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Enhancing Crop Clinic Assistance: Empowering Plant Disease Detection through Multi-task Machine Learning | IEEE Conference Publication | IEEE Xplore

Enhancing Crop Clinic Assistance: Empowering Plant Disease Detection through Multi-task Machine Learning


Abstract:

Pests and diseases are one of the biggest problems in agriculture, responsible for significant economic losses. Pesticides are frequently misused, resulting in inefficien...Show More

Abstract:

Pests and diseases are one of the biggest problems in agriculture, responsible for significant economic losses. Pesticides are frequently misused, resulting in inefficient pest control. For small farmers, the lack of information is even more damaging due to the little support received from government agencies. Many systems to detect plant diseases through images exist nowadays, aiding phytopathology specialists in diagnosing crops. Those systems use computer vision and machine learning to classify diseases through photos of leaves and fruits. However, proper diagnosis involves many intermediary steps, such as symptom classification and disease agent recognition. This work emphasizes the pioneering utility of multi-task learning within digital decision support systems specifically designed for crop clinics. We propose to build a single model capable of dealing with multiple tasks from a single input, being those the indication if a leaf is healthy or not, which symptoms it has, and if the disease is caused by fungi or not, along with a pipeline to make use of its outputs through a mobile app.
Date of Conference: 29 October 2023 - 01 November 2023
Date Added to IEEE Xplore: 26 January 2024
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ISSN Information:

Conference Location: Recife-Pe, Brazil

References

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