Abstract

Crop diseases significantly threaten global food security, impacting agricultural productivity and economic stability. These diseases are caused by various pathogens, including fungi, bacteria, viruses, and nematodes, which can infect various plant parts, including leaves, stems, roots, and fruits. Classifying the several crop diseases is the requirement for the prevention of distinct disease problems. However, it is challenging to detect exact crop diseases that cause slight differences among the diseases of the same crop. Meanwhile, multi-layer convolutional neural networks, while effective in daily computer vision tasks, come with drawbacks such as significant computational memory requirements and extended training times. The simplified convolutional neural network (SCNN) model comprises three hidden layers with increasing order in each convolution kernels 16, 16, and 32, reducing the time and space complexity. This study incorporates normalization, dropout, and regulation techniques to accelerate training merging and enhance accuracy. Then, the performance metrics are found, and distinct algorithms are compared to measure the effectiveness of the top-performing model. The investigational comparisons among the projected SCNN model and others revealed that the planned SCNN model offers the uppermost accuracy. Furthermore, the SCNN outcome is applied to actual crop image datasets, achieving a classification accuracy of 95.69%. Above all, the planned SCNN model demonstrates promising results in potato blight disease classification, offering high accuracy while mitigating the computational memory requirements and training time. These findings suggest its potential applicability in real-world agricultural scenarios for efficient crop disease detection and prevention.

Keyword

Neural networks, SCNN, Potato blight disease, Crop diseases, VGG-16, ResNet-18.

Cite this article

Khan MR, Akther J, Rahman F.Comparative analysis of potato blight diseases BARI-72 and BARI-73 using a simplified convolutional neural network method. International Journal of Advanced Technology and Engineering Exploration. 2024;11(115):819-837. DOI:10.19101/IJATEE.2024.111100083

Refference

[1]Sun DX, Shi MF, Wang Y, Chen XP, Liu YH, Zhang JL, et al. Effects of partial substitution of chemical fertilizers with organic fertilizers on potato agronomic traits, yield and quality. Journal of Gansu Agricultural University. 2023:20230113-1117.

[2]Herrera JP, Rabezara JY, Ravelomanantsoa NA, Metz M, France C, Owens A, et al. Food insecurity related to agricultural practices and household characteristics in rural communities of northeast Madagascar. Food Security. 2021; 13(6):1393-405.

[3]https://www.thedailystar.net/backpage/potato-production-in-bangladesh-glut-badly-hurts-growers-1748572 Accessed 10 June 2023.

[4]https://en.somoynews.tv/news/2024-01-25/potatoes-cost-200-more-than-production-cost. Accessed 29 April 2024.

[5]Feng J, Hou B, Yu C, Yang H, Wang C, Shi X, et al. Research and validation of potato late blight detection method based on deep learning. Agronomy. 2023; 13(6):1-22.

[6]Shoaib M, Shah B, Ei-sappagh S, Ali A, Ullah A, Alenezi F, et al. An advanced deep learning models-based plant disease detection: a review of recent research. Frontiers in Plant Science. 2023; 14:1158933.

[7]Farjon G, Krikeb O, Hillel AB, Alchanatis V. Detection and counting of flowers on apple trees for better chemical thinning decisions. Precision Agriculture. 2020; 21:503-21.

[8]Mohanty SP, Hughes DP, Salathé M. Using deep learning for image-based plant disease detection. Frontiers in Plant Science. 2016; 7:1419.

[9]Trong VH, Gwang-hyun Y, Vu DT, Jin-young K. Late fusion of multimodal deep neural networks for weeds classification. Computers and Electronics in Agriculture. 2020; 175:105506.

[10]Ren F, Liu W, Wu G. Feature reuse residual networks for insect pest recognition. IEEE Access. 2019; 7:122758-68.

