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Plant diseases are one of the grand challenges that face the agriculture sector worldwide. In the United States, crop diseases cause losses of one-third of crop production annually. Despite the importance, crop disease diagnosis is challenging for limited-resources farmers if performed through optical observation of plant leaves’ symptoms. Therefore, there is an urgent need for markedly improved detection, monitoring, and prediction of crop diseases to reduce crop agriculture losses. Computer vision empowered with Machine Learning (ML) has tremendous promise for improving crop monitoring at scale in this context. This paper presents an ML-powered mobile-based system to automate the plant leaf disease diagnosis process. The developed system uses Convolutional Neural networks (CNN) as an underlying deep learning engine for classifying 38 disease categories. We collected an imagery dataset containing 96,206 images of plant leaves of healthy and infected plants for training, validating, and testing the CNN model. The user interface is developed as an Android mobile app, allowing farmers to capture a photo of the infected plant leaves. It then displays the disease category along with the confidence percentage. It is expected that this system would create a better opportunity for farmers to keep their crops healthy and eliminate the use of wrong fertilizers that could stress the plants. Finally, we evaluated our system using various performance metrics such as classification accuracy and processing time. We found that our model achieves an overall classification accuracy of 94% in recognizing the most common 38 disease classes in 14 crop species.
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Editorial: Improving crop health: Understanding the interaction mechanisms between crops and their pathogens
Crop diseases are responsible for substantial yield losses worldwide, thereby threatening global food security. In this Research Topic, a collection of high-quality articles reported recent research progress concerning genes, proteins, secondary metabolites involved in the interactions between crop plants and their pathogens as well as utilization of new synthetic chemicals in control of crop diseases. As co-editors of this research topic, we appreciate the contributions from the authors of the papers published under this topic and highlight the three themes drawn from their research findings.
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- Award ID(s):
- 1901566
- PAR ID:
- 10483130
- Publisher / Repository:
- Frontiers Media
- Date Published:
- Journal Name:
- Frontiers in Plant Science
- Volume:
- 14
- ISSN:
- 1664-462X
- Subject(s) / Keyword(s):
- crop protection, crop-pathogen interactions, immune response, secondary metabolites, synthetic chemicals
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fpls.2023.1161154
