Multispectral images for discrimination of sources and doses of fertilizer in coffee plants

Rezende, Camila Isabel Pereira; Assis, Gleice Aparecida de; Martins, George Deroco; Carvalho, Fábio Janoni; Franco, Miguel Henrique Rosa; Araújo, Nathalia Oliveira de

doi:10.1590/0034-737X202370030006

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Crop Production • Rev. Ceres 70 (3) • May-Jun 2023 • https://doi.org/10.1590/0034-737X202370030006 copy

Multispectral images for discrimination of sources and doses of fertilizer in coffee plants¹ 1 This work is part of the first author’s Master Dissertation.

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Remote monitoring of the management of coffee crops is necessary as the demand in decision-making, where the aim is to rise production based on sustainable management is in a constant growth. In this work, it was evaluated the potential of images obtained by low-cost sensors in the discrimination of sources and doses of mineral and organomineral fertilizers in coffee. The experimental design was in randomized blocks, with five blocks and six treatments, as follows: (T1) - 100% of the organomineral treatment; (T2) - 70% of the organomineral treatment; (T3) - 50% of the organomineral treatment; (T4) - 100% of mineral fertilization; (T5) - standard treatment of the farm and (T6) - 70% of mineral fertilization. After management, we used the Mapir 3 Survey3W camera coupled to an ARP drone – Phantom4 to take images of the experiment over a 12-month vegetative period. Combined with image taking, it was collected agronomic parameters of coffee growth and productivity for two crops and concluded that different fertilization doses did not significantly affect the analyzed parameters. Based on the supervised classification of multispectral images, it was possible to discriminate treatments with a higher degree of accuracy (86.66% accuracy) than when analyzing coffee growth parameters.

Keywords
Coffea arabica L.; fertilization management; low-cost remote monitoring; treatment discrimination

Means of the Leaf and Soil Nutritional contents
Treatment	Foliar means			Soil means
Treatment	N(g.kg^-1)	P(g.kg^-1)	K (g.kg^-1)	P meh^-1 mg kg^-1	K mg kg^-1	Mgcmol_c dm^-3	Cacmolc dm^-3
T1	50.7 a	1.6	21.5	3.2	164.9	1.2 abc	3.7 ab
T2	49.2 a	1.8	23.7	7.8	157.3	1.4 ab	3.7 ab
T3	37.0 b	1.8	24.1	3.3	159.7	1.2 bc	3.2 b
T4	35.7 b	1.6	22.9	2.9	157.7	1.1 c	3.1 b
T5	31.1 bc	1.7	22.9	3.8	159.6	1.5 a	3.9 ab
T6	28.2 c	1.8	24.4	5.1	155.3	1.4 ab	4.0 a
Means of soil chemical parameters
Soil means
Treatment	pH H₂O	H+Al cmolc dm^-3	MO g kg^-1		SB cmol_c dm^-3	T cmol_c dm^-3	V%
T1	6.66 b	1.44	25.1		5.46 abc	6.89 ab	78.9
T2	6.79 ab	1.35	24.8		5.66 ab	7.01 ab	80.6
T3	6.57 b	1.38	25.2		4.86 bc	6.24 bc	77.7
T4	6.77 ab	1.38	25.4		4.68 c	6.06 c	77.2
T5	6.94 a	1.34	26.1		5.87 a	7.22 a	81.3
T6	6.97 a	1.29	24.7		5.95 a	7.23 a	82.0

Means of the growth parameter
Treatment	Height	Crown diameter	Number of nodes	Length of branches
T1	2.00	1.89	8.28 ab	0.20
T2	1.98	1.91	8.00 ab	0.19
T3	1.98	1.87	8.19 ab	0.18
T4	2.02	2.70	8.21 ab	0.19
T5	1.96	1.87	8.84 a	0.21
T6	2.03	1.92	7.52 b	0.18
Means of the chlorophyll indices and productivity
Treatment	Chlorophyl	Productivity 2020	Productivity 2021	20/21 Mean productivity
T1	64.15	21.92 a	78.92	50.40
T2	63.11	17.44 ab	71.80	44.62
T3	62.65	20.00 ab	72.38	46.20
T4	62.56	17.04 ab	82.22	49.62
T5	65.09	9.28 b	88.12	48.70
T6	63.08	19.84 ab	73.00	46.42

Classified subsets	1^st Evaluation 150 DAD		2^nd Evaluation 210 DAD		6^th Evaluation 460 DAD		7^th Evaluation 560 DAD
	Random Forest
	EG	K	EG	K	EG	K	EG	K
Agronomic parameter	73.33%	0.60	63.33%	0.45	70.00%	0.55	73.33%	0.60
Plant Height	50.00%	0.25	63.33%	0.45	36.66%	0.05	56.66%	0.35
CD, NN, CM	73.33%	0.60	66.66%	0.50	70.00%	0.55	76.66%	0.65
Chlorophyl	30.00%	-0.05-	63.33%	0.45	66.66%	0.50	70.00%	0.55
RGN and indices	73.33%	0.60	70.00%	0.55	73.33%	0.60	86.66%	0.80
RGN	26.66%	-0.10-	66.66%	0.50	73.33%	0.60	86.66%	0.80
Indices	40.00%	0.10	66.66%	0.50	66.66%	0.50	73.33%	0.60
Confusion matrix on the 1^rst evaluation day (150 DAD)
Class	Agronomic parameter					RGN and Vegetation indices
Class	a	b	c			A	b	C
a	10	0	0			9	1	0
b	1	4	5			3	7	0
c	0	2	8			1	3	6
Confusion matrix on the 2^nd evaluation day (210 DAD)
a	9	1	0			9	0	1
b	5	2	3			3	6	1
c	1	1	3			1	3	6
Confusion matrix on the 6^th evaluation day (460 DAD)
a	9	1	0			9	1	0
b	3	5	2			1	6	3
c	3	0	7			0	3	7
Confusion matrix on the 7^th evaluation day (560 DAD)
a	9	1	0			10	0	0
b	2	7	1			0	8	2
c	2	2	6			2	0	8

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[1] *Corresponding author: gleice@ufu.br

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Multispectral images for discrimination of sources and doses of fertilizer in coffee plants1 1 This work is part of the first author’s Master Dissertation.

ABSTRACT

Multispectral images for discrimination of sources and doses of fertilizer in coffee plants¹ 1 This work is part of the first author’s Master Dissertation.