Maize is a globally important cereal and fodder crop. Accurate monitoring of maize planting densities is vital for informed decision-making by agricultural managers. Compared to traditional manual methods for collecting crop trait parameters, approaches using unmanned aerial vehicle (UAV) remote sensing can enhance the efficiency, minimize personnel costs and biases, and, more importantly, rapidly provide density maps of maize fields. This study involved the following steps: (1) Two UAV remote sensing-based methods were developed for monitoring maize planting densities. These methods are based on (a) ultrahigh-definition imagery combined with object detection (UHDI-OD) and (b) multispectral remote sensing combined with machine learning (Multi-ML) for the monitoring of maize planting densities. (2) The maize planting density measurements, UAV ultrahigh-definition imagery, and multispectral imagery collection were implemented at a maize breeding trial site. Experimental testing and validation were conducted using the proposed maize planting density monitoring methods. (3) An in-depth analysis of the applicability and limitations of both methods was conducted to explore the advantages and disadvantages of the two estimation models. The study revealed the following findings: (1) UHDI-OD can provide highly accurate estimation results for maize densities (R² = 0.99, RMSE = 0.09 plants/m²). (2) Multi-ML provides accurate maize density estimation results by combining remote sensing vegetation indices (VIs) and gray-level co-occurrence matrix (GLCM) texture features (R² = 0.76, RMSE = 0.67 plants/m²). (3) UHDI-OD exhibits a high sensitivity to image resolution, making it unsuitable for use with UAV remote sensing images with pixel sizes greater than 2 cm. In contrast, Multi-ML is insensitive to image resolution and the model accuracy gradually decreases as the resolution decreases.

Maize, an important cereal crop and crucial industrial material, is widely used in various fields, including food, feed, and industry. Maize is also a highly adaptable crop, capable of thriving under various climatic and soil conditions. Against the backdrop of intensified climate change, studying the classification of maize growth stages can aid in adjusting planting strategies to enhance yield and quality. Accurate classification of the growth stages of maize breeding materials is important for enhancing yield and quality in breeding endeavors. Traditional remote sensing-based crop growth stage classifications mainly rely on time series vegetation index (VI) analyses; however, VIs are prone to saturation under high-coverage conditions. Maize phenotypic traits at different growth stages may improve the accuracy of crop growth stage classifications. Therefore, we developed a method for classifying maize growth stages during the vegetative growth phase by combining maize phenotypic traits with different classification algorithms. First, we tested various VIs, texture features (TFs), and combinations of VI and TF as input features to estimate the leaf chlorophyll content (LCC), leaf area index (LAI), and fractional vegetation cover (FVC). We determined the optimal feature inputs and estimation methods and completed crop height (CH) extraction. Then, we tested different combinations of maize phenotypic traits as input variables to determine their accuracy in classifying growth stages and to identify the optimal combination and classification method. Finally, we compared the proposed method with traditional growth stage classification methods based on remote sensing VIs and machine learning models. The results indicate that (1) when the VI+TFs are used as input features, random forest regression (RFR) shows a good estimation performance for the LCC (R²: 0.920, RMSE: 3.655 SPAD units, MAE: 2.698 SPAD units), Gaussian process regression (GPR) performs well for the LAI (R²: 0.621, RMSE: 0.494, MAE: 0.397), and linear regression (LR) exhibits a good estimation performance for the FVC (R²: 0.777, RMSE: 0.051, MAE: 0.040); (2) when using the maize LCC, LAI, FVC, and CH phenotypic traits to classify maize growth stages, the random forest (RF) classification method achieved the highest accuracy (accuracy: 0.951, precision: 0.951, recall: 0.951, F1: 0.951); and (3) the effectiveness of the growth stage classification based on maize phenotypic traits outperforms that of traditional remote sensing-based crop growth stage classifications.

