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; Naonobu Okazaki (Agriculture, 2023)
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.