Rice Science ›› 2024, Vol. 31 ›› Issue (1): 47-61.DOI: 10.1016/j.rsci.2023.08.004
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Norhashila Hashim1(
), Maimunah Mohd Ali2(), Muhammad Razif Mahadi1, Ahmad Fikri Abdullah1, Aimrun Wayayok1, Muhamad Saufi Mohd Kassim1, Askiah Jamaluddin3
Received:2023-05-01
Accepted:2023-08-04
Online:2024-01-28
Published:2024-02-06
Contact:
Norhashila Hashim (norhashila@upm.edu.my); Maimunah Mohd Ali (maimunahmma@ukm.edu.my)
Norhashila Hashim, Maimunah Mohd Ali, Muhammad Razif Mahadi, Ahmad Fikri Abdullah, Aimrun Wayayok, Muhamad Saufi Mohd Kassim, Askiah Jamaluddin. Smart Farming for Sustainable Rice Production: An Insight into Application, Challenge, and Future Prospect[J]. Rice Science, 2024, 31(1): 47-61.
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Fig. 1. Harvested area (A) and production (B) of rice worldwide from 2016 to 2022 (FAOSTAT, 2023).
| Country/Region | Total production (t) | Country/Region | Total production (t) | Country/Region | Total production (t) |
|---|---|---|---|---|---|
| China | 208 494 800 | Brazil | 10 776 268 | Sri Lanka | 3 392 905 |
| India | 196 245 700 | Japan | 10 363 900 | Mali | 2 864 723 |
| Bangladesh | 57 189 193 | Nigeria | 8 502 000 | the United Republic of Tanzania | 2 856 500 |
| Indonesia | 54 748 977 | the United States of America | 7 274 170 | Colombia | 2 620 100 |
| Vietnam | 42 672 338 | Egypt | 5 800 000 | Guinea | 2 523 304 |
| Thailand | 34 317 028 | Nepal | 5 486 500 | Malaysia | 2 364 453 |
| Myanmar | 24 680 200 | Korea | 4 996 223 | North Korea | 2 061 443 |
| the Philippines | 19 756 392 | Madagascar | 4 585 000 | Côte d’Ivoire | 1 993 000 |
| Cambodia | 11 624 000 | Laos | 3 594 800 | Congo | 1 692 323 |
| Pakistan | 10 983 081 | Peru | 3 449 365 | Taiwan, China | 1 576 000 |
Table 1. Top 30 rice producers worldwide in 2022 (FAOSTAT, 2023).
| Country/Region | Total production (t) | Country/Region | Total production (t) | Country/Region | Total production (t) |
|---|---|---|---|---|---|
| China | 208 494 800 | Brazil | 10 776 268 | Sri Lanka | 3 392 905 |
| India | 196 245 700 | Japan | 10 363 900 | Mali | 2 864 723 |
| Bangladesh | 57 189 193 | Nigeria | 8 502 000 | the United Republic of Tanzania | 2 856 500 |
| Indonesia | 54 748 977 | the United States of America | 7 274 170 | Colombia | 2 620 100 |
| Vietnam | 42 672 338 | Egypt | 5 800 000 | Guinea | 2 523 304 |
| Thailand | 34 317 028 | Nepal | 5 486 500 | Malaysia | 2 364 453 |
| Myanmar | 24 680 200 | Korea | 4 996 223 | North Korea | 2 061 443 |
| the Philippines | 19 756 392 | Madagascar | 4 585 000 | Côte d’Ivoire | 1 993 000 |
| Cambodia | 11 624 000 | Laos | 3 594 800 | Congo | 1 692 323 |
| Pakistan | 10 983 081 | Peru | 3 449 365 | Taiwan, China | 1 576 000 |
| Technology | Application | Result | Reference |
|---|---|---|---|
| ANN | Rice quality prediction | Highest classification of 98% | Aznan et al, |
| ANN, fuzzy logic, and genetic algorithms | Irrigation water demand | R2 of 0.72 | González Perea et al, |
| CNN | Leaf disease detection | Testing accuracy of 98% | Venu Vasantha et al, |
| CNN | Rice chalkiness | Gradient-weighted class activation mapping accurately classified rice chalkiness | Wang et al, |
| CNN | Rice damage classification | Overall classification accuracy of 98% | Moses et al, |
| CNN | Rice disease detection | Highest accuracy of 89% | Asif Saleem et al, |
| CNN | Rice variety classification | Highest classification accuracy of 100% | Koklu et al, |
| Coarse tree classifier | Rice seed classification | Classification accuracy of 100% | Geollegue et al, |
| Computer vision | Rice kernel identification | Accuracy of 98 % | Zia et al, |
| Decision support system | Water-saving rice production | Saving 41% of water while producing 96% of rice yield | Kadiyala et al, |
| Deep convolutional neural network | Rice disease detection | Highest average accuracy of 96% | Latif et al, |
| Deep learning | Rice leaf disease detection | Highest accuracy of 78% | Tejaswini et al, |
| Deep learning-CNN | Early rice disease detection | Classification accuracy of 93% | Shrivastava et al, |
