Search Results (3,375)

Rice is a primary food crop, and rice production ensures food security and maintains social stability with great significance. Flooding paddy rice fields as an important step in rice production affects the entire growth process of rice. The selection of flooding time is highly correlated with paddy rice yield and water resource utilization. In the background of global warming, early flooding in high-latitude paddy rice planting areas can ensure that rice has sufficient growing time to increase yield. However, overly early flooding may cause waste of water resources due to insufficient heat. Currently, research on flooding timing is relatively lacking, and monitoring of temperature during flooding is particularly deficient. To respond to climate change, it is necessary to explore whether the current flooding schedule meets the actual needs. Based on MODIS surface reflectivity data, we identified the First Flooding Day (FFD) and Peak Flooding Day (PFD) in the Sanjiang Plain. Using MODIS Land Surface Temperature (LST) data and meteorological station-provided air temperature data, we analyzed the corresponding LST and air temperature for PFD from 2008 to 2024. The main conclusions are as follows: (1) both FFD and PFD in the Sanjiang Plain have a trend of advancing year by year, with PFD showing stronger advancement than FFD; (2) the LST and air temperature during flooding in the Sanjiang Plain show a downward trend year by year; and (3) by 2024, the flooding temperature of paddy rice fields in the Sanjiang Plain has generally met the needs for the next step of production. This study first attempts to use high-temporal-resolution remote sensing images to identify the flooding time of paddy fields and achieve timely monitoring of flooding and changes in flooding temperature. Full article

Rice blast is a highly damaging disease that greatly impacts both the quality and yield of rice. Timely identification and monitoring of this disease are essential for effective agricultural management and for ensuring optimal crop performance. The spectral vegetation index has been widely used in the identification of crop diseases. However, a limitation of these indices is that they cannot identify diseases at different scales. This study aimed to address these issues by developing the rice blast-specific hyperspectral Geometry Ratio Vegetation Index (GRVI_RB) for monitoring rice blast disease at the leaf and canopy scales. The sensitive bands for identifying rice blast disease were 688 nm, 756 nm, and 1466 nm using the successive projection algorithm. Based on these three sensitive bands and the spectral response mechanism of rice blast, the GRVI_RB was designed. GRVI_RB demonstrated high classification accuracy using SVM (support vector machine) and LDA (Linear Discriminant Analysis) models in leaf-scale and canopy-scale datasets from 2020 and 2021, surpassing the current vegetation indices of rice blast detection. It is demonstrated that the GRVI_RB has excellent robustness and universality for rice blast detection from leaf to canopy scales in different years. Additionally, the research suggests that the new hyperspectral vegetation index can serve as a valuable reference for studies conducted at both unmanned aerial vehicle and satellite scales. Full article

Sprinkler irrigation is an effective method for protecting economic crops from frost damage; however, current research on its impacts is insufficient and lacks comprehensive evaluation. This research investigated the effects of sprinkler irrigation for frost protection on the air, soil, and tea plants in the tea garden. Sprinkler frost protection experiments were conducted in the tea garden, where temperature sensors measured the air and soil temperatures, and Monitoring-PAM was used to measure the chlorophyll fluorescence parameters (Fv/Fm) of the tea plants. The results indicated that lower initial ambient temperatures or smaller droplet sizes accelerate the rate of air temperature increase and slow the cooling rate. Under conditions of heavy frost, ice formation from irrigation water acts as an insulating layer, protecting the inter-row soil. Additionally, the Fv/Fm values of tea leaves protected by sprinkler irrigation ranged from 0.6 to 0.7, and were significantly higher than those of leaves exposed to frost damage. The results also showed that air and soil temperature and tea Fv/Fm can be used to perform a comprehensive assessment of sprinkler frost protection effectiveness. Full article

Xinjiang is an important region due to its unique epidemic characteristics of wheat stripe rust disease caused by Puccinia striiformis f. sp. tritici. Some previous studies on race identification were conducted in this region, but it is still unclear how temporal changes affect the dynamics, diversity, and virulence characteristics of Pst races in Xinjiang. To gain a better understanding, we compared the race data from spring and winter wheat crops of 2022 with that of 2021. Our results showed a significant change in virulence frequency in 2022. Vr10, Vr13, and Vr19 exhibited an increasing trend, with a frequency of ≥18%, while the maximum decline was observed in Vr1, Vr3, and Vr9, with a frequency of ≤−25%. It was found that Yr5 and Yr15 remained effective against Xinjiang Pst races. The race diversity increased from 0.92 (70 races out of 345 isolates) to 0.94 (90 races out of 354 isolates) in 2022, with G22G being the dominant race group. Race CYR34 became prevalent in the region in 2022, while the LvG grouped was wiped out in 2022, from both summer and winter crop seasons. HyG and SuG groups showed an overall declining trend. Overall prevalent races showed over-summering and over-wintering behaviors in Xinjiang. The number of new races occurrence frequency increased by 34% in 2022, indicating a potential change in the population structure of Pst. It is crucial to introduce newly resistant gene cultivars in this region and to establish rust-monitoring protocols to prepare for any future epidemics. Full article

