1. Introduction
The impact of blast fungus (
Magnaporthe oryzae syn.
Pyricularia oryzae) is a global rice (
Oryza sativa L.) production issue [
1]. The fungus has been found in at least 85 rice-growing countries [
2] and is a particular threat to food security in South Asia and Africa [
3]. Rice blast disease caused the loss of 157 million tons of rice worldwide between 1975 and 1990, resulting in maximum possible losses corresponding to 30% of global rice production [
4,
5,
6] and annual food losses for 60 million people [
1]. The cost of chemical control may exceed approximately USD 70/ha/year [
7]. In addition, blast fungus has been found to affect not only rice but also barley (
Hordeum vulgare) [
8]. Due to the threat to food security under and farmers’ income, blast fungus has been evaluated as the most destructive fungus globally [
9] and has been identified as one of the significant and omnipresent rice cultivation constraints [
10]. Fungal blast poses an especially severe threat to the tropical rice-growing regions, which feed more than 33% of the global population [
11]. Thus, growers have a great need to determine when the disease will start, how severe an epidemic will be and when they should apply fungicides [
12].
The rice blast pathogen develops in the nodes, leaves, collars, necks, panicles, seeds and roots over the entire growth period [
13,
14].
Figure 1 shows the leaf and panicle symptom of rice blast. The fungus subsequently invades the above-ground parts of the rice plant and, in severe epidemics, large ellipsoid lesions can engulf the entire surface of a leaf [
15]. Based on the rice blast attack mechanism, it has been found that the damage is very much influenced by environmental factors. The disease can survive throughout the year in the air and can be severed during periods of low temperatures and high moisture; while conidia do not germinate under direct sunlight, overcast conditions and dew encourage blast spread [
16]. Blast lesions occur in suitable weather, leading to increased blast incidence and severity for 7 to 10 days [
17]; their life cycle is 7 to 14 days [
18,
19,
20], then destroying plants within 15 to 20 days, causing yield losses of up to 100% [
21]. Moreover, inappropriate farming practices, e.g., excessive fertilizer [
22], may lead to severe rice blast happening. Although ongoing research is working to develop rice blast resistant varieties, it has been found that the resistance is being lost and even vanishes within a few years [
23].
Considering the severity of rice blast effects on rice production, many researchers have developed rice blast forecasting models for early warning. Katsantonis, et al. [
24] reviewed 52 rice blast forecasting models and concluded that the parameters applied (with frequencies) included air temperature (T, 67.3%), relative humidity (RH, 57.7%), rainfall (55.8%), leaf wetness (34.6%), sunlight (30.8%), wind speed (30.8%) and dewpoint (15.4%). The variables most often combined were air temperature (T) and relative humidity (RH). Moreover, under the growth mechanism of the rice blast pathogen, the disease occurrence has been found to be positively related to soil temperature (T
S). Hemi and Abe [
25] used a modified Wisconsin soil temperature tank for a rice blast experiment in the seeding stage, considering the effect of T
S. Incidence of rice blast at 20 °C, 24 °C, 28 °C and 32 °C was 33.95%, 17.28%, 17.85% and 13.70%, respectively. Abe [
26] found that rice blast incidence was lowest in seedlings grown at T
S of 27.8 °C with highest in those grown at 20 °C A similar finding from Hemi, et al. [
27] showed that the rice blast incidence after the booting stage with T
S of 20 °C to 29 °C and 18 °C to 24 °C was 28.46% and 56.02%, respectively. Hashioka [
28] reported that resistance increases with the rise of both air and soil temperatures. The ratio of carbon to nitrogen in the leaves is increased in proportion to the rise in temperature. Suzuki [
29] inoculated the panicles of two resistant and two susceptible rice varieties grown at low and normal soil temperatures. Disease development in all plants in the low temperature series was 75 to 100%, whereas at the normal temperature, it was 0 to 13.3% for the resistant and 20 to 33.3% for susceptible varieties. These forecasting models could be used to identify which years are conducive and whether fungicide application would be cost-effective or risky under those conditions. Both empirical and explanatory simulation models via regression analysis have been developed for rice blast prediction in many countries [
24]. However, due to climate change affects the rice field environment variate intensely, the conventional prediction model might lose the predicting accuracy [
30].
