Strategic Implementation of Disease Management in Banana Plants Through Soil Mineral Deficiency Correction

International Journal of Electrical and Electronics Engineering
© 2024 by SSRG - IJEEE Journal
Volume 11 Issue 4
Year of Publication : 2024
Authors : Chukka Keerthana, Mudivarthi Venkata Subba Rao, Prasanth Yalla, Peram Tejasree, R.S. Sai Pavan Kumar
pdf
How to Cite?

Chukka Keerthana, Mudivarthi Venkata Subba Rao, Prasanth Yalla, Peram Tejasree, R.S. Sai Pavan Kumar, "Strategic Implementation of Disease Management in Banana Plants Through Soil Mineral Deficiency Correction," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 4, pp. 254-260, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I4P127

Abstract:

This research highlights a critical issue in banana farming: diseases caused by mineral deficiencies in the soil. The focus here is not on how well bananas grow but on how poorly managed soil leads to their sickness. The good news is that new technologies offer promising solutions. By using precise methods like soil mapping and smart fertilizer systems, farmers can pinpoint and address these deficiencies directly. Furthermore, advancements in biotechnology, such as GMOs and biofertilizers, could improve the plants’ ability to absorb nutrients and fight off disease. These techniques have the potential to revolutionize banana cultivation, not just by increasing yields but by creating a more sustainable and disease-resistant crop. The next steps involve developing tools to predict disease based on soil minerals and creating practical recommendations for better soil management and fertilization practices.

Keywords:

Advanced technologies, Banana cultivation, Disease management, Mineral deficiencies, Soil health.

References:

[1] M. Shyamala Devi et al., “Eight Convolutional Layered Deep Convolutional Neural Network Based Banana Leaf Disease Prediction,” 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, pp. 313-317, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] N. Bharathi Raja, and P. Selvi Rajendran, “Comparative Analysis of Banana Leaf Disease Detection and Classification Methods,” 2022 6 th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, pp. 1215-1222, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Eduardo Correa et al., “Design and Implementation of a CNN Architecture to Classify Images of Banana Leaves with Diseases,” 2021 IEEE International Conference on Automation/XXIV Congress of the Chilean Association of Automatic Control (ICA-ACCA), Valparaiso, Chile, pp. 1-6, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Vandana Chaudhari, and Manoj Patil, “Banana Leaf Disease Detection Using K-Means Clustering and Feature Extraction Techniques,” 2020 International Conference on Advances in Computing, Communication & Materials (ICACCM), Dehradun, India, pp. 126-130, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Akshaya Aruraj et al., “Detection and Classification of Diseases of Banana Plant Using Local Binary Pattern and Support Vector Machine,” 2019 2nd International Conference on Signal Processing and Communication (ICSPC), Coimbatore, India, pp. 231-235, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[6] N. Saranya et al., “Detection of Banana Leaf and Fruit Diseases Using Neural Networks,” 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, pp. 493-499, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] G. Karthik, and N. Praburam, “Detection and Prevention of Banana Leaf Diseases from Banana Plant Using Embedded Linux Board,” 2016 Online International Conference on Green Engineering and Technologies (IC-GET), Coimbatore, India, pp. 1-5, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Giorgette Louise H. Tuazon, Hazeline M. Duran, and Jocelyn F. Villaverde, “Portable Sigatoka Spot Disease Identifier on Banana Leaves Using Support Vector Machine,” 2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Manila, Philippines, pp. 1-6, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Huang Jianqing et al., “Research on Banana Leaf Disease Detection Based on the Image Processing Technology,” 2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET), Beijing, China, pp. 76-79, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] N. P. Vidhya, and R. Priya, “Detection and Classification of Banana Leaf Diseases Using Machine Learning and Deep Learning Algorithms,” 2022 IEEE 19th India Council International Conference (INDICON), Kochi, India, pp. 1-6, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Priyanka Sahu et al., “A Systematic Literature Review of Machine Learning Techniques Deployed in Agriculture: A Case Study of Banana Crop,” IEEE Access, vol. 10, pp. 87333-87360, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Estefani Almeyda, Jayro Paiva, and William Ipanaque, “Pest Incidence Prediction in Organic Banana Crops with Machine Learning Techniques,” 2020 IEEE Engineering International Research Conference (EIRCON), Lima, Peru, pp. 1-4, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Wenzhi Liao et al., “Morphological Analysis for Banana Disease Detection in Close Range Hyperspectral Remote Sensing Images,” 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, pp. 3697-3700, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Shankar Pujari, and Nagaraju Bogiri, “Precision Agriculture for Banana Using Wireless Sensor Network,” 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), Pune, India, pp. 1-6, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Jonalee Barman Kakati, and Tapashi Kashyap Das, “Classification of Healthy and Unhealthy Banana Leaves Using Deep Learning Approach: A Comparative Assessment,” 2023 4th International Conference on Computing and Communication Systems (I3CS), Shillong, India, pp. 1-5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Deepak Banerjee et al., “Precision Agriculture: Classifying Banana Leaf Diseases with Hybrid Deep Learning Models,” 2023 IEEE 8th International Conference for Convergence in Technology (I2CT), Lonavla, India, pp. 1-5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Nionel C. Ibarra, Matthew P. Rivera, and Cyrel O. Manlises, “Detection of Panama Disease on Banana Leaves Using the YOLOv4 Algorithm,” 2023 15th International Conference on Computer and Automation Engineering (ICCAE), Sydney, Australia, pp. 209-214, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] E. Mohanraj et al., “Banana Leaf Disease Detection Using Advanced Convolutional Neural Network,” 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), Coimbatore, India, pp. 597-603, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Nionel C. Ibarra, Matthew P. Rivera, and Cyrel O. Manlises, “Determination of Leaf Degradation Percentage for Banana leaves with Panama Disease Using Image Segmentation of Color Spaces and OpenCV,” 2023 15th International Conference on Computer and Automation Engineering (ICCAE), Sydney, Australia, pp. 269-275, 2023.
[CrossRef] [Google Scholar] [Publisher Link]