Integration of Remote Sensing and Machine Learning for Precision Agriculture: A Comprehensive Perspective on Applications
Abstract
:1. Introduction
2. Remote Sensing Technology and the Machine Learning Method
2.1. Remote Sensing Data in Precision Agriculture
2.2. Overview of the Use of ML Algorithms in Precision Agriculture
3. Integrated Application of Remote Sensing Technology and the Machine Learning Method
3.1. Agricultural Monitoring and Identification
3.2. Stress Detection of Diseases and Insect Pests
3.3. Management and Analysis of Soil and Land
3.4. Prediction and Decision Making Regarding Crop Yield
4. Discussion
4.1. Current Challenges
4.1.1. Acquisition and Processing of Multi-Source RS Data
4.1.2. Interpretability and Generalization of the Model
4.2. Prospects for the Future
4.2.1. Trend of Intelligence and Automation
4.2.2. Data Sharing and Multidisciplinary Interaction
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Model Name | Application of Precision Agriculture | Reference | |
---|---|---|---|
Supervised Learning | Naive Bayes | Classification of different crop diseases, soil types, etc.; prediction of the yield of wheat, corn, and other crops. | [76,77] |
Logistic Regression | Assessment of the risk level of pest occurrence; prediction of the yield of wheat, corn, and other crops. | [78,79] | |
Linear Regression | Optimization of the amount of fertilizer application to improve the prediction accuracy of wheat, corn, and other crops yield. | [80,81] | |
Lasso Regression | Detection of the extent to which crops are attacked by diseases and insect pests. | [82,83] | |
AdaBoosT Algorithm | Classification and identification of different crop species and detection of crop diseases and insect pests. | [84,85] | |
Linear Discriminant Analysis | Classification of soil types, identification of crop varieties, and determination of the effects of different soil fertilities on crop growth. | [86,87] | |
Recurrent Neural Network | Analysis of crop growth time series data and prediction of time series changes in crop diseases and insect pests. | [88,89] | |
Decision Tree | Selection of pest management strategies; identification of crop pest types. | [90,91] | |
Nearest Neighbor Algorithm | Identification of different crop varieties; evaluation of soil fertility grades. | [92,93] | |
XGBoost Algorithm | Prediction of yield of wheat, corn, and other crops based on climate, soil conditions, and other variables. | [84,85] | |
Long Short-Term Memory Network | Forecasting the long-term trend of crop yield based on climate variables, such as precipitation and temperature, and prediction of the outbreak of crop diseases and insect pests by time series. | [94,95] | |
Support Vector Regression | Crop growth monitoring and modeling, using remote sensing reflectance data to predict crop leaf area index, yield, etc. | [80,96] | |
Artificial Neural Network | Identification of crop diseases and insect pests; crop growth monitoring and modeling; prediction of crop leaf area index, yield, etc. | [97,98] | |
Convolutional Neural Algorithm | Identification of crop leaf diseases and detection of disease invasion degree of crop leaves; prediction of crop leaf area index, yield, etc. | [87,99] | |
Random Forest | Identification of crop diseases and insect pests; crop growth monitoring and modeling; prediction of crop leaf area index, yield, etc. | [100,101] | |
Support Vector Machine | Identification of crop diseases and insect pests; crop growth monitoring and modeling; prediction of crop leaf area index, yield, etc. | [102,103] | |
CatBoosT Algorithm | Identification of crop leaf diseases and detection of disease invasion degree of crop leaves. | [96,104] | |
Ridge Regression | Prediction of soil nutrients and key nutrient content based on soil sample data. | [105,106] | |
Random Gradient Descent | Optimization of model parameters to improve the accuracy of agricultural prediction and decision-making models; application to complex agricultural system modeling and prediction. | [107,108] | |
Semi supervised learning | Generative Semi-Supervised Learning | Assessment of soil quality; prediction of soil fertility, acidity, alkalinity, etc.; prediction and control of diseases and insect pests. | [109,110] |
Autoencoders | Identification and classification of diseases and insect pests; assessment of the risk level of pest occurrence. | [111] | |
Unsupervised | Co-Training | Identification, classification, and risk assessment of diseases and insect pests; soil type classification. | [112] |
Learning | Probabilistic Graphical Model | Identification of crop diseases and insect pests; crop growth monitoring and modeling; prediction of crop leaf area index, yield, etc. | [113] |
Independent Component Analysis | Identification, classification, and risk assessment of diseases and insect pests; soil type classification. | [114] | |
Anomaly Detection Algorithm | Detection of crop wilt, soil moisture, and pH anomaly. | [115] | |
Self-Organizing Maps | Classification of crops and rapid identification of soil types. | [116] | |
K-Means Clustering | Accurate identification of crops. | [117] | |
Principal Component Analysis | Accurate classification of crops based on their growth characteristics (such as color, texture, size, etc.). | [87] | |
Reinforcement | Deep Q-Network | Retrieval of key growth information, such as vegetation index, to effectively monitor crop growth and development. | [118] |
Policy Gradient Methods | Optimization of crop irrigation and fertilization strategies. | [89] | |
Q-learning | Optimization of agricultural decision making and environmental interaction. | [119] |
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Wang, J.; Wang, Y.; Li, G.; Qi, Z. Integration of Remote Sensing and Machine Learning for Precision Agriculture: A Comprehensive Perspective on Applications. Agronomy 2024, 14, 1975. https://doi.org/10.3390/agronomy14091975
Wang J, Wang Y, Li G, Qi Z. Integration of Remote Sensing and Machine Learning for Precision Agriculture: A Comprehensive Perspective on Applications. Agronomy. 2024; 14(9):1975. https://doi.org/10.3390/agronomy14091975
Chicago/Turabian StyleWang, Jun, Yanlong Wang, Guang Li, and Zhengyuan Qi. 2024. "Integration of Remote Sensing and Machine Learning for Precision Agriculture: A Comprehensive Perspective on Applications" Agronomy 14, no. 9: 1975. https://doi.org/10.3390/agronomy14091975