learning Model for Olive Diseases Detection.
[27] Luaibi, A. R., Salman, T. M., & Miry, A. H. (2020). Detection of citrus leaf diseases using a deep learning technique. International Journal of Electrical and Computer Engineering (IJECE).
Vol. 11, No. 2, pp. 1719~1727
‌[28] Barman, U., Choudhury, R. D., Sahu, D., & Barman, G. G. (2020). Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease. Computers and Electronics in Agriculture, 177, 105661. ‌
‌[29] Tetila, E. C., Machado, B. B., Astolfi, G., de Souza Belete, N. A., Amorim, W. P., Roel, A. R., & Pistori, H. (2020). Detection and classification of soybean pests using deep learning with UAV images. Computers and Electronics in Agriculture, 179, 105836. ‌
‌[30] Liu, B., Ding, Z., Tian, L., He, D., Li, S., & Wang, H. (2020). Grape leaf disease identification using improved deep convolutional neural networks. Frontiers in Plant Science, 11, 1082.
[31] Miao, R. H., Tang, J. L., & Chen, X. Q. (2015). Classification of farmland images based on color features. Journal of Visual Communication and Image Representation, 29, 138-146.
[32] Hernández-Hernández, J. L., García-Mateos, G., González-Esquiva, J. M., Escarabajal-Henarejos, D., Ruiz-Canales, A., & Molina-Martínez, J. M. (2016). Optimal color space selection method for plant/soil segmentation in agriculture. Computers and Electronics in Agriculture, 122, 124-132.
[33] Liu, X., Zhao, D., Jia, W., Ruan, C., Tang, S., & Shen, T. (2016). A method of segmenting apples at night based on color and position information. Computers and Electronics in Agriculture, 122, 118-123.
[34] Ali, M. M., Hashim, N., & Hamid, A. S. A. (2020). Combination of laser-light backscattering imaging and computer vision for rapid determination of oil palm fresh fruit bunches maturity. Computers and Electronics in Agriculture, 169, 105235. ‌
[35] Polder, G., van der Heijden, G. W., van Doorn, J., & Baltissen, T. A. (2014). Automatic detection of tulip breaking virus (TBV) in tulip fields using machine vision. Biosystems Engineering, 117, 35-42. ‌
[36] Nguyen, H. D. D., Pan, V., Pham, C., Valdez, R., Doan, K., & Nansen, C. (2020). Night-based hyperspectral imaging to study association of horticultural crop leaf reflectance and nutrient status. Computers and Electronics in Agriculture, 173, 105458.
[37] Askari Asli-Ardeh, E., Larijani, M. R., Loni, R. (2020). Diagnosis of Rice Blast Disease in Different Environmental Conditions using Image Processing Technique. Journal of Food Science & Technology. JFST No 100, Vol 17. 17- 28. ‌
[38] Kathuria, A. (2018). Intro to optimization in deep learning: Momentum, rmsprop and adam.‌
[39] Keskar, N. S., & Socher, R. (2017). Improving generalization performance by switching from adam to sgd. arXiv preprint arXiv:1712.07628. ‌
[40] Fooladi, S., Farsi, H., & Mohamadzadeh, S. (2019). Detection and classification of skin cancer using deep learning. J Birjand Univ Med Sci, 26(1), 44- 53.‌
[41] Karabayir, I., Akbilgic, O., & Tas, N. (2020). A Novel Learning Algorithm to Optimize Deep Neural Networks: Evolved Gradient Direction Optimizer (EVGO). IEEE Transactions on Neural Networks and Learning Systems. ‌
[42] Çarkacı, N. (2018). Derin Öğrenme Uygulamalarında En Sık kullanılan Hiper-parametreler. ‌
[43] Saleem, M. H., Potgieter, J., & Arif, K. M. (2020). Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers. Plants, 9(10), 1319.
[44] Jahanbakhshi, A., Momeny, M., Mahmoudi, M., & Zhang, Y. D. (2020). Classification of sour lemons based on apparent defects using stochastic pooling mechanism in deep convolutional neural networks. Scientia Horticulturae, 263, 109133
[45] Bhusal, S., Bhattarai, U., & Karkee, M. (2019). Improving Pest Bird Detection in a Vineyard Environment using Super-Resolution and Deep Learning. IFAC-PapersOnLine, 52(30), 18-23.
[46] Abdullahi, H. S., Sheriff, R., & Mahieddine, F. (2017). Convolution neural network in precision agriculture for plant image recognition and classification. In International Conference Seventh on Innovative Computing Technology (INTECH) (Vol. 10).