Abstract
Rice (Oryza sativa L.) growth prediction is key for precise rice production. However, the traditional linear rice growth forecasting model is ineffective under rapidly changing climate conditions. Here we show that growth rate (Gr) can be well-predicted by artificial intelligence (AI)-based artificial neural networks (ANN) and gene-expression programming (GEP), with accumulated air temperatures based on growth degree day (GDD). In total, 10,246 Gr from 95 cultivations were obtained with three cultivars, TK9, TNG71, and KH147, in Central and Southern Taiwan. The model performance was evaluated by the Pearson correlation coefficient (r), root mean square error (RMSE), and relative RMSE (r-RMSE) in the whole growth period (lifecycle), as well as the average and specific key stages (transplanting, 50% initial tillering, panicle initiation, 50% heading, and physiological maturity). The results in lifecycle Gr modeling showed that ANN and GEP models had comparable r (0.9893), but the GEP model had the lowest RMSE (3.83 days) and r-RMSE (7.24%). In stage average and specific key stages, each model has its own best-fit growth period. Overall, GEP model is recommended for rice growth prediction considering the model performance, applicability, and routine farming work. This study may lead to smart rice production due to the enhanced capacity to predict rice growth in the field.
Highlights
Rice is the second-highest produced cereal in terms of yield and is a staple food for approximately four billion people globally [1]; knowing the critical requirements for rice growth and the best timing for rice planting and harvest are crucial for understanding the effects of policy, and optimizing agricultural practice to achieve higher food security [2]
growth rate (Gr) ranged from 0.1234 to 0.3199, as shown in∑Tin=a1(bylie−y1ˆi.)2In Stage 1, the growth degree day (GDD) model has an unignorable prediction biasr-rResMuSltEf(r%om) =the offsent−o1f Gr =×01, 0w0hich means the initial mo(d3)eling rice growth stage may start from Gr ≈ 0.45y, possibly leading to a wrong fieldwork wdehceirseioyni.iHs tihgehperepdriecdteidctivoanlueer,ryoirsisatlhseo ocbasnebrveefdouvnalduein, yStisagthee3aavnedraSgteaogfet4h.e observations, and n is the number of actual observations
Because of the importance of rice production and smart agriculture, GDD was conducted in artificial intelligence (AI) algorithms-artificial neural networks (ANN) and gene-expression programming (GEP), to predict rice growth variations
Summary
Rice is the second-highest produced cereal in terms of yield and is a staple food for approximately four billion people globally [1]; knowing the critical requirements for rice growth and the best timing for rice planting and harvest are crucial for understanding the effects of policy, and optimizing agricultural practice to achieve higher food security [2]. A precise method is needed so that the rice growth stages may be accurately predicted at varying environmental conditions [3], so as to effectively implement field cultivation management [4], achieve rationalization of irrigation and fertilization [5] and increase yield and profits for farmers [6]. Due to the development of digital agriculture modeling, the automatic field operation may be achieved by accurate growth period prediction, leading to precision agriculture [6]. The raised carbon dioxide concentration causes existing rice growth patterns to change in Taiwan [9]. The increased extreme rainfall events cause variation in rice growth patterns [10] and yield loss [11]
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