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Investigation Into the Use of IoT Technology and Machine Learning for the Identification of Crop Diseases

Investigation Into the Use of IoT Technology and Machine Learning for the Identification of Crop Diseases

K. Manikandan, Vivek Veeraiah, Dharmesh Dhabliya, Sanjiv Kumar Jain, Sukhvinder Singh Dari, Ankur Gupta, Sabyasachi Pramanik
Copyright: © 2024 |Pages: 14
ISBN13: 9798369329641|ISBN13 Softcover: 9798369348062|EISBN13: 9798369329658
DOI: 10.4018/979-8-3693-2964-1.ch013
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MLA

Manikandan, K., et al. "Investigation Into the Use of IoT Technology and Machine Learning for the Identification of Crop Diseases." The Ethical Frontier of AI and Data Analysis, edited by Rajeev Kumar, et al., IGI Global, 2024, pp. 211-224. https://doi.org/10.4018/979-8-3693-2964-1.ch013

APA

Manikandan, K., Veeraiah, V., Dhabliya, D., Jain, S. K., Dari, S. S., Gupta, A., & Pramanik, S. (2024). Investigation Into the Use of IoT Technology and Machine Learning for the Identification of Crop Diseases. In R. Kumar, A. Joshi, H. Sharan, S. Peng, & C. Dudhagara (Eds.), The Ethical Frontier of AI and Data Analysis (pp. 211-224). IGI Global. https://doi.org/10.4018/979-8-3693-2964-1.ch013

Chicago

Manikandan, K., et al. "Investigation Into the Use of IoT Technology and Machine Learning for the Identification of Crop Diseases." In The Ethical Frontier of AI and Data Analysis, edited by Rajeev Kumar, et al., 211-224. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-2964-1.ch013

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Abstract

The control and management of crop diseases has always been a focal point of study in the agricultural domain. The growth of agricultural planting areas has posed several obstacles in monitoring, identifying, and managing large-scale illnesses. Insufficient disease identification capacity in relation to the expanding planting area results in heightened disease intensity, leading to decreased crop production and reduced yield per unit area. Evidence indicates that the reduction in crop productivity resulting from illnesses often surpasses 40%, leading to both financial setbacks for farmers and a certain degree of impact on local economic growth. A total of 1406 photos were gathered from 50 image sensor nodes. These images consist of 433 healthy images, 354 images showing big spot disease, 187 images showing tiny spot disease, and 432 images showing rust disease. This chapter examines the cultivation of maize fields in open-air environments and integrates internet of things (IoT) technologies.

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