Predictive fertilization models for potato crops using machine learning techniques in Moroccan Gharb region - Optimal NPK prediction model development - hardware implementation

Aug 4, 2026

Upload by Anda

Adequate nutrient prescription for potato production remains difficult due to interacting factors such as weather, soil conditions, land management practices, genotypes, and pest and disease pressure. This study develops an accurate machine learning model to determine optimal nitrogen, phosphorus, and potassium levels for high-quality and high-yield potato crops while accounting for environmental drivers including weather, soil type, and management. Using 900 Kaggle field experiments, models including KNN, linear SVM, naive Bayes, decision tree, random forest, and XGBoost are trained, evaluated, and compared with MAE, MSE, R-squared, and RMSE, and XGBoost shows the strongest predictive performance. The work concludes with a suggested hardware implementation to support farmers in the field.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Article overview

Problem background and influencing factors

Nutrient management, eutrophication, and literature context

NPK roles in crop development

Data and proposed machine learning approach

Model training, evaluation metrics, and comparison

Results and recommendation for field deployment

Predictive fertilization models for potato crops using machine learning techniques in Moroccan Gharb region Said Tkatek, Samar Amassmir, Amine Belmzoukia, Jaafar Abouchabaka Computer Sciences Research Laboratory, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco | Article history:
Received Dec 12, 2022 Revised Jan 13, 2023 Accepted Feb 4, 2023 | Given the influence of several factors, including weather, soils, land management, genotypes, and the severity of pests and diseases, prescribing adequate nutrient levels is difficult. A potato ’s performance can be predicted using machine learning techniques in cases when there is enough data. This study aimed to develop a highly precise model for determining the optimal levels of nitrogen, phosphorus, and potassium required to achieve both high-quality and high-yield potato crops, taking into account the impact of various environmental factors such as weather, soil type, and land management practices. We used 900 field experiments from Kaggle as part of a data set. We developed, evaluated, and compared prediction models ofk-nearest neighbor (KNN), linear support vector machine (SVM), naive Bayes (NB) classifier, decision tree (DT) regressor, random forest (RF) regressor, and eXtreme gradient boosting (XGBoost) . We used measures such as mean average error (MAE), mean squared error (MSE), R-Squared (RS), and R2Root mean squared error (RMSE) to describe the model ’s mistakes and prediction capacity. It turned out that the XGBoost model has the greatest R2, MSE and MAE values. Overall, the XGBoost model outperforms the other machine learning models. In the end, we suggested a hardware implementation to help farmers in the field.
This is an open access article under the CC BY-SA license.
| | --- | --- | | Keywords:
Artificial intelligence Fertilization Internet of things Machine learning Raspberry Pi3 | | Corresponding Author: Said Tkatek Laboratory for Computer Sciences Research, Faculty of Science, Ibn Tofail University 14000, Kenitra, Morocco Email: [said.tkatek@uit.ac.ma](said.tkatek@uit.ac.ma) Article Info ABSTRACT 1. INTRODUCTION The two most important and fundamental resources for life on earth are soil and water. Morocco ’s soils and rivers are becoming more and more deteriorated, and this deterioration is accelerating. Due to its rich soils and easy access to water, The Gharb (Morocco) is widely renowned for its intense agriculture. However, after extensive use of these resources, the quality of these soils and rivers should be evaluated. The Atlantic Ocean has a significant impact on the Gharb ’s climate, which is characterized by a sub-humid bioclimatic zone with high air humidity in the winter and high temperatures in the summer. Various factors can affect fertilization for optimal tuber yield, including the type and quality of the soil [1], [2], organic fertilizers [3], [4], previous crops [5]–[9], weather [10], irrigation [11], timing and location of the applied fertilizer [12], pests and diseases [13], and genetic factors. For instance, soil with high organic matter content tends to retain nutrients better and provide a more suitable environment for microbial activity, which aids in nutrient availability. The use of organic fertilizers also enhances soil quality, improves plant nutrient uptake, and reduces environmental impacts compared to synthetic fertilizers. Furthermore, previous crops, weather patterns, and irrigation practices influence the nutrient cycling and availability in the soil, ultimately impacting crop growth and development. In addition to these factors, various other factors can influence the growth and development of crops, including day length, photoperiod, water availability, intercepted radiation, air temperature, precipitation, root development, and crop management. These factors interact in complex ways, making it challenging to optimize crop growth [14], [15], and development for optimal yield. Howev

FAQs

Why is prescribing fertilizer levels for potatoes considered difficult?

Because potato performance depends on interacting factors such as weather, soil properties, land management, genotypes, and the severity of pests and diseases.

What is the goal of the study regarding NPK fertilization?

To predict and determine optimal nitrogen, phosphorus, and potassium levels that achieve both high-quality and high-yield potato crops under varying environmental conditions.

Which machine learning model performs best in the study?

The XGBoost model shows the greatest R-squared, MSE, and MAE values, outperforming the other tested models.

Aug 4, 2026

Upload by Anda