.svg){kind=logo}
A DEEP LEARNING APPROACH FOR ENHANCING CROP DISEASE DETECTION AND PESTICIDE RECOMMENDATION: Tri-bridNet WITH COLLABORATIVE FILTERING
Keywords:
agriculture sector, median value imputation, LeNet-DLV3 model, Tsallis entropy based conditional mutual information, Tri-bridNet disease classifierDOI:
https://doi.org/10.17654/0974165824031Abstract
To ensure the crop health and optimize output in a sustainable way are challenges in the agricultural sector. To meet these challenges, the objective should be prompt detection of crop diseases and the accurate pesticide prescriptions. We present a novel methodology which combines the models of Deep Learning (DL) with a sophisticated image processing method. Both of the metadata and the image data were employed in this work which undergoes to a distinct pre-processing. The segmentation of pre-processed images was used by the model of LeNet-DLV3. By using the statistical features, domain-specific image features, the pertinent features and color features were recovered by the crop image collection as well in metadata. For the Feature Selection (FS), the Tsallis entropy based Conditional Mutual Information (TE-CMI) has been presented. Next, the creation and training of a Tri-bridNet Disease Classifier (TDC) for precise detection of crop disease using Gated Recurrent Units (GRUs), architectures, Convolutional Neural Networks (CNNs) and Multilayer Perceptron (MLP) has been described. After that a strategy of cooperative filtering based on crop disease trends is given along with the environmental variables to recommend the pesticides.
Received: April 12, 2024
Revised: May 22, 2024
Accepted: May 30, 2024
References
W. Albattah, M. Nawaz, A. Javed, M. Masood and S. Albahli, A novel deep learning method for detection and classification of plant diseases, Complex and Intelligent Systems 8 (2022), 507-524.
G. Sakkarvarthi, G. W. Sathianesan, V. S. Murugan, A. J. Reddy, P. Jayagopal and M. Elsisi, Detection and classification of tomato crop disease using convolutional neural network, Electronics 11(21) (2022), 3618.
I. Yag and A. Altan, Artificial intelligence-based robust hybrid algorithm design and implementation for real-time detection of plant diseases in agricultural environments, Biology 11(12) (2022), 1732.
G. Storey, Q. Meng and B. Li, Leaf disease segmentation and detection in apple orchards for precise smart spraying in sustainable agriculture, Sustainability 14(3) (2022), 1458.
A. S. Baghel, A. Bhardwaj and W. Ibrahim, Optimization of pesticides spray on crops in agriculture using machine learning, Computational Intelligence and Neuroscience, 2022.
V. Balaska, Z. Adamidou, Z. Vryzas and A. Gasteratos, Sustainable crop protection via robotics and artificial intelligence solutions, Machines 11(8) (2023), 774.
M. Tholkapiyan, B. Aruna Devi, D. Bhatt, E. Saravana Kumar, S. Kirubakaran and R. Kumar, Performance analysis of rice plant diseases identification and classification methodology, Wireless Personal Communications 130(2) (2023), 1317-1341.
M. Nandhini, K. U. Kala, M. Thangadarshini and S. M. Verma, Deep learning model of sequential image classifier for crop disease detection in plantain tree cultivation, Computers and Electronics in Agriculture 197 (2022), 106915.
L. C. Ngugi, M. Abdelwahab and M. Abo-Zahhad, A new approach to learning and recognizing leaf diseases from individual lesions using convolutional neural networks, Information Processing in Agriculture 10(1) (2023), 11-27.
V. Gautam, N. K. Trivedi, A. Singh, H. G. Mohamed, I. D. Noya, P. Kaur and N. Goyal, A transfer learning-based artificial intelligence model for leaf disease assessment, Sustainability 14(20) (2022), 13610.
P. Kaur, S. Harnal, R. Tiwari, S. Upadhyay, S. Bhatia, A. Mashat and A. M. Alabdali, Recognition of leaf disease using hybrid convolutional neural network by applying feature reduction, Sensors 22(2) (2022), 575.
S. Kendler, R. Aharoni, S. Young, H. Sela, T. Kis-Papo, T. Fahima and B. Fishbain, Detection of crop diseases using enhanced variability imagery data and convolutional neural networks, Computers and Electronics in Agriculture 193 (2022), 106732.
S. M. Hassan and A. K. Maji, Plant disease identification using a novel convolutional neural network, IEEE Access 10 (2022), 5390-5401.
A. Venkataramana, K. S. Kumar, N. Suganthi and R. Rajeswari, Prediction of brinjal plant disease using support vector machine and convolutional neural network algorithm based on deep learning, Journal of Mobile Multimedia 18(3) (2022), 771-788.
