Drought stress is one of the key factors restricting crop yield. The beneficial effects of exogenous proline on crop growth under drought stress have been demonstrated in maize, rice, and other crops. However, little is known about its effects on wheat under drought stress. Especially, the water-holding capacity of leaves were overlooked in most studies. Therefore, a barrel experiment was conducted with wheat at two drought levels (severe drought: 45% field capacity, mild drought: 60% field capacity), and three proline-spraying levels (0 mM, 25 mM, and 50 mM). Meanwhile, a control with no stress and no proline application was set. The anatomical features, water-holding capacity, antioxidant capacity, and proline content of flag leaves as well as grain yields were measured. The results showed that drought stress increased the activity of catalase and peroxidase and the content of proline in flag leaves, lessened the content of chlorophyll, deformed leaf veins, and decreased the grain yield. Exogenous proline could regulate the osmotic-regulation substance content, chlorophyll content, antioxidant enzyme activity, water-holding capacity, and tissue structure of wheat flag leaves under drought stress, ultimately alleviating the impact of drought stress on wheat yield. The application of proline (25 mM and 50 mM) increased the yield by 2.88% and 10.81% under mild drought and 33.90% and 52.88% under severe drought compared to wheat without proline spray, respectively.

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.

In recent years, the alternating occurrence of high temperature and waterlogging disasters in South China has seriously reduced the yield of single cropping rice. Studying the changes in anatomical structure of the rice root system could provide theoretical basis for understanding the mechanisms of high temperature and waterlogging stress. To examine interactions between temperature and waterlogging stress, an experiment was set up in a growth chamber consisting of two temperatures (moderate, 30–34 °C and high, 35–38 °C) with three depths of flooding (0–5, 10 and 15 cm). Treatments commenced at the booting stage and lasted five days, after which all treatments were returned to a 0–5 cm flooding depth and the same temperature regime. Observations were made immediately after cessation of treatments, then after 5, 10 and 20 d to test the effect of treatments on subsequent root anatomical development. The low-stress control (0–5 cm, medium temperature) showed no change with time in aerenchyma area, thickness of the outer root, stele diameter, and the number nor diameter of xylem vessels. Root diameter and stele diameter under the high-stress control (0–5 cm, high temperature) were decreased by 29.09% and 15.28%, respectively, at the booting stage, whereas the high stress control (15 cm, high temperature) affected only the vessel diameter, reducing it by 14.11% compared with that in the low-stress control (0–5 cm, medium temperature). Compared to the high-stress control (0–5 cm, high temperature), the interaction of high temperature and waterlogging stress alleviated the inhibiting effect of the changes in the root system, especially after the end of the stress. We thought that waterlogging could reduce the damage of high temperature on rice root growth. Low water depth waterlogging has little effect on rice root system and aerenchyma area root diameter at 0, 5, 10 and 20 d after the stress ended, and the thickness of the outer root, stele diameter and the number and diameter of vessels at 0 d under M15 (15 cm, medium temperature) had no significant difference compared with M5 (0–5 cm, medium temperature). However, the increase in rice root diameter, stele diameter, thickness of the outer root cortex depth and vessel diameter were inhibited under high temperature stress at the booting stage. Root diameter and stele diameter under H5 (0–5 cm, high temperature) were decreased by 29.09% and 15.28%, respectively, at the booting stage, whereas H15 (15 cm, high temperature) affected only the vessel diameter, reducing it by 14.11% compared with that in the M5. Compared to H5, the interaction of high temperature and waterlogging stress alleviated the inhibiting effect of the changes in the root system, especially after the end of the stress. We thought that waterlogging could lighten the damage of high temperature on rice root growth.

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.