Cassava Leaf Disease Identification and Detection Using Deep Learning Approach
Keywords:Cassava leaf diseases, Deep learning, Convolutional Neural Network (CNN)
Agriculture is the primary source of livelihood for about 60% of the world's total population according to the Food and Agricultural Organization (FAO). The economy of the developing countries is solely dependent on agriculture commodities. As the world population is increasing at faster pace, the demand for food is also escalating tremendously. In recent days, agriculture is experiencing an automation revolution. Hence the introduction of disruptive technologies like Artificial Intelligence plays a major role in increasing agricultural productivity. AI enabled approaches would help in overcoming the traditional challenges faced in agriculture practices, by automating various agriculture related tasks. Nowadays, farmers adopt precision farming which uses AI techniques namely in crop health monitoring, weed detection, plant disease identification and detection, and forecast weather, commodity prices to increase the yield. As there is scarcity of manpower in agriculture sector, AI based equipment like bots and drones are used widely. Crop diseases are a major threat to food security and the manual identification of the diseases with the help of experts will incur more cost and time, especially for larger farms. The machine-vision based techniques provide image based automatic process control, inspection, and robot guidance for pest and disease control. It provides automated process in agriculture, paving way for improved efficiency and profitability. Various factors contribute for plant diseases, which includes soil health, climatic conditions, species and pests. The proposed chapter elaborates on the use of deep learning techniques in the leaf disease detection of Cassava plants. The chapter initially describes the evolution of various neural network techniques used in classification and prediction. It describes the significance of using Convolutional Neural Network (CNN) over deep neural networks. The chapter focuses on classification of leaf disease in Cassava plants using images acquired real time and from Kaggle dataset. In the final part of the chapter, the results of the models with original and augmented data were illustrated considering accuracy as performance metric.
 https://arxiv.org/pdf/1908.02900.pdf (Kaggle dataset)
 https://www.isppweb.org/foodsecurity_casava_diseases.asp#:Ëœ:text=African%20cassa va%20mosaic%20disease%20is,all%20producing%20countries%20in%20Africa
 Bofarhe, Ozichi & Bofarhe, Justice & Segun, Adebayo & Ayandiji, Adebamiji & Demeji, Oloyede & James, Oreoluwa. (2019), "Detection and Classification of Cassava Diseases Using Machine Learning", International Journal of Computer Science and Software Engineering, 8, 2409-4285.
 Gnanasekaran, Sambasivam & Opiyo, Geoffrey. (2020). A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks. Egyptian Informatics Journal. 22. https://doi.org/10.1016/j.eij.2020.02.007
 Karthik, A., MazherIqbal, J.L. Efficient Speech Enhancement Using Recurrent Convolution Encoder and Decoder. Wireless Pers Commun 119, 1959-1973 (2021). https://doi.org/10.1007/s11277-021-08313-6
 Amanda Ramachandran et.al, Deep Learning for Image-Based Cassava Disease Detection,Front. Plant Sci., 22 September 2016 https://doi.org/10.3389/fpls.2017.01852
 P. B. Padol and A. A. Yadav, "SVM classifier based grape leaf disease detection," 2016 Conference on Advances in Signal Processing (CASP), 2016, pp. 175-179. https://doi.org/10.1109/CASP.2016.7746160
 Sharada P. Mohanty, David P. Hughes and Marcel Salathé (2016) "Using Deep Learning for Image-Based Plant Disease Detection", Front. Plant Sci. 7:1419. https://doi.org/10.3389/fpls.2016.01419
 O. Kulkarni, "Crop Disease Detection Using Deep Learning," 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 2018, pp. 1-4. https://doi.org/10.1109/ICCUBEA.2018.8697390
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.