Detection of land under renovation with Artificial Intelligence

La Paz. 2021

In this article, we will see how to carry out land detection with Artificial Intelligence in order to identify the land (lots) available for production (under renovation). In this way we intend to improve the costs and efficiency associated with its detection and exploitation.

We understand by land subdivision the process of dividing the land, regardless of the number of resulting properties, whose project contemplates the opening of new public roads, and their corresponding urbanization. However, we will analyze the land for its agricultural production.

The data are extracted from the geographic database of production monitoring of sugarcane growers in the northern area of Santa Cruz. All this is managed in a CADASTRE.

This geographic information has related tabulated information:

Then you can have a visualization of the status of the lots, whether they are in production (with coverage) or under renewal (without coverage) through the polygons that limit the lots and the satellite images.

It is these images at different times of the year that allow us to visually analyze whether the lots are being renovated or not.

Problem description:

Determining whether or not a lot is under renovation is important since it is a variable when calculating the production and performance of the properties of each sugarcane grower, and for this the following drawbacks arise:

• Field inspections and satellite images are lacking.
• Susceptible to errors.
• Too much time invested.

Objective:

Determine if a sugarcane crop plot is under renewal through the change in coverage based on NDVI statistical data from the last 12 months with Machine Learning.

Solution proposal.

Satellite images can be processed to obtain certain indices. The index of interest to observe if a lot is under renovation is called NDVI (Normalized Difference Vegetation Index):

You can see the lots with coverage in green, and those without coverage in red, this color thing is just symbology. What you really have with NDVI is a matrix of pixels:

Each pixel has values between -1 and 1; being -1 completely bare soils, and 1 soils with vegetal cover.

Then, the descriptive statistics of each lot can be obtained, and through its mean and standard deviation determine whether a lot is under renovation or not.

As can be seen, lots with coverage have a mean close to 1 and a low standard deviation, and those without coverage have a mean close to 0 and also a low standard deviation. The standard deviation is important since it determines that the pixel values in each lot are uniform.

Feature engineering.

• The field was identified as a target Variety which the variety planted in that lot is stored, but if the lot is under renewal, it has the label “Renewal”, we also change the name of the column to Renewal.
• We convert the Renewal field from categorical to boolean.
• We united the 13 datasets (1 from the cadastre and 12 from the statistical values of the last year) into a single dataset for better use.

Data Visualization

• Number of registrations per Renewal.

• Number of records per management.

• Number of records per hectare.

• Correlation matrix.

• Visualization of the target balance.

Dimension reduction through PCA.

Dimension reduction was carried out through PCA to two main components, and a difference can be seen between the records:

Model training

Three types of models were tested, Cross Validation was also implemented. The results were the following:

• Logistic Regression

• Random Forest

• SVM

Choosing the best model

Based on the results obtained, we will now choose the most appropriate Artificial Intelligence model for the detection of land under renovation:

It is observed that the 3 selected models approach the same probability 0.93, however, SVM has a better score.

It was also decided to apply the ROC Curve, and the following result was obtained:

In this case, Random Forest is the one that has the greatest area under the curve, therefore, SVM and Random Forest are the best models to consider for the classification of lots under renovation.

Authors of the project.

• Bismark Socompi.
• Ruth Paola Vedia
• Christian Vargas

Saturdays.AI

• Machine Learning
• Lots
• Renovacipon
• Logistic Regression
• Svm

WRITTEN BY

Bismark Socompi Rodriguez

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