In recent years, a few researchers have considered the rice blast mechanism to be a nonlinear system and have adopted machine learning (ML), especially artificial neural networks (ANNs), known as sophisticated problem-solving algorithms, for the development of rice blast forecasting models. This approach was first explored by Kaundal, Kapoor and Raghava [
12]. The researchers collected 5 years of data on rice blast events and weekly weather data (from 2000 to 2004) in India for the development of a long-term forecasting model, including “cross-location” and “cross-year” scenarios. The model included multiple regression (REG), the backpropagation neural network (BPNN), generalized regression neural network (GRNN) and support vector machines (SVM). Approximately 80% of data were used for model training and validation, the remaining 20% for model testing. The average correlation coefficients (r) between observed and modeled rice blast happening probability in REG, BPNN, GRNN and SVM were 0.48, 0.56, 0.66 and 0.74 for the cross-location model and 0.50, 0.60, 0.70 and 0.77 for the cross-year model, respectively. In the factor importance analysis, rainfall was most influential in predicting the disease, followed by rainy days/week, minimum RH (RH
min), maximum RH (RH
max), minimum T (T
min) and maximum T (T
max). Mojerlou, et al. [
31] applied weather data including precipitation, daily T
min, T
max, RH
min, RH
max and duration of sunny hours from 2006 to 2008 for rice blast spore population forecasting in Iran. A multilayer perceptron (MLP) was employed for model development. In total, 80% of the data were used for model training and validation and 20% of the testing data. The highest coefficient of determination (R
2) obtained was 0.552. The results indicate that RH and T were the most important meteorological factors and form a suitable base for predicting the spore population, according to the findings of Calvero, et al. [
32]. Kim, Roh and Kim [
14] applied a long short-term memory (LSTM) recurrent neural network (RNN) to establish an early rice blast event forecasting model for four regions in Korea. Climatic data, including T, RH and sunlight, were obtained in June and July from 2003 to 2016 during the rice blast data collection period. This study used 70% of the training data, 10% for validation and 20% for testing. An early (1 year) rice blast model was developed. The highest accuracy and F1-score was 79.4%. Nettleton, et al. [
33] compared 4 models (two role-based, called YOSHINO and WARM, the other two for ML were M5RULES and LSTM RNN) for rice blast disease prediction. The inputs for the ANN models included daily T
min, T
max, RH and leaf wetness. The result showed that the LSTM RNN achieved the highest averaged r (0.7). The authors indicated that these 4 models exhibit significant signals during the early warning period, with a similar performance level.
Compare the traditional REG model, these ML-based rice blast predicting models, e.g., BPNN, GRNN, SVM, RNN, have developed a reliable forecasting result, according to relative environment factors, such as temperature, relative humidity and sunlight. However, regarding soil temperature, it has been found that it affects the blast pathogen growth and spread; in addition, the blast occurrence is known to relate to soil conditions, which is yet to be seen in relevant applied research. Therefore, this study considered the influence of Ts as one of the variables for forecasting model development and discuss the effect mechanism.
This study aims to develop a reasonable and reliable rice blast forecasting model based on the mechanism of rice blast pathogen growth and spread using ML techniques. Accordingly, the objectives were as follows:
- (1)
Based on rice blast pathogen growth and spread mechanisms, to determine short-term environment data for establishing models.
- (2)
To employ ML as rice blast forecasting models and to assess model performance by confusion matrix.
- (3)
Conduct sensitivity analysis on high performance ML model, to evaluate the important rice blast influence factors.
To achieve the aims of this study, firstly, regional weather data and rice blast event were collected. Secondly, the collected data were applied to selected ML algorithms for rice blast event forecasting model development. In the third step, the performances of each model were evaluated to find the best-fit model. Finally, the sensitivity analysis was conducted in highest performance model to identify the relative importance from each input to the model.
