Advancing Sugarcane Disease Detection through CNN-Based Deep Learning | [International Journal of Membrane Science and Technology • 2023]

Author(s):

1. Riya Walia: Computer Science & Engineering Quantum University Uttarakhand,,India

2. Satender kumar: Computer Science & Engineering Student Quantum University Uttarakhand,,India

Abstract:

Agriculture produce especially sugarcane crop is no exception to diseases as compared to the other crops. Sugarcane, a vital cash crop for the global sugar industry, faces numerous challenges, with the Top Borer disease. Disease prone sugarcane crop directly affects the production quality and quantity. Sugarcane infections are a cause of worry for the farmers because they can wipe out the entire crop field. Researchers are working on applying Artificial Intelligence (AI) techniques, like Machine Learning (ML) and Deep Learning (DL), to analyse the agricultural data (yield prediction, selling price forecasting, climate, and soil quality etc.) and prevent crop damage due to various reasons, diseases being one of them. Deep neural network which includes Convolutional Neural Network (CNN) is a modern technique for agricultural disease detection. Hence, this paper presents the feasibility study and the effectiveness of DL based CNN algorithm in the disease detection of crops with special reference to selective four diseases of sugarcane crop in India. The proposed system integrates state-of-the-art deep learning algorithms, leveraging Convolutional Neural Networks (CNNs) and recurrent models, to analyze high-resolution images captured by unmanned aerial vehicles (UAVs) or ground-based sensors. These images provide a comprehensive view of the sugarcane plantation, allowing for the identification of subtle symptoms and early-stage infections that may go unnoticed by the human eye. The key components of the developed system include a robust image preprocessing pipeline to enhance the quality of input data, a customized deep neural network architecture trained on a diverse dataset of sugarcane images, and a real-time monitoring system for timely intervention. The model's performance is evaluated on a large-scale dataset collected from sugarcane plantations across diverse geographic regions. The results demonstrate the system's high accuracy in detecting and classifying the Top Borer disease, outperforming traditional methods.

Page(s): 1851-1861

DOI: 10.15379/ijmst.v10i1.3501

Published: Journal: International Journal of Membrane Science and Technology, Volume: 10, Issue: 1, Year: 2023

Keywords:

Machine learning ML , Topborer Diseases , Convolutional Neural Network CNN

References:

[1] Bazarevsky V.,Grishchenko I.,Raveendran K.,Zhu T.,Zhang F.,Grundmann M. .2006 .On-device real-time body pose tracking. arXiv, : .

[2] Zhang S.,Chen C. .2022 .Human deep squat detection method based on MediaPipe combined with Yolov5 network,”. 202241st Chinese Control Conference (CCC), : 6404-6409.

[3] Halder .2021 .Real-time vernacular sign language recognition using mediapipe and machine learning. ” Journal homepage: www. ijrpr. com ISSN 2582, : 7421.

[4] Chen S.,Yang R. R. .2006 .Pose trainer: correcting exercise posture using pose estimation. , : .

[5] Agarwal V.,Sharma K.,Rajpoot A. K. .2022 .AI based Yoga Trainer - Simplifying home yoga using mediapipe and video streaming. ” 2022 3rd International Conference for Emerging Technology (INCET), 9824332 : 1-5.

[6] Jiunn. .2023 .Workout exercise. Kaggle. Retrieved Febru-ary 25, : .

[7] Abdillah H. .2023 .. , : .

[8] Li X.,Zhang M.,Gu J.,Zhang Z.,CISP-BMEI ) .2022 .Fitness Action Count-ing Based on MediaPipe,” 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informat- ics (. , 9980337 : 1-7.

[9] Taware G.,Kharat R.,Dhende P.,Jondhalekar P.,Agrawal R.,( ICCUBEA .2022 .AI-Based Workout Assistant. , 10010733 : 1-4.

[10] Samhitha G.,Rao D. S.,Rupa C.,Ekshitha Y.,Jaswanthi R. .2021 .Intel-ligent Communication and Smart Electrical Systems (ICSES), Chennai. Vyayam: Artificial Intelligence based Bicep Curl Workout Tracking System,” 2021 International Conference on Innovative Computing, 9633841 : 1-5.

[11] Singh A.,Agarwal S.,Nagrath P.,Saxena A.,Thakur N. .2019 .Human Pose Estimation Using Convolutional Neural Networks. UnitedArab Emirates, 8701267 : 946-952.

[12] Gadhiya R.,Kalani N. .2021 .Analysis of Deep Learning BasedPose Estimation Techniques for Locating Landmarks on Human Body Parts. Controls and Communications (CCUBE), 9702726 : 1-4.

[13] Kanase R. R.,Kumavat A. N.,Sinalkar R. D.,Somani S. .2021 .Pose Estimation and Correcting Exercise Posture. EDP Sciences., 40 : 03031.

[14] Kumar D.,Sinha A. .2020 .Yoga pose detection and classification using deep learning. LAP LAMBERT Academic Publishing., : .

[15] Anilkumar K.T.,Athulya K.A.,Sreeja. K.A. .2021 .Pose Estimated Yoga Monitoring System. SSRN Electronic Journal, 10 : .

[16] Irfan I.,Muthalib M. A. .2022 .Implementation of Human Pose Estimation Using Angle Calculation Logic on The Elder of The Handsas A Fitness Repetition. International Journal of Engineering, Science and Information Technology, 2(4) : 101-110.

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