Table 5.

Classification performance of traditional machine learning and proposed 1D-CNN model for pesticide residues.

Model	Class	Accuracy(%)	Precision(%)	Recall(%)	F1-score
RF	all	82.50	82.70	82.50	0.8251
	none	82.50	84.62	82.50	0.8354
	acetamiprid	85.00	82.93	85.00	0.8395
	malathion	87.50	79.55	87.50	0.8333
	difenoconazole	77.50	77.50	77.50	0.7750
	beta-cypermethrin	80.00	88.89	80.00	0.8421
KNN	all	85.50	85.67	85.50	0.8553
	none	87.50	87.50	87.50	0.8750
	acetamiprid	87.50	81.40	87.50	0.8434
	malathion	87.50	85.37	87.50	0.8642
	difenoconazole	77.50	79.49	77.50	0.7848
	beta-cypermethrin	87.50	94.59	87.50	0.9091
Proposed 1D-CNN	all	94.00	94.06	94.00	0.9396
	none	90.00	90.00	90.00	0.9000
	acetamiprid	95.00	95.00	95.00	0.9500
	malathion	100.00	95.24	100.00	0.9756
	difenoconazole	97.50	92.86	97.50	0.9512
	beta-cypermethrin	87.50	97.22	87.50	0.9211