Varietal Discrimination of Guava (Psidium Guajava) Leaves Using Multi Features Analysis

Figures & data

Table 1. Local Guava Varieties Cultivar in Pakistan.

S. No	General Variety	Local Variety Fruit Features	Leaf Image	Images Taken
1	Red Bangkok Guava	Red skin and flesh with a sweet in taste. Rounded in shape.		1220
2	China Surahi	Crunchy and oval good shape. Full sweet in taste and more juicy. Yellowish skin with white yellowish pulp.		1340
3	Moti Surahi	Crunchy, oval, and bulk of irregular shape. Light sweet in taste, juicy. Green skin with white flesh.		1440
4	Choti Surahi	Small oval shape with sweet taste. Yellowish skin and white flesh. Bunch of fruit gives it beauty.		1305
5	Golden Gola	Green and yellowish skin and white flesh. Light sweet, and oval in shape.		1289
6	China Gola	Green and yellowish skin and white flesh. Light sweet, and rounded in shape. Heavy and loaded fruit in a bunch. Farmer like it due to its more and round-the-year production with heavy temperature resistance.		1378
7	Multani Sada Gola	Crunchy, rounded, and irregular shape. Sweet in taste, and less juicy with more seeds. Heavy weather resistance. Green skin with white flesh.		1390
8	Sadda Bahar Gola	Yellowish skin with white Flesh. Sweet in taste. More fruit loaded over on stem.		1089
9	Larkana Surahi	Oval in shape Sweet in taste and juicy. yellowish-white and crunchy flesh.		1270
10	Black Guava	Fruit is oval rounded and pear-shaped Medicated fruit. Black leaves fruit with black flesh with some seeds. Sour sweet with leaves and fruits are mildly fragrant,		1270
11	Hyderabadi Safeeda	Oval in shape Yellowish skin and white-yellowish flesh Sweet in taste.		1367
12	Strawberry Pink Gola	Strawberry shape with some pink spot. Green skin with pink spot and pink Flesh inside. Medicated fruit. Light sweet, lightly crunchy and juicy.		1280

Figure 1. Proposed Methodology of Guava Leaf Discrimination by Classification.

Table 2. Hybrid Threshold Range-Based Segmentation (HTRS).

Algorithm 1: Hybrid Threshold Range Based Segmentation (HTRS) Input: Guava Image Dataset Parameter: GID {Guava Image Dataset} Output: Cluster with Area of Interest (AOI)
1:	Function HTRS(GIDataset)
2:	Begin
3:	For each image in GIDataset do
4:	GTR←compute the total pixel range
5:	GBP←calculate the guava image base hybrid range
6:	If GTR≠GBP, then
7:	GPC←find guava pixel cluster
8:	AOI←find area of interest
9:	end if
10:	end for
11:	return {GPC,AOI}
12:	end
13:	end function

Table 3. Correlation-based Feature Selection (CFS) List.

S.No	Features
1	Object-Column Coordinate
2	Area
3	Centroid (Row_Column)
4	Orientation (Axis_of_Least_Second_Moment
5	Histo-Mean
6	Histo-Standard Deviation
7	Histo_Skew
8	Histo_Energy
9	Histo_Entropy
10	Texure_enrgy_average (Texure_Distance:2_IndudeZeroPairs:False)
11	Texure_enrgy_range
12	Inertia_average (Texture_Distance:2_IndudeZeroPairs:False)
13	Correlation_range
14	Inverse_diff_average (Texture_Distance:2_IndudeZeroPairs:False)
15	Invers_diff_range
16	Texture_entropy_average (Texure_Distance:2_IndudeZeroPairs:False
17	Texture_entropy_range
18	Spectral_DC

Table 4. Training and Testing Data Distribution for Experiments.

Number of Varieties	Images Per Variety	Total Images	Per Image Derived Features	Total Derived Features in Images	Per Image Optimized Features	Total Optimized Features
12	200	2400	47	112800	18	43200

Table 5. Machine Learning Classifier Performance on Guava Leaves Images of Size (256 × 256).

Classifier	KappaValue	True Positive	False Positive	Receiver Operating Characteristics ROC	Mean Absolute Error MAE	Root- MeanSquare Error	Time (Sec)	Accuracy (%)
IBI	0.8595	0.871	0.012	0.93	0.0215	0.1465	0.01	87.125
RF	0.8432	0.856	0.013	0.989	0.0532	0.1439	3.76	85.625
Meta Bagging	0.7814	0.018	0.8	0.981	0.0653	0.1638	1.09	80.00

Figure 2. Machine Learning Classifier Performance on Guava Leaves Images of Size (256 × 256).