This study explores agroecological practices designed to improve soil quality and crop yield in small-scale agriculture, focusing on soil inoculation with beneficial bacteria over conventional fertilizers. Conducted at the SZIA Agroecological Garden MATE in Gödöllő, Hungary, the research utilizes 12 plots to evaluate different conservation tillage methods, including minimum and no-tillage, with and without microbial inoculation. Commenced in 2022, this study centers on potato cultivation (Solanum tuberosum L.) and includes comprehensive chemical and physical analyses of soil and harvested potatoes, alongside continuous monitoring of growth. Statistical analysis using One-way Anova in R revealed p-values predominantly above 0.05, indicating no significant differences across most parameters, though variations in soil plasticity and pH (KCl) were noted. Results suggest that substantial treatmeent differences may require a longer observation period. Notably, plots with microbial inoculation exhibited higher harvest weights and tuber sizes compared to control plots. Additionally, trends and interactions were found between weed abundance, total harvest, and plant height. The findings indicate that the benefits of integrated agroecological practices, including conservation tillage, may take time to materialize, emphasizing the necessity for extended observation. This research lays the groundwork for future studies, underscoring the importance of patience in achieving improvements in soil health and crop quality through sustainable agricultural methods. Full article

The rice sector is facing the challenge of increasing rice yields while maintaining or improving input use efficiency. The purpose of this study was to determine the most effective vegetation indices for monitoring nitrogen uptake (N uptake) under different irrigation techniques. The study was conducted in Uruguay over two rice-growing seasons. A split plot experimental design featured two irrigation treatments (main plots): continuous flooding (C) and alternate wetting and drying (AWD). The nitrogen-rate (N-rate) treatments (split plots) included no nitrogen, the recommended N-rate based on soil analyses, and two additional doses (±50% of the recommendation). The plant N uptake relationships with selected drone-based vegetation indices (VIs) were assessed at panicle initiation. The presence or absence of standing water during image collection affected the VIs and their relationships with N uptake. The relationships estimated for traditional irrigation may not be applicable for AWD. The SCCCI was the top index with a significantly stronger relationship with N uptake under the C (R² = 0.84) and AWD (R² = 0.71) irrigation techniques in relation to all evaluated vegetation indices. The Clre, NDRE², NDRE, and CLg also had a significant relationship with N uptake under both irrigation treatments in both seasons, though their average R² values of 0.75, 0.74, 0.73, and 0.71, respectively, were lower than the SCCCI (average R² = 0.78). The findings would assist rice growers for selecting effective VIs for remote crop monitoring. Full article

Sugarcane mechanized harvesting generates large volumes of data that are used to monitor harvesters’ functionalities. The dynamic interaction of the machine-onboard instrumentation–crop system introduces discrepant and noisy values into the data, requiring outlier detectors to support this complex and empirical decision. This study proposes an automatic filtering technique for sugarcane harvesting data to automate the process. A three-step automated filtering algorithm based on a sliding window was developed and further evaluated with four configurations of the maximum variation factor f and six SW sizes. The performance of the proposed method was assessed by using artificial outliers in the datasets with an outlier magnitude (OM) of ±0.01 to ±1.00. Three case studies with real crop data were presented to demonstrate the effectiveness of the proposed filter in detecting outliers of different magnitudes, compared to filtering by another method in the literature. In each dataset, the proposed filter detected nearly 100% of larger (OM = ±1.00 and ±0.80) and medium (OM = ±0.50) magnitudes’ outliers, and approximately 26% of smaller outliers (OM = ±0.10, ±0.05, and ±0.01). The proposed algorithm preserved wider ranges of data compared to the comparative method and presented equivalent results in the identification of regions with different productive potentials of sugarcane in the field. Therefore, the proposed method retained data that reflect sugarcane yield variability at the row level and it can be used in practical application scenarios to deal with large datasets obtained from sugarcane harvesters. Full article