[11]Akther J, Harun-or-roshid M, Nayan AA, Kibria MG. Transfer learning on VGG16 for the classification of potato leaves infected by blight diseases. In emerging technology in computing, communication and electronics 2021(pp. 1-5). IEEE.

[12]Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems. 2012.

[13]Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. In proceedings of the conference on computer vision and pattern recognition 2015 (pp. 1-9). IEEE.

[14]Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations. 2015:1-14.

[15]Suarez BMJ, Gomez AL, Diaz JE. Supervised learning-based image classification for the detection of late blight in potato crops. Applied Sciences. 2022; 12(18):1-17.

[16]Siddiqua A, Kabir MA, Ferdous T, Ali IB, Weston LA. Evaluating plant disease detection mobile applications: quality and limitations. Agronomy. 2022; 12(8):1-25.

[17]Wang B. Identification of crop diseases and insect pests based on deep learning. Scientific Programming. 2022; 2022(1):9179998.

[18]Genaev MA, Skolotneva ES, Gultyaeva EI, Orlova EA, Bechtold NP, Afonnikov DA. Image-based wheat fungi diseases identification by deep learning. Plants. 2021; 10(8):1-21.

[19]Genaev MA, Komyshev EG, Shishkina OD, Adonyeva NV, Karpova EK, Gruntenko NE, et al. Classification of fruit flies by gender in images using smartphones and the YOLOv4-tiny neural network. Mathematics. 2022; 10(3):1-19.

[20]Ferentinos KP. Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture. 2018; 145:311-8.

[21]Geetharamani G, Pandian A. Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Computers & Electrical Engineering. 2019; 76:323-38.

[22]Chambon S, Guillaumin J, Montero L, Nikulin Y, Wambergue P, Fillard P. When high-performing models behave poorly in practice: periodic sampling can help. In the tenth international conference on learning representations 2021 (pp. 1-18).

[23]Ahad MT, Li Y, Song B, Bhuiyan T. Comparison of CNN-based deep learning architectures for rice diseases classification. Artificial Intelligence in Agriculture. 2023; 9:22-35.

[24]Akther J, Nayan AA, Harun-Or-Roshid M. Potato leaves blight disease recognition and categorization using deep learning. Engineering Journal. 2023; 27(9):27-38.

[25]Akther J, Harun-or-roshid M, Mahbub-or-rashid M, Soheli SJ. Bangladesh travel guide (BTG) an android mobile application to utilize free time in a better way. Journal of Advance Research in Mobile Computing. 2021; 3(1):1-10.

[26]Chen J, Zhang D, Zeb A, Nanehkaran YA. Identification of rice plant diseases using lightweight attention networks. Expert Systems with Applications. 2021; 169:114514.

[27]Chen J, Zhang D, Nanehkaran YA. Identifying plant diseases using deep transfer learning and enhanced lightweight network. Multimedia Tools and Applications. 2020; 79:31497-515.

[28]Ahmad I, Hamid M, Yousaf S, Shah ST, Ahmad MO. Optimizing pretrained convolutional neural networks for tomato leaf disease detection. Complexity. 2020; 2020(1):8812019.

[29]Krishnaswamy RA, Purushothaman R. Disease classification in eggplant using pre-trained VGG16 and MSVM. Scientific Reports. 2020; 10(1):2322.

[30]Kumar JP, Domnic S. Image based leaf segmentation and counting in rosette plants. Information Processing in Agriculture. 2019; 6(2):233-46.

[31]Rangarajan AK, Raja P. Automated disease classification in (selected) agricultural crops using transfer learning. Automatika: a Magazine for Automation, Measurement, Electronics, Computing and Communications. 2020; 61(2):260-72.

[32]Acharya A, Muvvala A, Gawali S, Dhopavkar R, Kadam R, Harsola A. Plant disease detection for paddy crop using ensemble of CNNs. In international conference for innovation in technology 2020 (pp. 1-6). IEEE.

