Transfer Learning-Based Framework for Classification of Pest in Tomato Plants

Figures & data

Table 5. Benchmarking of our approach with literature on tomato pest classification.

Authors (year)	Data Size	#Classes	Methodology	Classification Accuracy (%)
Fuentes et al. (2017)	5,000	9 (7 diseases and 2 pest)	Faster R-CNN, R-FCN, SSD	85.98 (R-FCN)
Shijie et al. (2017)	7,040	10 (7 diseases and 3 pest)	VGG16 with transfer learning	89
Nieuwenhuizen et al. (2018)	6,900	4 (pest)	Faster R-CNN	87.4
Gutierrez et al. (2019)	4,331	4 (2 insects and 2 their eggs)	KNN,MLP, Faster R-CNN, SSD	82.51 (Faster-RCNN)
Our Approach	859	10 (pests)	Transfer Learning (DenseNet169)	88.83

Table 1. Details of pest dataset.

Figure 1. The concept of transfer learning.

Table 2. Details of pre-trained deep CNN models.

Deep CNN Model	Input Shape	#Convolution Layers	#Pooling Layers	Non-trainable Parameter	Trainable Parameter
VGG16	(224,224,3)	13	5	134,260,544	40,970
VGG19	(224,224,3)	16	5	139,570,240	40,970
ResNet50V2	(224,224,3)	51	1	23,564,800	20,490
ResNet101V2	(224,224,3)	105	1	42,626,560	20,490
ResNet152V2	(224,224,3)	151	4	58,331,648	20,490
Inception	(299,299,3)	83	12	21,802,784	20,490
Xception	(299,299,3)	37	4	20,861,480	20,490
InceptionResNetV2	(299,299,3)	240	6	54,336,736	15,370
MobileNet	(224,224,3)	14	1	4,253,864	10,010
DenseNet121	(224,224,3)	114	4	7,037,504	10,250
DenseNet169	(224,224,3)	152	5	12,642,880	16,650
DenseNet201	(224,224,3)	196	5	18,321,984	19,210
NASNet Mobile	(224,224,3)	183	57	4,269,716	10,570
NASNetLarge	(224,224,3)	227	76	84,916,818	40,330
MobileNetV2	(224,224,3)	34	1	2,257,984	12,810

Table 3. Classification results obtained using 15 pre-trained models on tomato pest dataset.

Deep CNN model	T1	T2	T3	T4	T5	OA±STD
VGG16	79.65	82.55	79.65	83.72	84.30	81.97 ± 2.21
VGG19	85.46	84.30	83.13	83.72	86.04	84.53 ± 1.20
ResNet50V2	79.06	84.88	86.62	83.72	88.37	88.37 ± 3.52
ResNet101V2	82.55	87.79	88.95	88.37	90.11	87.55 ± 2.92
ResNet152V2	80.81	87.20	86.62	87.79	88.37	86.16 ± 3.05
InceptionV3	84.88	86.04	79.65	86.04	81.97	83.72 ± 2.81
Xception	84.30	82.55	88.95	81.39	85.46	84.53 ± 2.92
InceptionResNetV2	81.97	85.46	86.04	81.97	84.88	84.06 ± 1.95
MobileNet	79.06	81.39	86.62	83.13	88.95	83.83 ± 3.97
DenseNet121	87.79	83.13	84.88	86.04	76.74	88.48 ± 2.67
DenseNet169	87.79	90.69	90.11	86.62	88.95	88.83 ± 1.48
DenseNet201	84.30	83.13	75.58	83.72	87.79	82.90 ± 4.47
NASNetLarge	79.65	88.37	77.32	82.55	84.30	82.44 ± 4.26
NASNetMobile	74.41	74.41	74.41	76.16	80.81	76.04 ± 2.77
MobileNetV2	76.16	75.58	72.09	73.25	83.13	76.04 ± 4.29

Table 4. Other performance parameters obtained using denseNet169 model for tomato pest dataset.

Classes	Class Accuracy (%)	Precision (%)	Sensitivity (%)	Specificity (%)	F1-Score (%)
Pest 1	97.3	87.0	89.7	98.4	88.0
Pest 2	95.6	74.2	92.2	96.3	81.9
Pest 3	97.3	86.6	82.0	98.8	84.1
Pest 4	100	100	100	100	100
Pest 5	96.5	85.0	85.6	97.9	85.1
Pest 6	96.8	94.4	70.9	99.6	80.0
Pest 7	97.0	87.8	86.9	98.3	87.3
Pest 8	98.3	85.3	95.6	98.5	90.0
Pest 9	98.9	100	88.6	100	93.3
Pest 10	98.8	93.3	95.4	99.2	94.2

Figure 2. Performance comparison of 15 pre-trained deep CNN models on tomato pest dataset.

Fuentes, A., S. Yoon, S. Kim, and D. Park. 2017. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17 (9):2022. doi:10.3390/s17092022.

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Shijie, J., J. Peiyi, and H. Siping. 2017 October. Automatic detection of tomato diseases and pests based on leaf images. In 2017 Chinese automation congress (CAC), Jinan, China, 2537–2510. IEEE.

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Nieuwenhuizen, A. T., J. Hemming, and H. Suh. 2018. Detection and classification of insects on stick-traps in a tomato crop using Faster R-CNN. Proceedings of the Netherlands Conference on Computer Vision NCCV18, Netherlands (pp. 1-4).

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Gutierrez, A., A. Ansuategi, L. Susperregi, C. Tubío, I. Rankić, and L. Lenža. 2019. A benchmarking of learning strategies for pest detection and identification on tomato plants for autonomous scouting robots using internal databases. Journal of Sensors 2019.

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