Abstract
Crop disease is a significant issue that affects the growth and yield of crops, leading to financial loss for farmers. Identification and treatment of crop diseases have become challenging due to the increase in the variety of diseases and the lack of knowledge among farmers. To address this issue, this investigate uses an ensembled-deep-learning paradigm to propose a deep learning-based model for crop disease identification trained with an ABC-CPOA. Initially, collected raw images are pre-processed via Bilateral filter and gamma correction Feature Extraction: Then, from the pre-processed images, the features like texture feature (Local Quinary Pattern (LQP), Local Gradient Pattern (LGP), Enriched Local Binary Pattern (E-LBP), color features (Color Histogram, Color Moments), shape features (Contour-based features, Convex Hull). Optimal feature selection- Among the extracted features, the optimal features is designated by means of a self-improved meta-heuristic optimization model referred as ABC-CPOA. This ABC-CPOA model is an extended version of standard Coyote Optimization Algorithm (COA). Crop disease detection phase is modelled with a new ensembled-deep-learning paradigm. Ensembled-deep-learning paradigm comprises Attention-based Bi-LSTM, Recurrent Neural Networks (RNNs) and Optimized Deep Neural Network (O-DNN). The weight function of O-DNN is fine-tuned using the new ABC-CPOA. Precision, recall, sensitivity, and specificity, in addition to TPR, FPR, FNR, and TNR, F1-score, and accuracy are used to assess the suggested approach. The implementation was performed by the MATLAB tool (version: 2022B).
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References
Fang Y, Ramasamy RP (2015) Current and prospective methods for plant disease detection. Biosensors 5(3):537–561
Sujatha R, Chatterjee JM, Jhanjhi NZ, Brohi SN (2021) Performance of deep learning vs machine learning in plant leaf disease detection. Microprocess Microsyst 80:103615
Hernández S, López JL (2020) Uncertainty quantification for plant disease detection using Bayesian deep learning. Appl Soft Comput 96:106597
Saleem MH, Potgieter J, Arif KM (2019) Plant disease detection and classification by deep learning. Plants 8(11):468
Arsenovic M, Karanovic M, Sladojevic S, Anderla A, Stefanovic D (2019) Solving current limitations of deep learning-based approaches for plant disease detection. Symmetry 11(7):939
Kerkech M, Hafiane A, Canals R (2020) Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach. Comput Electron Agric 174:105446
Sharma P, Berwal YPS, Ghai W (2020) Performance analysis of deep learning CNN models for disease detection in plants using image segmentation. Information Processing in Agriculture 7(4):566–574
Singh V, Misra AK (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf Process Agriculture 4(1):41–49
Abbas A, Jain S, Gour M, Vankudothu S (2021) Tomato plant disease detection using transfer learning with C-GAN synthetic images. Comput Electron Agric 187:106279
Zhang Y, Song C, Zhang D (2020) Deep learning-based object detection improvement for tomato disease. IEEE Access 8:56607–56614
Singh V (2019) Sunflower leaf diseases detection using image segmentation based on particle swarm optimization. Artif Intell Agriculture 3:62–68
Picon A, Alvarez-Gila A, Seitz M, Ortiz-Barredo A, Echazarra J, Johannes A (2019) Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Comput Electron Agric 161:280–290
Selvaraj MG, Vergara A, Ruiz H, Safari N, Elayabalan S, Ocimati W, Blomme G (2019) AI-powered banana diseases and pest detection. Plant Methods 15:1–11
Ray M, Ray A, Dash S, Mishra A, Achary KG, Nayak S, Singh S (2017) Fungal disease detection in plants: Traditional assays, novel diagnostic techniques and biosensors. Biosens Bioelectron 87:708–723
Kerkech M, Hafiane A, Canals R (2018) Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images. Comput Electron Agric 155:237–243
Khamparia A, Saini G, Gupta D, Khanna A, Tiwari S, de Albuquerque VHC (2020) Seasonal crops disease prediction and classification using deep convolutional encoder network. Circuits Syst Signal Process 39:818–836
Agarwal M, Gupta SK, Biswas KK (2020) Development of Efficient CNN model for Tomato crop disease identification. Sustain Comput: Inf Syst 28:100407
Coulibaly S, Kamsu-Foguem B, Kamissoko D, Traore D (2019) Deep neural networks with transfer learning in millet crop images. Comput Ind 108:115–120
Ozguven MM, Adem K (2019) Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms. Physica A 535:122537
Bajwa SG, Rupe JC, Mason J (2017) Soybean disease monitoring with leaf reflectance. Remote Sensing 9(2):127
Khan MA, Akram T, Sharif M, Javed K, Raza M, Saba T (2020) An automated system for cucumber leaf diseased spot detection and classification using improved saliency method and deep features selection. Multimed Tools Appl 79:18627–18656
Picon A, Seitz M, Alvarez-Gila A, Mohnke P, Ortiz-Barredo A, Echazarra J (2019) Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions. Comput Electron Agric 167:105093
Pires RDL, Gonçalves DN, Oruê JPM, Kanashiro WES, Rodrigues JF Jr, Machado BB, Gonçalves WN (2016) Local descriptors for soybean disease recognition. Comput Electron Agric 125:48–55
Kamal KC, Yin Z, Wu M, Wu Z (2019) Depthwise separable convolution architectures for plant disease classification. Comput Electron Agric 165:104948
Lu J, Hu J, Zhao G, Mei F, Zhang C (2017) An in-field automatic wheat disease diagnosis system. Comput Electron Agric 142:369–379
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Chithambarathanu, M., Jeyakumar, M.K. Crop disease detection via ensembled-deep-learning paradigm and ABC Coyote pack optimization algorithm (ABC-CPOA). Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19329-y
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DOI: https://doi.org/10.1007/s11042-024-19329-y