[33]Sethy PK, Barpanda NK, Rath AK, Behera SK. Deep feature based rice leaf disease identification using support vector machine. Computers and Electronics in Agriculture. 2020; 175:105527.

[34]Shah SR, Qadri S, Bibi H, Shah SM, Sharif MI, Marinello F. Comparing inception V3, VGG 16, VGG 19, CNN, and ResNet 50: a case study on early detection of a rice disease. Agronomy. 2023; 13(6):1-13.

[35]Oyewola DO, Dada EG, Misra S, Damaševičius R. Detecting cassava mosaic disease using a deep residual convolutional neural network with distinct block processing. PeerJ Computer Science. 2021; 7: e352.

[36]Picon A, Alvarez-gila A, Seitz M, Ortiz-barredo A, Echazarra J, Johannes A. Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Computers and Electronics in Agriculture. 2019; 161:280-90.

[37]Li P, Zhong N, Dong W, Zhang M, Yang D. Identifiction of tomato leaf diseases using convolutional neural network with multi-scale and feature reuse. International Journal of Agricultural and Biological Engineering. 2024; 16(6):226-35.

[38]Hu G, Yang X, Zhang Y, Wan M. Identification of tea leaf diseases by using an improved deep convolutional neural network. Sustainable Computing: Informatics and Systems. 2019; 24:100353.

[39]Chen J, Chen J, Zhang D, Sun Y, Nanehkaran YA. Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture. 2020; 173:105393.

[40]Atila Ü, Uçar M, Akyol K, Uçar E. Plant leaf disease classification using efficientnet deep learning model. Ecological Informatics. 2021; 61:101182.

[41]Hassan SM, Jasinski M, Leonowicz Z, Jasinska E, Maji AK. Plant disease identification using shallow convolutional neural network. Agronomy. 2021; 11(12):1-20.

[42]Zeng W, Li M. Crop leaf disease recognition based on self-attention convolutional neural network. Computers and Electronics in Agriculture. 2020; 172:105341.

[43]Oztel I, Yolcu OG, Sahin VH. Deep learning‐based skin diseases classification using smartphones. Advanced Intelligent Systems. 2023; 5(12):2300211.

[44]Hong NM, Thanh NC. Distance-based mean filter for image denoising. In proceedings of the 4th international conference on machine learning and soft computing 2020 (pp. 98-102). ACM.

[45]https://pythonprogramming.net/loading-custom-data-deep-learning-python-tensorflow-keras/. Accessed 27 February 2022.

[46]Ramya R, Anandh A, Muthulakshmi K, Venkatesh S. Gender recognition from facial images using multichannel deep learning framework. In machine learning for biometrics 2022 (pp. 105-28). Academic Press.

[47]Chicho BT, Sallow AB. A comprehensive survey of deep learning models based on keras framework. Journal of Soft Computing and Data Mining. 2021; 2(2):49-62.

[48]He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In proceedings of the conference on computer vision and pattern recognition 2016 (pp. 770-8). IEEE.

[49]Lee JY, Dernoncourt F. Sequential short-text classification with recurrent and convolutional neural networks. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 2016 (pp. 515-20). Association for Computational Linguistics.

[50]Too EC, Yujian L, Njuki S, Yingchun L. A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture. 2019; 161:272-9.

[51]Ma J, Du K, Zheng F, Zhang L, Gong Z, Sun Z. A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Computers and Electronics in Agriculture. 2018; 154:18-24.

[52]Li Y, Nie J, Chao X. Do we really need deep CNN for plant diseases identification?. Computers and Electronics in Agriculture. 2020; 178:105803.

[53]Barbedo JG. Factors influencing the use of deep learning for plant disease recognition. Biosystems Engineering. 2018; 172:84-91.

[54]Gandhi R, Nimbalkar S, Yelamanchili N, Ponkshe S. Plant disease detection using CNNs and GANs as an augmentative approach. In international conference on innovative research and development 2018 (pp. 1-5). IEEE.