Preharvest crop yield estimation is crucial for achieving food security and managing crop growth. Unmanned aerial vehicles (UAVs) can quickly and accurately acquire field crop growth data and are important mediums for collecting agricultural remote sensing data. With the rapid development of machine learning, especially deep learning, research on yield estimation based on UAV remote sensing data and machine learning has achieved excellent results. This paper systematically reviews the current research of yield estimation research based on UAV remote sensing and machine learning through a search of 76 articles, covering aspects such as the grain crops studied, research questions, data collection, feature selection, optimal yield estimation models, and optimal growth periods for yield estimation. Through visual and narrative analysis, the conclusion covers all the proposed research questions. Wheat, corn, rice, and soybeans are the main research objects, and the mechanisms of nitrogen fertilizer application, irrigation, crop variety diversity, and gene diversity have received widespread attention. In the modeling process, feature selection is the key to improving the robustness and accuracy of the model. Whether based on single modal features or multimodal features for yield estimation research, multispectral images are the main source of feature information. The optimal yield estimation model may vary depending on the selected features and the period of data collection, but random forest and convolutional neural networks still perform the best in most cases. Finally, this study delves into the challenges currently faced in terms of data volume, feature selection and optimization, determining the optimal growth period, algorithm selection and application, and the limitations of UAVs. Further research is needed in areas such as data augmentation, feature engineering, algorithm improvement, and real-time yield estimation in the future.

Crop phenology is an important attribute of crops, not only reflecting the growth and development of crops, but also affecting crop yield. By observing the phenological stages, agricultural production losses can be reduced and corresponding systems and plans can be formulated according to their changes, having guiding significance for agricultural production activities. Traditionally, crop phenological stages are determined mainly by manual analysis of remote sensing data collected by UAVs, which is time-consuming, labor-intensive, and may lead to data loss. To cope with this problem, this paper proposes a deep-learning-based method for rice phenological stage recognition. Firstly, we use a weather station equipped with RGB cameras to collect image data of the whole life cycle of rice and build a dataset. Secondly, we use object detection technology to clean the dataset and divide it into six subsets. Finally, we use ResNet-50 as the backbone network to extract spatial feature information from image data and achieve accurate recognition of six rice phenological stages, including seedling, tillering, booting jointing, heading flowering, grain filling, and maturity. Compared with the existing solutions, our method guarantees long-term, continuous, and accurate phenology monitoring. The experimental results show that our method can achieve an accuracy of around 87.33%, providing a new research direction for crop phenological stage recognition.

The sustainable development of agriculture is closely related to the adoption of precision agriculture techniques, and accurate crop classification is a fundamental aspect of this approach. This study explores the application of machine learning techniques to crop classification by integrating RGB images and multispectral data acquired by UAVs. The study focused on five crops: rice, soybean, red bean, wheat, and corn. To improve classification accuracy, the researchers extracted three key feature sets: band values and vegetation indices, texture features extracted from a grey-scale co-occurrence matrix, and shape features. These features were combined with five machine learning models: random forest (RF), support vector machine (SVM), k-nearest neighbour (KNN) based, classification and regression tree (CART) and artificial neural network (ANN). The results show that the Random Forest model consistently outperforms the other models, with an overall accuracy (OA) of over 97% and a significantly higher Kappa coefficient. Fusion of RGB images and multispectral data improved the accuracy by 1–4% compared to using a single data source. Our feature importance analysis showed that band values and vegetation indices had the greatest impact on classification results. This study provides a comprehensive analysis from feature extraction to model evaluation, identifying the optimal combination of features to improve crop classification and providing valuable insights for advancing precision agriculture through data fusion and machine learning techniques.

Technological advances have dramatically improved precision agriculture, and accurate crop classification is a key aspect of precision agriculture (PA). The flexibility and real-time nature of UAVs have led them to become an important tool for acquiring agricultural data and enabling precise crop classification. Currently, crop identification relies heavily on complex high-precision models that often struggle to provide real-time performance. Research on lightweight models specifically for crop classification is also limited. In this paper, we propose a crop classification method based on UAV visible-light images based on PP-LiteSeg, a lightweight model proposed by Baidu. To improve the accuracy, a pyramid pooling module is designed in this paper, which integrates adaptive mean pooling and CSPC (Convolutional Spatial Pyramid Pooling) techniques to handle high-resolution features. In addition, a sparse self-attention mechanism is employed to help the model pay more attention to locally important semantic regions in the image. The combination of adaptive average pooling and the sparse self-attention mechanism can better handle different levels of contextual information. To train the model, a new dataset based on UAV visible-light images including nine categories such as rice, soybean, red bean, wheat, corn, poplar, etc., with a time span of two years was created for accurate crop classification. The experimental results show that the improved model outperforms other models in terms of accuracy and prediction performance, with a MIoU (mean intersection ratio joint) of 94.79%, which is 2.79% better than the original model. Based on the UAV RGB images demonstrated in this paper, the improved model achieves a better balance between real-time performance and accuracy. In conclusion, the method effectively utilizes UAV RGB data and lightweight deep semantic segmentation models to provide valuable insights for crop classification and UAV field monitoring.

Grain mildew is a significant hazard that causes food loss and poses a serious threat to human health when severe. Therefore, effective prediction and determination of mildew grade is essential for the prevention and control of mildew and global food security. In the present study, a model for predicting and determining the mildew grade of rice was constructed using logistic regression, back propagation neural network and GS-SVM (a grid search-based support vector machine algorithm) based on laboratory culture data and actual data from a granary, respectively. The results show that the GS-SVM model has a better prediction effect, but the model cannot automatically adjust the parameters and is more subjective, and the accuracy may decrease when the data set changes. Therefore, this paper establishes a new model for a support vector machine based on a fruit fly optimization algorithm (FOA-SVM), which can achieve automatic parameter search and automatically adjust its parameters to find the best result when the data set changes, with a strong ability of self-adjustment of parameters. In addition, the FOA-SVM converges quickly and the model is stable. The results of this study provide a technical method for early identification of mildew grade during grain storage, which is beneficial for the prevention and control of rice mildew during grain storage.

Quick and accurate prediction of crop yields is beneficial for guiding crop field management and genetic breeding. This paper utilizes the fast and non-destructive advantages of an unmanned aerial vehicle equipped with a multispectral camera to acquire spatial characteristics of rice and conducts research on yield estimation in an open environment. The study proposes a yield estimation framework that hybrids synthetic minority oversampling technique (SMOTE) and deep neural network (DNN). Firstly, the framework used the Pearson correlation coefficient to select 10 key vegetation indices and determine the optimal feature combination. Secondly, it created a dataset for data augmentation through SMOTE, addressing the challenge of long data collection cycles and small sample sizes caused by long growth cycles. Then, based on this dataset, a yield estimation model was trained using DNN and compared with partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF). The experimental results indicate that the hybrid framework proposed in this study performs the best (R² = 0.810, RMSE = 0.69 t/ha), significantly improving the accuracy of yield estimation compared to other methods, with an R² improvement of at least 0.191. It demonstrates that the framework proposed in this study can be used for rice yield estimation. Additionally, it provides a new approach for future yield estimation with small sample sizes for other crops or for predicting numerical crop indicators.