Figure 3. Confusion Matrix Graph of IBI Classifier on Leaves Image Size (256 × 256).

Table 6. Confusion Matrix Table of IBI Classifier on Leaves Image Size (256 × 256).

Classes	Bangkok Red	China Surahi	Moti Surahi	Choti Surahi	Golden Gola	China Gola	Multani Sada Gola	Sada Bahar Gola	Larkana Surahi	Black Guava	Hyderabadi Safeeda	Strawberry Pink Gola
Bangkok Red	154	34	11	1	0	0	0	0	0	0	0	0
China Surahi	40	126	26	1	0	2	0	1	3	1	0	0
Moti Surahi	12	25	147	0	0	1	0	0	0	2	2	11
Choti Surahi	0	0	0	195	0	3	1	0	1	0	0	0
Golden Gola	0	0	0	2	192	5	0	1	0	0	0	0
China Gola	0	0	0	0	6	194	0	0	0	0	0	0
Multani Sada Gola	0	0	0	0	1	0	198	0	0	0	1	0
Sadda Bahar Gola,	0	0	1	0	1	0	0	197	0	0	1	0
Larkana Surahi	0	0	0	0	0	0	0	1	198	0	0	1
Black Guava,	0	0	0	0	0	0	0	2	0	162	12	24
Hyderabadi Safeeda	0	0	1	1	0	0	1	1	1	7	176	12
Strawberry Pink Gola	0	0	10	1	0	0	0	0	0	25	12	152

Table 7. Machine Learning Classifier Performance on Guava Leaves Images of Size (512 × 512).

Classifier	Kappavalue	True positive	False positive	Receiver operating characteristics roc	Mean absolute error mae	Root- meansquare error	Time (sec)	Accuracy (%)
IBI	0.9164	0.923	0.007	0.958	0.0128	0.113	0.15	93.00
RF	0.8909	0.9	0.009	0.992	0.0483	0.1293	0.1004	90.03
Meta Bagging	0.8118	0.828	0.016	0.981	0.0669	0.1614	0.75	82.75

Figure 4. Confusion Matrix Graph of IBI Classifier on Leaves Image Size (512 × 512).

Figure 5. Confusion Matrix Graph of IBI Classifier on Image Size (512 × 512).

Table 8. Confusion Matrix Table for IBI classifier on Image Size (512 × 512).

Classes	Bangkok Red	China Surahi	Moti Surahi	Choti Surahi	Golden Gola	China Gola	Multani Sada Gola	Sadda Bahar Gola,	LarkanaSurahi	Black Guava,	Hyderabadi Safeeda	Strawberry Pink Gola
Bangkok Red	181	5	8	0	0	0	0	2	1	1	1	1
China Surahi	7	179	10	1	0	0	0	1	0	2	0	0
Moti Surahi	3	5	188	0	1	0	1	0	0	0	2	0
Choti Surahi	0	1	1	194	0	2	1	0	1	0	0	0
Golden Gola	0	0	0	2	191	5	1	0	0	0	1	0
China Gola	0	0	0	0	6	194	0	0	0	0	0	0
Multani Sada Gola	1	0	0	0	1	0	198	0	0	0	0	0
Sadda Bahar Gola,	0	1	0	0	1	0	0	193	1	0	2	2
Larkana Surahi	0	0	0	0	0	0	0	1	198	0	1	0
Black Guava,	0	0	1	0	0	0	0	2	0	161	10	26
Hyderabadi Safeeda	1	0	1	1	0	0	0	1	1	7	176	12
Strawberry Pink Gola	0	0	0	0	0	0	0	0	0	24	13	163

Table 9. Comparison of Proposed Approaches with Existing Approaches Working on Leaf Dataset.

