Evaluation of waste recycling of fruits based on Support Vector Machine (SVM)

Figures & data

Figure 1. Example of linear classification

Figure 2. Example of multi-hyperplane classification

Figure 3. Example of marginal hyperplane classification

Table 1. Descriptive data (central, dispersal, distribution indices)

Numbers	170
Missed data	0
Average	45.01
Average standard error	.667
Median	44.00
Mode	33
Standard deviation	8.697
Variance	75.639
Angle	−.005
Angle standard error	.186
Elongation	−1.284
Elongation standard error	.370
Range	32
Minimum	29
Maximum	61
Summation	7651

Figure 4. The bar graph of distribution frequency of first stage scores

Table 2. The first-stage scores statistic sample characteristics

Score	Abundance	Variation percent	Distribution percent
29	2	1.2	1.2
31	4	2.4	3.5
33	18	10.6	14.1
34	4	2.4	16.5
35	6	3.5	20.0
36	1	.6	20.6
37	5	2.9	23.5
38	6	3.5	27.1
39	9	5.3	32.4
40	7	4.1	36.5
41	6	3.5	40.0
42	8	4.7	44.7
43	6	3.5	48.2
44	4	2.4	50.6
45	2	1.2	51.8
46	4	2.4	54.1
47	4	2.4	56.5
48	5	2.9	59.4
49	8	4.7	64.1
50	5	2.9	67.1
51	3	1.8	68.8
52	7	4.1	72.9
53	4	2.4	75.3
54	5	2.9	78.2
55	16	9.4	87.6
56	8	4.7	92.4
58	9	5.3	97.6
59	1	.6	98.2
60	2	1.2	99.4
61	1	.6	100.0
Total	170	100.0

Figure 5. The precision of determination parameters (C, γ) the RBF kernel by grid search method

Table 3. Comparison of kernel’s SVM accuracy based on the education delivered with the real results extracted from questionnaires

SVM kernel
RBF	Linear	Polynomial
98.8235%	100%	100%

Table 4. Exhibition of proposed method in improved recycling according to base estimate for SVM for crops

The proposed method for SVM	Recycling methods	Product name
Stocking Manganese Pulp with Wheat or Rice Straw to produce fresh grass	Stocking Manganese Pulp with Wheat or Rice Straw to produce fresh grass Soak the mango seed and remove tannin and cyanide for a chicken diet Mango seeds in a mixture of concentrates	Mango
Banana skin in the lactating cow diet	Stocking banana foliage to feed broiler chicks Stocking the banana foliage with wheat straw for lactation Banana skin in the lactating cow diet Banana dry skin for growing pigs and pregnant rabbits	Banana
Storing pineapple waste with straw for lactating cow	Replacing the forage section in the ruminant diet Storing pineapple waste with straw for lactating cow	pineapple
Dried carrots in chicken diet to improve egg yolk color	Dried carrots and its seeds for horse diet New carrots in cow diet to improve reproductive performance of dry carrot in chicken diet to improve egg yolk color Carrot Pulp For Soluble Sugar Production	carrot
Feeding lactating cows and improving the health of the milk	Feeding lactating cows and improving the health of the milk Storing potato tubers with chopped forage for feeding poultry Baked potatoes for feeding cock	potato

