# Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review
## 3 Survey Results on Local Explanation in Industrial AI Applications
### Qualitative Analysis
#### 3.2.8 Smart Agriculture  
The smart agriculture sector is explained locally using various datasets, including tabular, time-series, and text data, as illustrated in Table 2.  
Using publicly available tabular data, [PERSON] et al. [128] used XAI and interpretable ML to study the influence of no-tillage on agricultural yield compared to conventional tillage. The authors assessed the importance of factors for prediction, variable interactions, and the relationship between relevant variables and the response variable. [PERSON] et al. [129] used sentiment analysis to assess customer evaluations in the food delivery services (FDSs) domain, and they justified their predictions using SHAP and LIME. [PERSON] et al. [127] proposed a machine learning model to discover the factors influencing agricultural land usage at the regional level for wheat, maize, and olive grove plantings. Using a model-agnostic methodology, they presented global and local interpretations of the significant elements. XAI technologies were used by [PERSON] et al. [130] on the 'Crop Recommendation' dataset to make ML models clear and trustworthy. Their research focused on sensitivity analysis, comparing what the models discovered to what farmers and agronomists already knew. Future research could look into other XAI methodologies and visualization techniques in sectors as diverse as computational creativity and emotion recognition.  
Several studies have looked into the usage of deep neural networks in time-series data research. For example, [PERSON] et al. [131] used a deep neural network to predict wheat yield in the Indian wheat belt. They used regression activation maps (RAMs) to improve interpretability to show the model's learned features and yield drivers. Similarly, [PERSON] et al. [132] created an XAI-based technique for agri-directors to train agri-workers by analyzing varied data and merging agri-informatics, statistics, and human dynamics. [PERSON] et al. [133] created the ExamPle model, which employed a Siamese network and multi-view representation to forecast plants' small secreted peptides (SSPs) and revealed the SSPs' sequential pattern. Additionally, [PERSON] et al. [134] presented the AIDCC (automatic and intelligent data collector and classifier) framework for automating the collection of imaging and parametric datasets from farms producing pearl millet, disease prediction, and feature visualization using deep learning and IoT.  
Finally, [PERSON] et al. [136] showed that the Xception network outperforms other CNNs in recognizing suspicious situations in various photos. It improved its post hoc explainability by using the Grad-CAM++ and LIME algorithms. They recommended that future studies look at various methodologies, such as fuzzy logic and fuzzy cognitive maps (FCM), to examine timely fire and smoke incident detection.  
[PERSON] et al. [137] have presented OAK4 XAI (model towards out-of-box explainable artificial intelligence), an XAI system for text data analysis that combines domain knowledge semantics via an ontology and knowledge map model. To describe the knowledge mined in agriculture, they developed the agriculture computer ontology (AgriComO), a well-structured framework suitable for agriculture and computer domains. In future research, the authors intend to create an explanation interface as a service for user engagement and to expand the model by integrating multiple ML algorithms for prediction utilizing explainable methodologies.