| Deep learning-full resolution network | Growth stage detection | Highest accuracy of 89% | Xia et al, |
| Deep learning-ResNet | Weed detection | Mean average precision of 94% | Peng et al, |
| Deep learning-ResNet | Rice seed variety classification | Classification accuracy of 86% | Jeyaraj et al, |
| Empirical correction using drone | Monitoring rice growth | Mitigating the decrease in NDVI values | Hama et al, |
| Feed-forward neural network | Rice blast detection | Highest recall of 66% | Lee et al, |
| Fuzzy clustering | Aroma rice identification | Number of clusters achieved by automatic clustering was greater for aromatic rice | Rahimzadeh et al, |
| Fuzzy logic, ANN, and multi-objective genetic algorithm | Pressurized irrigation system | R2 of 0.70 | González Perea et al, |
| Fuzzy-programmable intelligence device algorithm | Irrigation control system | Reduced regulation time by 2.5 s | Liu et al, |
| GIS-based water management model | Rice irrigation scheduling | Performance monitoring features on-going water delivery programmes | Rowshon and Amin, |
| Gradient tree boosting machine learning | Rice quality detection | Accuracy of 96% | Aulia et al, |
| IoT | Alternate wetting and drying irrigation | 13% to 20% of water savings over manual practice | Pham et al, |
| IoT | Early disease detection | Increased field horizons | Sai et al, |
| IoT | Monitoring rice farm ontology | Phase-wise decision-making for rice | Afzal and Kasi, |
| IoT | Rice disease detection | High sensitivity of 91% | Sowmyalakshmi et al, |
| IoT-Markov chain process | Fertilization and irrigation control system | Decision-making module based on expert knowledge and system data | Bamurigire et al, |
| k-means clustering algorithm | Monitoring rice growth | 100% efficiency | Ramesh et al, |
| kNN | Shelf life prediction | R2 of 0.72 | Hanif et al, |
| kNN, decision tree, and random forest | Rice irrigation system | Highest accuracy of 99% | Zakzouk et al, |
| Logistic regression | Rice seed classification | Correct classification of 92% | Ruslan et al, |
| Moisture wireless sensor | Monitoring moisture content and water height of field soil | Water-saving irrigation is 65% of normal irrigation | Xiao et al, |
| Multi-models (ensemble) projection | Modelling water demand for rice irrigation | Climate-smart decision support system | Rowshon et al, |
| Multi-scale hybrid window panicle detect | Prediction of rice panicle yield | Counting accuracy higher than 87% | Xu et al, |
| Neural network algorithm | Rice quality detection | Accuracy score of 99% | Erlangga et al, |
| Partial least squares | Potassium content prediction | R2 of up to 0.76 | Lu et al, |
| Random forest | Rice disease detection | Highest accuracy of 100% | Singh et al, |
| Random forest | Rice irrigation system | Increasing trend in germination indices | Rashid et al, |
| Random forest | Rice leaf disease detection | Accuracy of 97% | Shahidur Harun Rumy et al, |
| Random forest | Rice moisture content classification | Highest accuracy of 87% | Azmi et al, |
| Random forest | Rice yield prediction | Prediction accuracy higher than 80% | Elders et al, |
| Random forest | Soil quality on rice productivity | Long-term fertilizer application | Garnaik et al, |
| Regression-based machine learning | Moisture distribution detection | Highest accuracy of 83% | Almaleeh et al, |
| Rice farm decision support system | Rice yield monitoring system | Decision support system using mobile apps | Ogunti et al, |
| RNN and LSTM | Rice harvest prediction | Training accuracy of 78% and testing accuracy of 75% | Muthusinghe et al, |
| Support vector regression | Shelf life prediction | R2 of 0.99 | Kusbandhini et al, |
| Unmanned aerial vehicle remote sensing | Rice yield estimation | R2 of 0.761 | Hama et al, |
| Water evaluation and planning system and decision support system | Water demand for irrigation system | Rice yields resemble more of step function based on sufficient water for flooding | Winter et al, |
Table 2. Applications of smart farming technologies for rice production.
