Name
Abstract | ||
|---|---|---|
In precision agriculture (PA), soil sampling and testing operation is prior to planting any new crop. It is an expensive operation since there are many soil characteristics to take into account. This paper gives an overview of soil characteristics and their relationships with crop yield and soil profiling. We propose an approach for predicting soil pH based on nearest neighbour fields. It implements spatial radius queries and various regression techniques in data mining. We use soil dataset containing about 4, 000 fields profiles to evaluate them and analyse their robustness. A comparative study indicates that LR, SVR, and GBRT techniques achieved high accuracy, with the R2 values of about 0.718 and MAE values of 0.29. The experimental results showed that the proposed approach is very promising and can contribute significantly to PA. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-34885-4_40 | ARTIFICIAL INTELLIGENCE XXXVI |
Keywords | DocType | Volume |
Soil prediction, Regression techniques, Precision agriculture, Data mining | Conference | 11927 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Quoc Hung Ngo | 1 | 0 | 0.34 |
| Nhien-An Le-Khac | 2 | 224 | 49.63 |
| M. Tahar Kechadi | 3 | 326 | 59.59 |