Version 1
: Received: 17 August 2023 / Approved: 18 August 2023 / Online: 21 August 2023 (10:32:32 CEST)
How to cite:
Malamsha, A.J.J.; Dida, M.A.; Moebs, S. A Survey of Machine Learning Modelling for Agricultural Soil Properties Analysis and Fertility Status Predictions. Preprints2023, 2023081395. https://doi.org/10.20944/preprints202308.1395.v1
Malamsha, A.J.J.; Dida, M.A.; Moebs, S. A Survey of Machine Learning Modelling for Agricultural Soil Properties Analysis and Fertility Status Predictions. Preprints 2023, 2023081395. https://doi.org/10.20944/preprints202308.1395.v1
Malamsha, A.J.J.; Dida, M.A.; Moebs, S. A Survey of Machine Learning Modelling for Agricultural Soil Properties Analysis and Fertility Status Predictions. Preprints2023, 2023081395. https://doi.org/10.20944/preprints202308.1395.v1
APA Style
Malamsha, A.J.J., Dida, M.A., & Moebs, S. (2023). A Survey of Machine Learning Modelling for Agricultural Soil Properties Analysis and Fertility Status Predictions. Preprints. https://doi.org/10.20944/preprints202308.1395.v1
Chicago/Turabian Style
Malamsha, A.J.J., Mussa Ally Dida and Sabine Moebs. 2023 "A Survey of Machine Learning Modelling for Agricultural Soil Properties Analysis and Fertility Status Predictions" Preprints. https://doi.org/10.20944/preprints202308.1395.v1
Abstract
The problem of low soil fertility and limited research in agricultural data driven tools, may lead to low crop productivity which makes it imperative to research in applications of high throughput computational algorithms such as of machine learning (ML) for effective soil analysis and fertility status prediction in order to assist in optimal soil fertility management decision-making activities. However, difficulties in the choice of the key soil properties parameters for use in reliable soil nutrients analysis and fertility prediction. Also, individual ML algorithms setbacks and modelling expert implementation procedures subjectivity, may lead to exploitation of worst fertility level targets and soil fertility status targets classification models performance reported variations. This paper surveys state-of-affair in ML for agricultural soil nutrients analysis and fertility status prediction. Prominent soil properties and widely used classical modelling algorithms and procedures are identified. Empirically exploited fertility status target classes are scrutinized, and reported soil fertility prediction model performances are depicted. The three pass method, with mixed method of qualitative content analysis and qualitative simple descriptive statistics were used in this survey. Observably, the frequently used soil nutrients and chemical properties were organic carbon, phosphorus, potassium, and potential Hydrogen, followed by iron, manganese, copper and zinc. Predominant algorithms included Random Forest, and Naïve Bayes, followed by Support Vector Machine. Model performances varied, with highest accuracy 98.93% and 98.15% achieved by ensemble methods, and the least being 60%. Interdisciplinary ML related researchers may consider using ensemble methods to develop high performance soil fertility status prediction models.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