Timely and accurate yield estimation is essential for effective crop management and the grain trade. Remote sensing has emerged as a valuable tool for monitoring rice yields; however, many studies concentrate on a single period or simply aggregate multiple periods, neglecting the complexities underlying yield formation. The study enhances yield estimation by integrating cumulative time series vegetation indices (VIs) from multispectral (MS) and RGB (Red, Green, Blue) sensors to identify optimal combinations of growth periods. We utilized two unmanned aerial vehicle to capture spectral information from rice canopies through MS and RGB sensors. By analyzing the correlations between vegetation indices from different sensors and rice yields, the optimal MS-VIs and RGB-VIs for each period were identified. Following this, the relationship between the cumulative time series of MS-VIs, RGB-VIs, and rice yields was further examined. The results demonstrate that the booting stage is a crucial growth period influencing rice yield, with VIs exhibiting increased correlation with yield, peaking during this stage before declining. For the MS sensor, the rice yield model, based on the cumulative time series of MS-VIs from the tillering stage to the panicle initiation stage, achieves optimal accuracy (R² = 0.722, RRMSE = 0.555). For the RGB sensor, the rice yield model, based on the cumulative time series of RGB-VIs from the tillering stage to the grain-filling stage, yields the highest accuracy (R² = 0.727, RRMSE = 0.526). In comparison, the multi-sensor rice yield model, which combines the cumulative time series of MS-VIs from the tillering stage and RGB-VIs from the panicle initiation to grain-filling stages, achieves the highest accuracy with R² = 0.759 and RRMSE = 0.513. These findings suggest that cumulative time series VIs and the integration of multiple sensors enhance yield prediction accuracy, providing a comprehensive approach for estimating rice yield dynamics and supporting precision agriculture and informed crop management. Full article

The beet armyworm Spodoptera exigua (Hübner), a global pest, feeds on and affects a wide range of crops. Its long-distance migration with the East Asian monsoon frequently causes large-scale outbreaks in East and Southeast Asia. This pest mainly breeds in tropical regions in the winter season every year; however, few studies have investigated associations with its population movements in this region. From 2017 to 2023, we monitored its population dynamics in a tropical site, located in Hainan Province of China, using a searchlight trap. Dissection of the ovaries of female S. exigua moths captured from the air revealed that most of them were reproductively mature and could be classified as a transit migratory population. Migration occurred most often in summer and least often in winter, with an increasing trend over the years. According to a trajectory model analysis based on the Weather Research and Forecasting (WRF) model, S. exigua migrated from Hainan Island to mainland China in the spring, primarily moved from the areas of Southeast Asia to Hainan and mainland China during the summer, and returned from China to Southeast Asia in the autumn and winter. Overall, our research defines the movement paths of S. exigua in the tropical area of China, establishing a theoretical foundation for its regional monitoring, early warning, and management in China and Southeast Asian countries. Full article

Closed hydroponics is an environmentally friendly and economical method for growing crops by circulating a nutrient solution while measuring and supplementing various ions contained in the solution. However, conventional monitoring systems in hydroponics do not measure individual ions in the nutrient solution; instead, they predict the total ion content from the pH and electrical conductivity (EC). This method cannot be used to supplement individual ions and adjusts the concentration of the circulating nutrient solution by diluting or supplying a premixed nutrient solution. A more advanced system should be able to identify the concentration of each ion in the nutrient solution and supplement any deficient ions, thus requiring individual ion monitoring systems. Therefore, we first investigated the nitrate, ammonium, phosphate, and potassium (NPK) ion concentration and pH range commonly used for nutrient solutions. Subsequently, we discuss the latest research trends in electrochemical and optical sensors for measuring NPK ions. We then compare the conventional monitoring system (pH and EC-based) and advanced monitoring systems (individual ion sensors) and discuss the respective research trends. In conclusion, we present the hurdles that researchers must overcome in developing agricultural ion sensors for advanced monitoring systems and propose the minimum specifications for agricultural NPK ion sensors. Full article