Authers Year	Dataset	Categories	Features	Classifiers	Accuracy [%)
.13	Swedish Leaf	15	Combined Feature [Shape+Texure]	MTD+LBP+HF	85.74
8	Sweet Cherry Leaf Dataset	88	Shape + Texture+Venation	DCNN+Xception/MFSCIS	83.52
,31	Swedish Leaf	12	Texture	K-NN,Fuzzy	85.75
32	Ocotea [RNV]	31	Venation Pattern	Faxitron DX,	86.00
] 33	Self-Acquired Dataset	7	Color+Shape	ANN+MLP+Neuro-Fuzzy	88.43
34	Self-Acquired Dataset	4	LBP,LGP,NABP	DCNN,SVM	98
35	Self-Acquired Dataset	14	Leaf Phenotype	CRD	67
36	Self-Acquired Dataset	40	Color+Shape, Leaf Tip,Leaf Margin	PC,PCA,	79
37	Self-Acquired Dataset	5	Color + Shape	CNN	70
38	LifeCLEF-2015	7	Shape+color+Texture	ImageCLEF,KNN	97
39	PlantVillage,	4	Shape +Texture	KNN,CNN	95.
Proposed approach	Self-Acquired Dataset of Guava Leaves	12	Shape +Texture	IBI, RF, Meta Bagging	93.00

Figure 6. The Comparison Analysis Graph, on Twelve Varieties of Guava Leaves Images between (256 × 256) and (512 × 512).

Yang, C. Plant Leaf Recognition by Integrating Shape and Texture Features. Pattern Recognit. 2021, 112, 107809. DOI: 10.1016/j.patcog.2020.107809.

 Web of Science ®Google Scholar

Zhang, Y.; Peng, J.; Yuan, X.; Zhang, L.; Zhu, D.; Hong, P.; Liu, W.; Liu, Q.; Liu, W. MFCIS: An Automatic leaf-based Identification Pipeline for Plant Cultivars Using Deep Learning and Persistent Homology. Hortic. Res. 2021, 8(1), 1–14. DOI: 10.1038/s41438-021-00608-w.

 PubMed Web of Science ®Google Scholar

Wang, Z.; Sun, X.; Zhang, Y.; Ying, Z.; Ma, Y. Leaf Recognition Based on PCNN. Neural Comput. Appl. 2016, 27(4), 899–908. DOI: 10.1007/s00521-015-1904-1.

 Web of Science ®Google Scholar

Moraes, P. L. R. D.; Cruz, I. C. L. D.; Coan, A. I.; Tomazello Filho, M. Leaf Venation of Ocotea Species (Lauraceae) from the Reserva Natural Vale; Feddes Repertorium, 132(1): Linhares, Espírito Santo, Brazil, 2021; pp 41–64.

 Google Scholar

Pazoki, A.; Pazoki, Z.; Paknejad, F. Leaf Identification of Sesame Varieties Using Artificial Neural Networks (MLP and Neuro-Fuzzy). Int. J. Biosc.| IJB, 3(5): 108–116. 2013.

 Google Scholar

Howlader, M. R.; Habiba, U.; Faisal, R. H.; Rahman, M. M. (2019, February). Automatic Recognition of Guava Leaf Diseases Using Deep Convolution Neural Network. In 2019 international conference on electrical, computer and communication engineering (ECCE), Cox'sBazar, Bangladesh (pp. 1–5). IEEE.

 Google Scholar

Nagar, P. K.; Satodiya, B. N.; Prajapati, D. G.; Nagar, S. K.; Patel, K. V. (2018).Assessment of Genetic Variability and Morphological Screening of Guava (Psidium Guajava L.) Hybrids.

 Google Scholar

Bui, D. Q.; Tran, T. D.; Le, H. T. M.; Nguyen, T. T.; Tran, Y. T. O. Phenotypic Diversity Assessment of Guava (Psidium Guajava L.) Collection in Vietnam. Indian J. Agric. Res. 2021, 55(5), 511–518.

 Google Scholar

Srinivas, B.; Satheesh, P.; Rama Santosh Naidu, P.; Neelima, U. Prediction of Guava Plant Diseases Using Deep Learning. In ICCCE 2020 Lecture Notes in Electrical Engineering,vol 698. Springer: Singapore, 2021; pp 1495–1505. https://doi.org/10.1007/978-981-15-7961-5_135

 Google Scholar

Reyes, A. K.; Caicedo, J. C.; Camargo, J. E. Fine-tuning Deep Convolutional Networks for Plant Recognition; Colombia: CLEF (Working Notes), 1391, 2015; pp 467–475.

 Google Scholar

Thanh-Nhan Nguyen, T.; Le, T. L.; Vu, H.; Nguyen, H. H.; Hoang, V. S. (2017, April). A Combination of Deep Learning and hand-designed Feature for Plant Identification Based on Leaf and Flower Images. In Asian Conference on Intelligent Information and Database Systems (pp. 223–233). Springer, Cham.

 Google Scholar