View Proposal

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Proposer

Alessandro Suglia

Title

Vision and Language Models for Plant Disease Detection

Goal

To study, design and implement novel Vision and Language models for plant inspection

Description

Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.

Resources

Background

Url

Difficulty Level

Challenging

Ethical Approval

None

Number Of Students

1

Supervisor

Alessandro Suglia

Keywords

deep learning, neural networks, language models, computer vision

Degrees

Bachelor of Science in Computer Science