Broad-spectrum crop protection technologies, such as abamectin and bifenthrin, are globally relied upon to curb the existential threats from economic crop pests such as the generalist herbivore Tetranychus urticae Koch (TSSM). However, the rising cost of discovering and registering new acaricides, particularly for specialty crops, along with the increasing risk of pesticide resistance development, underscores the urgent need to preserve the efficacy of currently registered acaricides. This study examined the overall genetic mechanism underlying adaptation to abamectin and bifenthrin in T. urticae populations from commercial hop fields in the Pacific Northwestern region of the USA. A transcriptomic study was conducted using four populations (susceptible, abamectin-resistant, and two bifenthrin-resistant populations). Differential gene expression analysis revealed a notable disparity, with significantly more downregulated genes than upregulated genes in both resistant populations. Gene ontology enrichment analysis revealed a striking consistency among all three resistant populations, with downregulated genes predominately associated with chitin metabolism. In contrast, upregulated genes in the resistant populations were linked to biological processes, such as peptidase activity and oxidoreductase activity. Proteolytic activity by peptidase enzymes in abamectin- and bifenthrin-resistant TSSM populations may suggest their involvement in acaricide metabolism. These findings provide valuable insights into the molecular mechanisms underlying acaricide resistance in the TSSM. This knowledge can be utilized to develop innovative pesticides and molecular diagnostic tools for effectively monitoring and managing resistant TSSM populations. Full article

The pigment content of rice leaves plays an important role in the growth and development of rice. The accurate and rapid assessment of the pigment content of leaves is of great significance for monitoring the growth status of rice. This study used the Analytical Spectra Device (ASD) FieldSpec 4 spectrometer to measure the leaf reflectance spectra of 4 rice varieties during the entire growth period under 4 nitrogen application rates and simultaneously measured the leaf pigment content. The leaf’s absorption spectra were calculated based on the physical process of spectral transmission. An examination was conducted on the variations in pigment composition among distinct rice cultivars, alongside a thorough dissection of the interrelations and distinctions between leaf reflectance spectra and absorption spectra. Based on the vegetation index proposed by previous researchers in order to invert pigment content, the absorption spectrum was used to replace the original reflectance data to optimize the vegetation index. The results showed that the chlorophyll and carotenoid contents of different rice varieties showed regular changes during the whole growth period, and that the leaf absorption spectra of different rice varieties showed more obvious differences than reflectance spectra. After replacing the reflectance of pigment absorptivity-sensitive bands (400 nm, 550 nm, 680 nm, and red-edge bands) with absorptivities that would optimize the vegetation index, the correlation between the vegetation index, which combines absorptivity and reflectivity, and the chlorophyll and carotenoid contents of 4 rice varieties during the whole growth period was significantly improved. The model’s validation results indicate that the pigment inversion model, based on the improved vegetation index using absorption spectra, outperforms the traditional vegetation index-based pigment inversion model. The results of this study demonstrate the potential application of absorption spectroscopy in the quantitative inversion of crop phenotypes. Full article

The fall armyworm (FAW) is an invasive pest that has been rapidly spreading across China since its detection in Yunnan province in January 2019. Although sugarcane and sorghum have been reported as hosts, their effects on FAW’s population growth and life table parameters have not been examined in China. Our research shows that FAW’s development and life table metrics vary significantly when reared on sorghum, sugarcane, and maize. Notably, the preadult stage, adult preoviposition period, and total preoviposition period of FAW were markedly longer on sugarcane and sorghum compared to maize. FAW reared on these two crops also exhibited reduced survival rates, pupal weight, fecundity, and lower female-to-male ratios. The study highlights that FAW had lower population growth rates, reproductive rates, and longer generation times on sugarcane and sorghum compared to maize. The consumption index and digestibility index were higher on maize, while conversion efficiency and growth rate were greater on sorghum. Although maize is the most favorable host, FAW can still survive and reproduce on sugarcane and sorghum during the nongrowing season, posing a risk to economically important crops in China. Despite being less favorable for population growth, sugarcane and sorghum still support FAW development and spread. Therefore, enhanced surveillance and early warning measures for sugarcane and sorghum are recommended to monitor FAW population dynamics and mitigate its potential impact on primary host plants. Full article

Aphids are significant pests affecting crop yields both through direct feeding and as vectors of viruses. The monitoring focused on 10 of the most important aphid species. This study investigates the dynamics of aphid populations in two Polish regions, Winna Góra (Greater Poland Province) and Sośnicowice (Silesia Province), over a five-year period (2019–2023) using Johnson suction traps. Data collection covered species composition, migration timing, and seasonal variations in aphid abundance. Dominance patterns were assessed using a species-specific index, and inter-regional comparisons were analyzed through correlation and principal component analysis. Results indicate notable population peaks during autumn, suggesting this period is optimal for implementing control measures. The Johnson traps proved valuable for timely pest monitoring, offering predictive potential for future aphid migration, particularly in relation to virus-transmitting species critical to plants. Full article