Table 5. Recycling methods

Energy recycling method	Mechanical recycling method	Chemical recycling method	Row
Energy recovery involves complete and partial oxidation, production of heat, power and gas fuels, oil and ash, and so on. According to the LCA, the environmental sustainability is higher in this method.Energy recovery often has little initial investment cost. The burned plants offer a lot of energy depending on the quality. Given the high thermal value of raw materials, it is an optimal waste management method. Energy recovery generates a significant amount of petrochemicals and energy in the form of heat and steam. The lack of flammable gases and highly toxic pollutants Ability to increase efficiency by using combination methods The energy conversion operation is done in each location, so it has a general application	Basically, there is no need for advanced design and devices, it is cheap and simple, and it is currently the most commonly used method in the world. Applicable and divided into several sections and each section is economically valuable.Unlike other recycling methods, it can also be used for low volumes of waste.Depending on the degree of separation methods for product production, it can be generalized from industrial applications to high purity hygiene.	There is less need for separation of waste materials. In fact, sometimes dirty wastes that cannot be mechanically recovered can be recycled in this way. Sometimes used to blend plastic wastes that allows the removal of valuable products from one or more plastics in the waste.Chemical recycling makes it possible for monomers to be restored to a degree of purity that is once again used to produce polymers that even consume food. Recycled products re-enter the production cycle and compete with new materials.Chemical recovery can also be valuable for thermosetting polymers. The value of the products of chemical recovery compared to the combustion phenomenon is more economical. Because of the fact that products produced from chemical recycling can be used without further purification, economically, one of the important advantages of this method is considered.	Advantages
Need high and up-to-date technical knowledge. The equipment needs to be efficient and fit the desired design.	Not controllable and the molecular mass of the product is low. Because the execution of mechanical operations such as crushing to some extent leads to the destruction of polymers.It is costly to apply separation methods to separate the plastics. In terms of economic value, the materials obtained are lower than other methods because of their lower purity. The water and waste from the mechanical recycling process must be recycled again. The loss of mechanical properties occurs at each heating and shaping time.	In the chemical recycling of the molecule, the molecule is lost to mechanical recycling. For the molecular destruction of the chains, it is necessary to apply extreme acidic conditions and play and temperature, that is, a special chemical design and operating unit that is required to create monomers with a degree of polymerization, which is costly and costly. The purification and recycling of water and solvent and reactants need to be recycled, which also removes materials that are also produced as sub-processes. In order to reduce costs, and to make a chemical unit economically viable, it must enter a large amount of plastic waste continuously.Useful monomers and oligomers are only obtained from a limited amount of polymers that can lead to selective reactions under selective reactions. As can be seen from the above, chemical recycling for low-consumption polymers and specific uses, such as polytetrafluoroethylene PTEF and acetal resins, is not economically feasible.	disadvantages

Table 6. Descriptive data of second step (central, dispersion, distribution)

Numbers	170
Missed Data	0
Average	49.32
Average standard errors	0.659
Median	50.5
Mode	44a
Standard deviation	8.596
Variance	73.889
Angle	−0.573
Angle standard error	0.186
Elongation	−0.34
Elongation standard error	0.37
Range	37
Minimum	28
Maximum	65
Total	8385

Figure 6. Bar graph of abundance distribution in the second step

Table 7. Frequency, percentage, and percentage of dispersion of the participants’ scores in the second phase

Score	Abundance	Abundance percent	Variation percent	Dispersion percent
28	3	1.8	1.8	1.8
29	2	1.2	1.2	2.9
31	1	.6	.6	3.5
32	1	.6	.6	4.1
33	2	1.2	1.2	5.3
34	2	1.2	1.2	6.5
35	4	2.4	2.4	8.8
36	3	1.8	1.8	10.6
37	4	2.4	2.4	12.9
38	3	1.8	1.8	14.7
39	1	.6	.6	15.3
41	2	1.2	1.2	16.5
42	3	1.8	1.8	18.2
43	7	4.1	4.1	22.4
44	10	5.9	5.9	28.2
45	6	3.5	3.5	31.8
46	6	3.5	3.5	35.3
47	6	3.5	3.5	38.8
48	4	2.4	2.4	41.2
49	6	3.5	3.5	44.7
50	9	5.3	5.3	50.0
51	7	4.1	4.1	54.1
52	8	4.7	4.7	58.8
53	8	4.7	4.7	63.5
54	4	2.4	2.4	65.9
55	10	5.9	5.9	71.8
56	9	5.3	5.3	77.1
57	8	4.7	4.7	81.8
58	7	4.1	4.1	85.9
59	7	4.1	4.1	90.0
60	7	4.1	4.1	94.1
61	3	1.8	1.8	95.9
62	5	2.9	2.9	98.8
64	1	.6	.6	99.4
65	1	.6	.6	100.0
Total	170	100.0	100.0

Figure 7. Precision determining the (C, γ) parameters of RBF kernel by grid search method

Table 8. Comparison of kernel’s SVM accuracy based on the education delivered with the real results extracted from questionnaires

Kernel
RBF	Linear	Polynomial
98.82%	96.47%	98.82%

Table 9. Correlation

	Pair differences					T	df	Sig. (2.tabled)
	Mean	Std. Deviation	Std. error mean	99% confidence interval of the difference
				Lower	Upper
Pair 1 pretest-past test	−4.318	4.612	0.354	−5.016	−3.619	−12.206	169	0.000