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

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 pre...

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Bibliographic Details
Published in:Agriculture (Basel) 2023-03, Vol.13 (3), p.627
Main Authors: Tende, Isakwisa Gaddy, Aburada, Kentaro, Yamaba, Hisaaki, Katayama, Tetsuro, Okazaki, Naonobu
Format: Article
Language:English
Subjects:
Agricultural production
Analysis
Cellular telephones
Climatic data
Corn
Coronaviruses
COVID-19
Crop yield
Crop yields
Crops
Deep learning
Design
digital farming
Economic conditions
electronic-agriculture
Farmers
Food
Food security
Food supply
Harvesting
Hunger
Long short-term memory
Machine learning
Moisture effects
Neural networks
Pandemics
Performance evaluation
Polls & surveys
Precipitation
Questionnaires
Rain
Rain and rainfall
Rainfall
Remote sensing
Rice
Seasons
Security management
Short message service
short message service (SMS)
Soil moisture
Soil temperature
Web browsers
Wheat
yield prediction
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Summary: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.
ISSN:2077-0472
2077-0472
DOI:10.3390/agriculture13030627