Guilherme Martins / Tomato_leaf_disease_detection Public

Target: Arduino Nano 33 BLE Sense (Cortex-M4F 64MHz) Clone this project

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Tomato_leaf_disease_detection

Este projeto utiliza os conceitos de TinyML para identificar doenças presentes em folhas de tomate a partir de fotografias, com intuito de auxiliar no monitoramento agrícola.

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Data (5,991 items)

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d989b5d3-715f-4d85-8475-0a2ec3beaa98___RS_Late.B 5022

ID: 1078071558

Label: late_blight

172458d3-8e11-4e75-9f39-d7e96e885108___GCREC_Bact.Sp 3071

ID: 1078048693

Label: bacterial_spot

Tomato___Tomato_mosaic_virus_original_656459eb-a35c-426b-8ee9-45d4a4076b5b___PSU_CG 2228.JPG_9e3a9abd-c880-4add-8dad-6ef1207a0537

ID: 1078080552

Label: mosaic_virus

b7f7a13f-b7c0-4923-aaba-c270ce583b66___RS_Late.B 6686

ID: 1078070279

Label: late_blight

c298eb46-4b8f-4a5b-a819-ceabb2fc7211___RS_HL 9677

ID: 1078066506

Label: healthy

fc6a1ebf-65e5-45f1-be33-1f2bd3dc4d71___GCREC_Bact.Sp 3511

ID: 1078051602

Label: bacterial_spot

ea815fb4-7c6f-4088-94d0-59609294c65e___RS_HL 9785

ID: 1078066738

Label: healthy

Tomato___Tomato_mosaic_virus_original_efd2cbd0-24dc-4c09-92b1-37963ae84721___PSU_CG 2379.JPG_488824ab-9209-44f6-b32e-576015657a7e

ID: 1078081789

Label: mosaic_virus

Model accuracy (Unoptimized (float32))

Validation set

86.5%

Test set

80.7%

On-device performance (Arduino Nano 33 BLE Sense (Cortex-M4F 64MHz), Quantized (int8))

Latency

1014 ms.

Peak RAM usage

70.9K

Flash usage

85.1K

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Dataset summary

Data collected

5,991 items

Labels

bacterial_spot, early_blight, healthy, late_blight, mosaic_virus, septoria_leaf_spot

Project info

Project ID	439395
License	Apache 2.0
No. of views	36,978
No. of clones	1