Development and Evaluation of a Deep Learning Based System to Predict District-Level Maize Yields in Tanzania

Isakwisa Gaddy Tende, Kentaro Aburada (), Hisaaki Yamaba, Tetsuro Katayama and Naonobu Okazaki
Additional contact information
Isakwisa Gaddy Tende: Department of Computer Studies, Dar es Salaam Institute of Technology, Dar es Salaam P.O. Box 2958, Tanzania
Kentaro Aburada: Faculty of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan
Hisaaki Yamaba: Faculty of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan
Tetsuro Katayama: Faculty of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan
Naonobu Okazaki: Faculty of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan

Agriculture, 2023, vol. 13, issue 3, 1-19

Abstract: Prediction of crop yields is very helpful in ensuring food security, planning harvest management (storage, transport, and labor), and performing market planning. However, in Tanzania, where a majority of the population depends on crop farming as a primary economic activity, the digital tools for predicting crop yields are not yet available, especially at the grass-roots level. In this study, we developed and evaluated Maize Yield Prediction System (MYPS) that uses a short message service (SMS) and the Web to allow rural farmers (via SMS on mobile phones) and government officials (via Web browsers) to predict district-level end-of-season maize yields in Tanzania. The system uses LSTM (Long Short-Term Memory) deep learning models to forecast district-level season-end maize yields from remote sensing data (NDVI on the Terra MODIS satellite) and climate data [maximum temperature, minimum temperature, soil moisture, and precipitation (rainfall)]. The key findings reveal that our unimodal and bimodal deep learning models are very effective in predicting crop yields, achieving mean absolute percentage error (MAPE) scores of 3.656% and 6.648%, respectively, on test (unseen) data. This system will help rural farmers and the government in Tanzania make critical decisions to prevent hunger and plan better harvesting and marketing of crops.

Keywords: electronic-agriculture; digital farming; machine learning; yield prediction; remote sensing; short message service (SMS); Web (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2077-0472/13/3/627/pdf (application/pdf)
https://www.mdpi.com/2077-0472/13/3/627/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:13:y:2023:i:3:p:627-:d:1089327

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().