4. Discussion
Katsantonis, Kadoglidou, Dramalis and Puigdollers [
24] reviewed 52 rice blast forecasting models indicating that weather variables, such as T, RH, spore dissemination and leaf wetness, are among the most critical model inputs, since these variables play essential roles in rice blast pathogen growth. In ML models, T, RH and sunlight have been found to be effective factors for rice blast forecasting model development. However, due to climate change, which has intensely affected rice field environmental variation [
30], the conventional prediction model may inappropriate for rice blast modeling. In addition, the rice blast spore mechanism involves a short-term effect that lasts only 7 to 14 days [
17,
18,
19,
20]. This indicates the long-term rice blast forecasting model might show reduced prediction accuracy. However, T
S has been found to be highly influential when considering the growth mechanism of the rice blast pathogen [
25,
26,
27,
28,
29]. Thus, this study adopted T
S along with conventional environmental factors for rice blast forecasting. In addition, under the mechanism of the rice blast pathogen, blast lesions occur with elevated blast incidence and severity 7 to 10 days after suitable weather conditions. Sporulation takes only 7 to 14 days as a short-term effect [
17,
18,
19,
20]; this investigation used D
10 (before 10 days of rice blast occurred) and D
20 (10 days to 20 days after the initiation of rice blast) as input variable periods. The weather data shows that the largest difference between D
20 and D
10 in an infection event was observed for SE and the smallest difference was observed for RH
avg. Values for most factors, such as T
min, T
max, T
avg, T
S and SE, increased. Only RH
avg decreased. In the “not infected” result, a slight difference between each factor could be found. Only RH
avg differences were positive and differences for the other five variables were negative. This shows an opposite pattern compared with data from infections. The variation of “infected” data was higher than for “not infected” data. This reveals that the rice blast occurrences corresponded with significant weather change from D
10 to D
20.
After data treatment, the ANN algorithms were applied, including traditional rice blast forecasting models MLP, SVM and Elman RNN, as well as a model not previously used for rice blast forecasting called PNN, to develop rice blast forecasting models. The developed models were mainly evaluated by F-measure, particularly with higher
β, for which the infection event is more important than the noninfected period. Therefore, F2-scores were used for model performance assessment. The F2-scores for MLP, SVM, Elman RNN and PNN models were 87.0%, 76.9%, 59.5% and 96.8%, respectively. Comparing model performance with previous research, periods predicted have ranged from 14 to 15 days [
56] to 1 year [
14] in advance with model prediction accuracy of 79.4% [
14] to 87.2% [
56]. The only F1-score was obtained by Kim, Roh and Kim [
14] with a value of 79.4%. In this study, the predicted period was 10 days, with accuracy 91.7% and F1-score 92.3%. The developed rice forecasting PNN model was found to perform similarly to or slightly better than models from previous studies. Thus, the PNN model is recommended for rice blast forecasting in the YCN area. Moreover, a sensitivity analysis (SA) was conducted of the PNN to evaluate the importance of factors in the model. D
10 was found to have a stronger influence than D
20. In addition, the most influential factors were T
min and SE, followed by T
max, T
S and RH
avg. The last factor T
avg was not found to have a significant influence in the model, perhaps because the contribution was accounted for by T
min and T
max. The impact of soil temperature (T
S) may result from the phase lag in the diurnal variations of temperature (T). At some point in daily T and T
S variation, T
S is warmer than T (T < T
S). Radiative cooling near the ground is very clear at night, leading to T values lower than T
S [
57]. In this case, a lower dewpoint has been reached. This causes RH to approach 100%, then mist appears in the field. Thus, the rice blast pathogen has an ideal environment for growth and spread. Under the opposite condition (T > T
S), a similar mechanism also occurs.
Based on the developed rice blast forecasting model, a disease warning could be issued to farmers 10 days before the disease occurs and spreads across a vast area. Several prevention measures are possible to implement, such as ferric chloride, di-potassium hydrogen phosphate, salicylic acid, nano-chitosan and resistance-inducing compounds produced by
F. solani [
58,
59,
60,
61]. Among them, the application of nano-chitosan and
F. solani have been shown to exert 10 to 14 days of resistance ability. These could perhaps be used in conjunction with the model developed in this study.