Crop leaf chlorophyll content (LCC) and fractional vegetation cover (FVC) are crucial indicators for assessing crop health, growth development, and maturity. In contrast to the traditional manual collection of crop trait parameters, unmanned aerial vehicle (UAV) technology rapidly generates LCC and FVC maps for breeding materials, facilitating prompt assessments of maturity information. This study addresses the following research questions: (1) Can image features based on pretrained deep learning networks and ensemble learning enhance the estimation of remote sensing LCC and FVC? (2) Can the proposed adaptive normal maturity detection (ANMD) algorithm effectively monitor maize maturity based on LCC and FVC maps? We conducted the following tasks: (1) Seven phases (tassel initiation to maturity) of maize canopy orthoimages and corresponding ground-truth data for LCC and six phases of FVC using UAVs were collected. (2) Three features, namely vegetation indices (VI), texture features (TF) based on Gray Level Co-occurrence Matrix, and deep features (DF), were evaluated for LCC and FVC estimation. Moreover, the potential of four single-machine learning models and three ensemble models for LCC and FVC estimation was evaluated. (3) The estimated LCC and FVC were combined with the proposed ANMD to monitor maize maturity. The research findings indicate that (1) image features extracted from pretrained deep learning networks more accurately describe crop canopy structure information, effectively eliminating saturation effects and enhancing LCC and FVC estimation accuracy. (2) Ensemble models outperform single-machine learning models in estimating LCC and FVC, providing greater precision. Remarkably, the stacking + DF strategy achieved optimal performance in estimating LCC (coefficient of determination (R²): 0.930; root mean square error (RMSE): 3.974; average absolute error (MAE): 3.096); and FVC (R²: 0.716; RMSE: 0.057; and MAE: 0.044). (3) The proposed ANMD algorithm combined with LCC and FVC maps can be used to effectively monitor maize maturity. Establishing the maturity threshold for LCC based on the wax ripening period (P5) and successfully applying it to the wax ripening-mature period (P5–P7) achieved high monitoring accuracy (overall accuracy (OA): 0.9625–0.9875; user’s accuracy: 0.9583–0.9933; and producer’s accuracy: 0.9634–1). Similarly, utilizing the ANMD algorithm with FVC also attained elevated monitoring accuracy during P5–P7 (OA: 0.9125–0.9750; UA: 0.878–0.9778; and PA: 0.9362–0.9934). This study offers robust insights for future agricultural production and breeding, offering valuable insights for the further exploration of crop monitoring technologies and methodologies.

A timely and comprehensive understanding of winter wheat maturity is crucial for deploying large-scale harvesters within a region, ensuring timely winter wheat harvesting, and maintaining grain quality. Winter wheat maturity prediction is limited by two key issues: accurate extraction of wheat planting areas and effective maturity prediction methods. The primary aim of this study is to propose a method for predicting winter wheat maturity. The method comprises three parts: (i) winter wheat planting area extraction via phenological characteristics across multiple growth stages; (ii) extraction of winter wheat maturity features via vegetation indices (VIs, such as NDVI, NDRE, NDII1, and NDII2) and box plot analysis; and (iii) winter wheat maturity data prediction via the selected VIs. The key findings of this work are as follows: (i) Combining multispectral remote sensing data from the winter wheat jointing-filling and maturity-harvest stages can provide high-precision extraction of winter wheat planting areas (OA = 95.67%, PA = 91.67%, UA = 99.64%, and Kappa = 0.9133). (ii) The proposed method can offer the highest accuracy in predicting maturity at the winter wheat flowering stage (R² = 0.802, RMSE = 1.56 days), aiding in a timely and comprehensive understanding of winter wheat maturity and in deploying large-scale harvesters within the region. (iii) The study’s validation was only conducted for winter wheat maturity prediction in the North China Plain wheat production area, and the accuracy of harvesting progress information extraction for other regions’ wheat still requires further testing. The method proposed in this study can provide accurate predictions of winter wheat maturity, helping agricultural management departments adopt information-based measures to improve the efficiency of monitoring winter wheat maturation and harvesting, thus promoting the efficiency of precision agricultural operations and informatization efforts.

Conservation tillage involves covering the soil surface with crop residues after harvest, typically through reduced or no-tillage practices. This approach increases the soil organic matter, improves the soil structure, prevents erosion, reduces water loss, promotes microbial activity, and enhances root development. Therefore, accurate information on crop residue coverage is critical for monitoring the implementation of conservation tillage practices. This study collected “crop–crop residues–soil” images from wheat-soybean rotation fields using mobile phones to create calibration, validation, and independent validation datasets. We developed a deep learning model named crop–crop residue–soil segmentation network (CCRSNet) to enhance the performance of cropland “crop–crop residues–soil” image segmentation and proportion extraction. The model enhances the segmentation accuracy and proportion extraction by extracting and integrating shallow and deep image features and attention modules to capture multi-scale contextual information. Our findings indicated that (1) lightweight models outperformed deeper networks for “crop–crop residues–soil” image segmentation. When CCRSNet employed a deep network backbone (ResNet50), its feature extraction capability was inferior to that of lighter models (VGG16). (2) CCRSNet models that integrated shallow and deep features with attention modules achieved a high segmentation and proportion extraction performance. Using VGG16 as the backbone, CCRSNet achieved an mIoU of 92.73% and a PA of 96.23% in the independent validation dataset, surpassing traditional SVM and RF models. The RMSE for the proportion extraction accuracy ranged from 1.05% to 3.56%. These results demonstrate the potential of CCRSNet for the accurate, rapid, and low-cost detection of crop residue coverage. However, the generalizability and robustness of deep learning models depend on the diversity of calibration datasets. Further experiments across different regions and crops are required to validate this method’s accuracy and applicability for “crop–crop residues–soil” image segmentation and proportion extraction.