Non-grain production has emerged as a potential threat to grain production capacity and security in China. Agricultural products with higher economic returns are beginning to replace traditional grain crops, which have relatively low economic returns on a large scale. In this study, we proposed and verified an identification method utilizing an unmanned aerial vehicle and a U-net algorithm to distinguish peach trees in cultivated land; the overall accuracy for verification and prediction were 0.90 and 0.92, respectively. Additionally, a non-grain production index was developed to assess the degree of non-grain production in target plots. The index was 76.90% and 91.38% in the projected plots, representing a high degree of non-grain production. This combination of an identification method and non-grain production index could provide efficient tools for agricultural management to inspect peach trees in cultivated land, thus replacing field measurements to achieve significant labor savings. Furthermore, this method can provide a reference for creating high-standard farmland, sustainable development of cultivated land, and policymaking.

To explore the applicability of different fungicide application schemes to control potato early blight (mainly caused by Alternaria solani) in China, field trials were conducted from 2020 to 2022, combining different fungicides with the tomato forecaster (TOMCAST) model and using weather variables to adjust the minimum temperature of TOMCAST to 7 °C. To effectively manage potato early blight, the TOMCAST model combines relative humidity (>88%) and air temperature to calculate daily severity values (DSVs). The application of fungicides (fungicide schedule) is as follows: untreated; two standard treatments, Amimiaoshou SC and Xishi SC, applied at the first appearance of disease symptoms; and two different TOMCAST treatments, in which fungicides are applied when the physiological days add up to 300 days and DSVs add up to 15. This study quantifies the intensity of early blight as the area under the disease progression curve and the final disease severity. Additionally, a progress curve for early blight is constructed to compare the development of early blight in different years and treatments. The TOMCAST-15 model reduces the number of fungicide applications in addition to significantly suppressing the development of early blight. Furthermore, fungicide application significantly increases the dry matter and starch contents of potatoes, and TOMCAST-15 × Amimiaoshou SC has similar enhancement effects on dry matter, protein, reducing sugar, and starch contents compared with Amomiaohou SC and Xishi SC. As a result, TOMCAST × Amimiaoshou SC may be an effective alternative to the standard treatment and have good applicability in China.

The leaf area index (LAI) serves as a key metric for tracking crop growth and can be integrated into crop models for yield estimation. Although the remote sensing LAI data provide a critical foundation for monitoring crop growth and estimating yields, the existing datasets often exhibit notable errors due to the pixel-level heterogeneity. To improve the applicability and inversion accuracy of MODIS LAI products in the Northeast China (NEC) region, this study upscaled the 500-m resolution MODIS LAI product to a 5-km resolution by initially calculating the mean value. Then, the scale factors were estimated based on the observed LAI data of spring maize. To further refine the accuracy of the remotely sensed LAI, 1-km resolution land use data were resampled to 500-m resolution, and the pixel purity of spring maize was calculated for each 5-km grid cell. The scale factor time series was fitted with and without consideration of pixel purity, and the accuracy of the adjusted LAI using these two methods was compared. Our findings demonstrate that the optimal method for fitting scale factors for spring maize LAI data is piecewise function method which combines Gaussian and quadratic polynomial functions. The time series of scale factors derived from high- and low-purity pixels, differentiated by a 50% purity threshold, resulted in improved performance in adjusting the spring maize LAI compared to traditional remote sensing LAI data. The adjusted LAI performed better in reflecting the growth characteristics of spring maize in the NEC region, with the relative mean square errors between observed and adjusted LAI of spring maize during 2016 and 2020 below 1 m²/m². This study provides crucial support for monitoring the growth process and estimating the yield of spring maize in the NEC region and also offers valuable scientific references for the optimization and application of remote sensing data.

Heat damage is a major abiotic stress that affects maize yield and quality. Although the differential impacts of heat damage during various growth stages have been widely documented, the grade levels of heat damage at different growth stages remain insufficiently quantified. In this study, based on daily maximum temperature data and historical disaster records of heat damage from 1980 to 2023, we quantified the grade indicators for heat damage at different growth stages, using disaster inversion and the K-means clustering method. The results identified that the duration thresholds of mild, moderate, and severe heat damage at different growth stages of summer maize are 3–5 days, 6–8 days, and more than 8 days, respectively. Further analysis revealed that the total station ratio of heat damage of summer maize showed a fluctuating upward trend from 1980 to 2023, and the station ratio at different growth stages reached the highest value in 1988, 2002, 2019, 2022, 2013, and 1999, respectively. Additionally, mild heat damage during sowing to maturity stages was found to be more widely distributed spatially and mainly exhibited a slight increasing trend. This study can provide support for enhancing disaster prevention and mitigation capabilities against different levels of heat damage.