Y. Resti, C. Irsan, M. Amini, I. Yani and R. Passarella, Performance improvement of decision tree model using fuzzy membership function for classification of corn plant diseases and pests, Science and Technology Indonesia 7(3) (2022), 284-290.
T. Thorat, B. K. Patle and S. K. Kashyap, Intelligent insecticide and fertilizer recommendation system based on TPF-CNN for smart farming, Smart Agricultural Technology 3 (2023), 100114.
S. Kumar, R. R. Patil and R. Rani, Smart IoT-based pesticides recommendation system for rice diseases, Intelligent Systems and Applications: Select Proceedings of ICISA 2022, Springer Nature Singapore, Singapore, 2023, pp. 17-25.
V. R. Sadasivam, S. M. Suhail, M. S. Rajan and R. Tharun, Classification of plant disease and pesticides recommendation using deep-learning, Journal of Population Therapeutics and Clinical Pharmacology 30(6) (2023), 391-397.
S. Patil, R. Saha and A. Sangole, Rice plant disease detection and remedies recommendation using machine learning, International Research Journal of Engineering and Technology (IRJET) (2022), 1431-1494.
B. Unhelkar and P. Chakrabarti, A novel deep learning models for efficient insect pest detection and recommending an organic pesticide for smart farming, International Journal of Intelligent Systems and Applications in Engineering 12(9s) (2024), 15-31.
M. Rahaman, M. Chowdhury, M. A. Rahman, H. Ahmed, M. Hossain, M. H. Rahman, M. Biswas, M. Kader, T. A. Noyan and M. Biswas, A deep learning based smartphone application for detecting mango diseases and pesticide suggestions, International Journal of Computing and Digital Systems 13(1) (2023), 1273-1286.
J. G. Esgario, P. B. de Castro, L. M. Tassis and R. A. Krohling, An app to assist farmers in the identification of diseases and pests of coffee leaves using deep learning, Information Processing in Agriculture 9(1) (2022), 38-47.
A. V. Panchal, S. C. Patel, K. Bagyalakshmi, P. Kumar, I. R. Khan and M. Soni, Image-based plant diseases detection using deep learning, Materials Today: Proceedings 80 (2023), 3500-3506.
S. Vallabhajosyula, V. Sistla and V. K. K. Kolli, Transfer learning-based deep ensemble neural network for plant leaf disease detection, Journal of Plant Diseases and Protection 129(3) (2022), 545-558.
C. Murugamani, S. Shitharth, S. Hemalatha, P. R. Kshirsagar, K. Riyazuddin, Q. N. Naveed, S. Islam, S. P. Mazher Ali and A. Batu, Machine learning technique for precision agriculture applications in 5G-based internet of things, Wireless Communications and Mobile Computing, 2022.
Dataset 1 is taken from https://www.kaggle.com/datasets/suhelahamed/drone camera-image-dataset-of-agriculture-fields dated on 15/04/2024.
Dataset 2 is taken from https://www.kaggle.com/datasets/akshatgupta7/cropyield- in-indian-states-dataset dated on 15/04/2024.
A. Vulli, P. N. Srinivasu, M. S. K. Sashank, J. Shafi, J. Choi and M. F. Ijaz, Fine-tuned DenseNet-169 for breast cancer metastasis prediction using FastAI and 1-cycle policy, Sensors 22(8) (2022), 2988.
S. S. Sajid, M. Shahhosseini, I. Huber, G. Hu and S. V. Archontoulis, County-scale crop yield prediction by integrating crop simulation with machine learning models, Frontiers in Plant Science 13 (2022), 1000224.
Y. Lu, S. Young, H. Wang and N. Wijewardane, Robust plant segmentation of color images based on image contrast optimization, Computers and Electronics in Agriculture 193 (2022), 106711.
S. P. Raja, B. Sawicka, Z. Stamenkovic and G. Mariammal, Crop prediction based on characteristics of the agricultural environment using various feature selection techniques and classifiers, IEEE Access 10 (2022), 23625-23641.
W. Yuan, N. K. Wijewardane, S. Jenkins, G. Bai, Y. Ge and G. L. Graef, Early prediction of soybean traits through color and texture features of canopy RGB imagery, Scientific Reports 9(1) (2019), 14089.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 PUSHPA PUBLISHING HOUSE, PRAYAGRAJ, INDIA
This work is licensed under a Creative Commons Attribution 4.0 International License.
_________________________
Attribution: Credit Pushpa Publishing House as the original publisher, including title and author(s) if applicable.
Contact Pushpa Publishing House for more info or permissions.
Journal Impact Factor: 
