Papers › Nutrispace: A novel color space to enhance deep learning based early detection of...
Nutrispace: A novel color space to enhance deep learning based early detection of cucurbits nutritional deficiency
Nabil Anan Orka, Ehtashamul Haque, M Nazim Uddin, Tofael Ahamed
Early detection of plant nutritional deficiencies, followed by prompt corrective measures, is crucial for maintaining crop yield and produce quality. However, detecting these early signs in plant leaves often proves challenging, even with computer-aided diagnostic tools, because of their subtlety. As a solution, this study introduces Nutrispace, a new color space that enhances deep learning-based nutritional stress recognition by accentuating early signs of nutritional deficiency in leaf images. In this study, we evaluated Nutrispace’s efficacy by comparing its performance to RGB, HSV, and CIELAB using three lightweight classifiers: EfficientNetB0, MobileNetV2, and DenseNet121. To better understand Nutrispace’s effective range, we tested these classifiers with four image input sizes: 32 × 32, 64 × 64, 128 × 128, and 256 × 256. Our test dataset comprised images of ash gourd (Benincasa hispida), bitter gourd (Momordica charantia), and snake gourd (Trichosanthes cucumerina) leaves with early nitrogen and potassium deficiencies, as well as healthy controls. Our findings show that Nutrispace consistently improved accuracy across all 12 test cases, with improvements ranging from 1% to more than 8% compared to RGB. The performance improvement was more significant for higher-resolution cases, with Nutrispace achieving the maximum test accuracy of 90.62% on 256 × 256 images. Overall, Nutrispace performed effectively, irrespective of classifier structure and input size
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | EarlyNSD | DenseNet121_256x256_Nutrispace | Test accuracy | 0.9062 | #1 of 3 | Archive leaderboard | report |
| Image Classification | EarlyNSD | DenseNet121_256x256_Nutrispace | Test f1 | 0.9050 | #1 of 3 | Archive leaderboard | report |
| Image Classification | EarlyNSD | EfficientNetB0_256x256_Nutrispace | Test accuracy | 0.8914 | #2 of 3 | Archive leaderboard | report |
| Image Classification | EarlyNSD | EfficientNetB0_256x256_Nutrispace | Test f1 | 0.8899 | #2 of 3 | Archive leaderboard | report |
| Image Classification | EarlyNSD | MobileNetV2_256x256_Nutrispace | Test accuracy | 0.8828 | #3 of 3 | Archive leaderboard | report |
| Image Classification | EarlyNSD | MobileNetV2_256x256_Nutrispace | Test f1 | 0.8766 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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