The Use of Sentinel-2 Vegetation Indices Imagery in Detecting the Effect of Plant Distance to the Productivity of Corn Crops

. There are several variations in planting distance used for corn plants, including 70 × 40 cm, 70 × 20 cm, and irregular. This affects the productivity of corn plants because of competition between corn plants. Satellite imagery, such as Sentinel-2 imagery, can be used to observe this effect. This research aimed to determine the effectiveness of Sentinel-2 satellite imagery in detecting the effect of plant distance on corn plant productivity. This research used Modified Red-edge Simple Ratio (mRE-SR) Vegetation Index imagery from Sentinel-2 imagery. The three plant distances used were 70 × 40 cm, 70 × 20 cm, and irregular. The images used corresponded to the corn planting times at research location, i.e. 10 and 20 December 2022, 14 January 2023, 28 February 2023 and 15 March 2023. Linear regression analysis was carried out to test the correlation between corn plant productivity of each plant distance and the mRE-SR Vegetation Index image. The results of this research indicated that the effect of plant distance on corn plant productivity can be studied using mRE-SR Sentinel-2 imagery.


Intoduction
Corn is one of the crops that is widely used by Indonesian people, such as for feeding animal, processed into flour and other basic ingredients for food industry.The whole parts of corn plants can be used.Apart of the fruits, the stems and leaves are used for animal feed.The level of production of corn plants is determined by the interaction of genetic factors of superior varieties with their growing environment such as soil fertility, water availability and plant management [1].*Corresponding author: suhardi@unhas.ac.id , 05001 (2024) BIO Web of Conferences https://doi.org/10.1051/bioconf/2024960500196 2 nd  UICAT 2023 In Indonesia, corns are planted at different planting distance, such as 20×70, 20×60, 20×80cm or irregularly.Many farmers use irregular planting distances.As a result, population size of the corn plants is not optimal.Applying plant spacing result in several advantages, such as efficient land use, population increase, and easy plant management, such as in applying fertilizers and pesticides [2].
The process of plant growth affects the amount or value of productivity produced.One of the factors that causes a decrease in plant productivity is plant spacing.Using plant spacing that is too close will affect productivity because there is competition between the plants to obtain water, nutrients, oxygen and sunlight, which results in decreased productivity.On the other hand, if planting distance is too sparse, land use will be inefficient so that the resulting population will be small or productivity will be low [3].
Currently, remote sensing application has developed rapidly in the agricultural sector [4].Remote sensing is used to determine the physical conditions of land.An example of technological development is satellites with different levels of accuracy.One of the satellites that is widely used today is Sentinel which has an increasing level of accuracy.
There are several problems that often occur on land, such as pest attacks, disease, drought and plant spacing that is too far apart.The vegetation index is a parameter used to determine the amount of greenness in an area.The processed vegetation index data contains information about the physical condition of plants (greenness) so that preventive measures can be taken if problems occur with plants [5].
This research aimed to identify the use of vegetation index of sentinel-2 imagery in detecting the effect of planting distance on corn plant productivity.Through this research, information about the ideal planting distance for corn plants can be obtained.

Data acquisitions and processing
Primary data consisted of corn field data, plant age and productivity of the NK7328 sumo hybrid corn.Meanwhile, Sentinel satellite imagery data was downloaded from https://scihub.copernicus.euon the date close to the corn crop harvest date, i.e. 28 February and 15 March 2023.In addition, imagery data dated on 10 December 2022, 20 December 2022 and 14 January 2023 were used to see changes in vegetation index values in the period corn growth.
The data processing began with atmospheric correction to clear and increase imagery accuracy in Quantum GIS software.After that, the imagery was cropped by adding study areas shp data, then selecting raster > extraction > clip raster by musk layer.Then, mRE-SR vegetation index imagery was generated using a raster calculator by using equation 1, where NIR and Red Edge were imagery band of the satellite data.

Data Analysis
Data analysis was used to determine the relationship between vegetation index Sentinel-2 and corn productivity.Estimation of productivity was conducted by mathematically modelling the relationship between vegetation index values and productivity data.The best mathematical model resulted from regression analysis was used to estimate the productivity by entering the vegetation index values of each plot.

mRE-SR vegetation index value
The minimum, maximum, and average spectral value of mRE-SR vegetation index can be seen in Table 1.mRE-SR index had the highest average value on 28 February 2023, i.e. 0.6921 and the lowest average value on 10 December 2022, i.e. 0.4541.According to [6], the mRE-SR spectral value of 0.6921 was included in the dense density class.Plots 1, 2, 7, 8 and 9 were plots used to observe the level of greenness at each growth phase.Corn plants were in the germination phase on 10 December 2022 (figure 2a).The vegetative phase of the corn plant was on 20 December 2022 (figure 2b), as a result, greenness of the plant began to increase.At the end of the vegetative phase the greenness of plot 3 and plots 1 and 2 decreased, because it was attacked by pests and weeds respectively.On 28 February 2023 (figure 2c) corn plants had very high greenness, because it was in the generative or seed

Relationship between mRE-SR Index and Maize Crop Productivity
The irregular spacing produced the lowest productivity, i.e. 4.65 ton/ha, while 70×40 cm had the highest productivity, i.e. 5.29 ton/ha.Planting distance 70×20 cm had productivity of 5.03 ton/ha (Table 2).Wahyudi and [7], said that the higher the population density of corn plants, the potential to produce higher productivity.Analysis of the relationship between the mRE-SR sentinel vegetation index and corn plant productivity can be seen in Figure 3.The results of the regression analysis on the mRE-SR index produced the highest R 2 value at a plant spacing of 70×20 cm with a value of 0.8407, followed by plant spacing of 70×40 with a value of 0.7651 and irregular plant spacing with a value of 0.6557.Thus, planting distance of 70×20 is the best because the correlation between productivity and an index value was close to one.

Corn Productivity Estimation
The estimation of corn crop productivity was done using the mathematical model with the highest determinant coefficient (R 2 ), i.e. mathematical model of planting distance of 70×20 cm.The result of this estimation and its difference with actual productivity can be seen in Table 4.It can be seen that comparing all the planting distance, the estimation productivity results of planting distance 70×20 cm had the lowest differences with the actual productivity.While on average, planting distance of 70×40 cm had the highest differences.

Conclusion
Based on the results of this research, it can be concluded that the effect of planting distance on corn crops' productivity can be identified using sentinel-2 satellite imagery using the mRE-SR vegetation index.The estimation productivity results of planting distance 70×20 cm had the lowest differences with the actual productivity.Based on regression analysis, the relationship between planting distance and crop productivity is strongest at a planting distance of 70×20 cm.

Fig. 1 .
Fig. 1.Map of the research location

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05001 (2024) BIO Web of Conferences https://doi.org/10.1051/bioconf/2024960500196 2 nd UICAT 2023 filling phase.The final phase of the corn plant was on 15 March 2023 (image d), where the greenness of the plant decreased due to the plant started to turn brown and dry.Harvesting was carried out on 22 March 2023, but the imagery data at this time could not be used because it was covered by clouds.

Table 1 .
Spectral results of mRE-SR Fig. 2. mRE-SR Index vegetation values throughout the growth period of corn crops

Table 2 .
Productivity results of each study plot

Table 3 .
Estimation of corn production using the mRE-SR vegetation index