Research Article
Internet of Things-Based Crop Classification Model Using Deep Learning for Indirect Solar Drying
Table 1
performance of indirect solar dyer for various crops.
| Mass of the product | Reduced to | Moisture content | Final moisture content | Crop | Reference |
| 2 kg | .5628 kg | 356% | 16.3292% 19.4736% 21.1592% 31.1582% 42.37% (open sun drying) | Banana | [16] | 1.8 kg | 0.14 kg | 93% | 10% | Tomato | [25] | .8864 kg | | 86% | 8.12% | Apple | [26] | 1 kg | | 83% | 12% | Papad | [27] | | | 77.75% | 14.53% | Grapes | [28] | 2 kg | | 78% | 18.05% | Banana | [16] | 7.45 kg | | 93.81% | 6.54% | Tomato | [29] | 10 kg | | 80% | 18% | Grapes | [30] | 10 kg | | 80% | 13% | Apricots | [30] | 50 kg | | 60% | 12% | Silk cocoon | [31] | | | 77.2% | 34.98% | Banana | [32] | 38.4 kg | | 90.21% | 10% | Red chili | [33] | 40 kg | | 80% | 10% | Red chili | [34] |
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