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| Pillar | Application | Author | Technique | Remarks | Practical use of the application |
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| Availability | Paddy land leveling system | Si et al. [1] | Fuzzy logic | Fuzzy system in the controller judges the land level | Land preparation |
| Contaminated soil classificatory tool | Lopez et al. [2] | Fuzzy logic | Greater accuracy over typical computer-based models | Land and crop selection |
| Stem water potential estimator | Valdes-Vela et al. [29] | Fuzzy logic | Greater approximation power compared to other models | Water management |
| Soybean aphid control system | Peixoto et al. [6] | Fuzzy logic | Predict the timing and release of predators for the biological control | Pest management |
| Image-based AI management system for wheat | Li et al. [7] | ANN (BPNN) | Uses pixel labelling algorithms for image strengthening | Fertilizer application time decision |
| Soil moisture monitoring system | Athani et al. [8] | IoT-enabled Arduino sensors | Vastly decreases the manufacturing and maintenance costs | Reduction of COP |
| System for detecting mature whiteflies on rose leaves | Boissard et al. [10] | ML | Reliable for rapid detection of whiteflies | Pest management |
| AI-assisted weed identification system | Tobal and Mokthar [30] | ANN | Minimize the time of classification training and error | Weed control |
| Weed identification system in paddy fields | Barrero et al. [11] | ANN | Based on areal image analysis | Weed control |
| Novel weed management strategy | Pérez-Harguindeguy et al. [31] | ML | Combines UAVs, image processing, and ML | Weed control |
| Field weed identification system | Ebenso et al. [32] | ANN | Improves crop/weed species discrimination | Weed control |
| Expert system for diagnosis of potato diseases | Boyd and Sun [33] | Rule-based computer program | Can diagnose eleven pathogenic diseases and six nonpathogenic diseases | Disease management |
| Expert system for diagnosing diseases in rice plant | Sarma et al. [34] | Rule-based computer program | Based on logic programming approach | Disease management |
| Leaf image classification system | Sladojevic et al. [35] | ANN | Uses deep convolutional networks | Disease management |
| System for diagnosing diseases of oilseed-crops | Chaudhary et al. [36] | Fuzzy logic | Much faster inference compared to earlier models | Disease management |
| System for rice yield prediction | Ji et al. [37] | ANN | More accurate than linear regression models for the yield predictions | Yield prediction (decision making) |
| System for cotton yield prediction | Zhang et al. [38] | ANN | More realistic trends versus input factors and predicted yields | Yield prediction (decision making) |
| System for wheat yield pre | Ruß et al. [39] | ANN | Uses cheaply available in-season data. | Yield prediction (decision making) |
| System for jute yield prediction | Rahman and Bala [40] | ANN | Could be used to predict production at different locations | Yield prediction (decision making) |
| Accessibility | Food desert identifier | Zhao [41] | Big data analytics and ML | Locates areas with low food access | Decision making |
| Food desert identifier | Amin et al. [42] | ML | Detects food deserts and food swamps with a prediction accuracy of 72% | Decision making |
| Decision tool to evaluate the performance of agriculture food value chain | Liu et al. [43] | Fuzzy logic | Integrates TFN, AHP, and TOPSIS | Decision making |
| Forecasting of food production | Sharma and Patil [44] | Fuzzy logic | Forecast the production and consumption of rice | Decision making |
| Forecasting of food production | Yan et al. [45] | ML | Uses ANN, SVM, GP, and GPR to forecast future milk yield | Decision making |
| Supply chain optimization | Cheraghalipour et al. [46] | Evolutionary ML | Reduce held inventory and cost in supply chains | Efficient food distribution |
| Supply chain optimization | Ketsripongsa et al. [47] | Evolutionary ML | Used for transportation scheduling of seafood and milk products | Efficient food distribution |
| Supply chain forecasting | Olan et al. [48] | ANN | Forecast the results of perishable food transportation | Decision making |
| System for preparing and dispensing food | Sharma et al. [49] | Robotics | Extremely useful in pandemic situations like COVID-19 | Efficient food distribution |
|
| Utilization | Cassava roots storage system | Babawuro et al. [50] | Fuzzy logic | Uses an intelligent temperature control technique | Postharvest quality control |
| Fruit storage system | Morimoto et al. [51] | Fuzzy logic and ANN | RH inside the storage house is controlled | Postharvest quality control |
| Potato storage system | Gottschalk [52] | Fuzzy logic | Highly energy efficient | Postharvest quality control |
| Mechanical damage detection of fruits | Vélez Rivera et al. [53] | Hyperspectral images and ML | Used as a tool for the automatic inspection and monitoring of internal defects of fruits and vegetables in postharvest quality control laboratories | Postharvest quality control |
| Assorting of fruits and vegetables | Valdez [54] | Computer vision and deep learning | Fast, reliable, and labor inexpensive methods | Reduce labor requirement |
|
| Stability | Water resource management | Sadeghfam et al. [55] | ANN | Minimize the ground water overexploitation and groundwater remediation through pump-treat-inject technology | Increasing water availability |
| Zahm et al. [56] |
| Zahm et al. [56] | ANN | Identify the reasons for spring flow decrease | Increasing water availability |
| Supply chain quality data integration method | Wang [18] | AI integration method of block chain technology | Supply chain of agriculture products | Increasing water availability |
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