| Technology | Application | Result | Reference |
|---|---|---|---|
| ANN | Rice quality prediction | Highest classification of 98% | Aznan et al, |
| ANN, fuzzy logic, and genetic algorithms | Irrigation water demand | R2 of 0.72 | González Perea et al, |
| CNN | Leaf disease detection | Testing accuracy of 98% | Venu Vasantha et al, |
| CNN | Rice chalkiness | Gradient-weighted class activation mapping accurately classified rice chalkiness | Wang et al, |
| CNN | Rice damage classification | Overall classification accuracy of 98% | Moses et al, |
| CNN | Rice disease detection | Highest accuracy of 89% | Asif Saleem et al, |
| CNN | Rice variety classification | Highest classification accuracy of 100% | Koklu et al, |
| Coarse tree classifier | Rice seed classification | Classification accuracy of 100% | Geollegue et al, |
| Computer vision | Rice kernel identification | Accuracy of 98 % | Zia et al, |
| Decision support system | Water-saving rice production | Saving 41% of water while producing 96% of rice yield | Kadiyala et al, |
| Deep convolutional neural network | Rice disease detection | Highest average accuracy of 96% | Latif et al, |
| Deep learning | Rice leaf disease detection | Highest accuracy of 78% | Tejaswini et al, |
| Deep learning-CNN | Early rice disease detection | Classification accuracy of 93% | Shrivastava et al, |
| Deep learning-full resolution network | Growth stage detection | Highest accuracy of 89% | Xia et al, |
| Deep learning-ResNet | Weed detection | Mean average precision of 94% | Peng et al, |
| Deep learning-ResNet | Rice seed variety classification | Classification accuracy of 86% | Jeyaraj et al, |
| Empirical correction using drone | Monitoring rice growth | Mitigating the decrease in NDVI values | Hama et al, |
| Feed-forward neural network | Rice blast detection | Highest recall of 66% | Lee et al, |
| Fuzzy clustering | Aroma rice identification | Number of clusters achieved by automatic clustering was greater for aromatic rice | Rahimzadeh et al, |
| Fuzzy logic, ANN, and multi-objective genetic algorithm | Pressurized irrigation system | R2 of 0.70 | González Perea et al, |
| Fuzzy-programmable intelligence device algorithm | Irrigation control system | Reduced regulation time by 2.5 s | Liu et al, |
| GIS-based water management model | Rice irrigation scheduling | Performance monitoring features on-going water delivery programmes | Rowshon and Amin, |
| Gradient tree boosting machine learning | Rice quality detection | Accuracy of 96% | Aulia et al, |
| IoT | Alternate wetting and drying irrigation | 13% to 20% of water savings over manual practice | Pham et al, |
| IoT | Early disease detection | Increased field horizons | Sai et al, |
| IoT | Monitoring rice farm ontology | Phase-wise decision-making for rice | Afzal and Kasi, |
| IoT | Rice disease detection | High sensitivity of 91% | Sowmyalakshmi et al, |
| IoT-Markov chain process | Fertilization and irrigation control system | Decision-making module based on expert knowledge and system data | Bamurigire et al, |
| k-means clustering algorithm | Monitoring rice growth | 100% efficiency | Ramesh et al, |
| kNN | Shelf life prediction | R2 of 0.72 | Hanif et al, |
| kNN, decision tree, and random forest | Rice irrigation system | Highest accuracy of 99% | Zakzouk et al, |
| Logistic regression | Rice seed classification | Correct classification of 92% | Ruslan et al, |
| Moisture wireless sensor | Monitoring moisture content and water height of field soil | Water-saving irrigation is 65% of normal irrigation | Xiao et al, |
| Multi-models (ensemble) projection | Modelling water demand for rice irrigation | Climate-smart decision support system | Rowshon et al, |
| Multi-scale hybrid window panicle detect | Prediction of rice panicle yield | Counting accuracy higher than 87% | Xu et al, |
| Neural network algorithm | Rice quality detection | Accuracy score of 99% | Erlangga et al, |
| Partial least squares | Potassium content prediction | R2 of up to 0.76 | Lu et al, |
| Random forest | Rice disease detection | Highest accuracy of 100% | Singh et al, |
| Random forest | Rice irrigation system | Increasing trend in germination indices | Rashid et al, |
| Random forest | Rice leaf disease detection | Accuracy of 97% | Shahidur Harun Rumy et al, |
| Random forest | Rice moisture content classification | Highest accuracy of 87% | Azmi et al, |
| Random forest | Rice yield prediction | Prediction accuracy higher than 80% | Elders et al, |
| Random forest | Soil quality on rice productivity | Long-term fertilizer application | Garnaik et al, |
| Regression-based machine learning | Moisture distribution detection | Highest accuracy of 83% | Almaleeh et al, |
| Rice farm decision support system | Rice yield monitoring system | Decision support system using mobile apps | Ogunti et al, |
| RNN and LSTM | Rice harvest prediction | Training accuracy of 78% and testing accuracy of 75% | Muthusinghe et al, |
| Support vector regression | Shelf life prediction | R2 of 0.99 | Kusbandhini et al, |
| Unmanned aerial vehicle remote sensing | Rice yield estimation | R2 of 0.761 | Hama et al, |
| Water evaluation and planning system and decision support system | Water demand for irrigation system | Rice yields resemble more of step function based on sufficient water for flooding | Winter et al, |
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