Soil moisture balance and its application to yield monitoring of rainfed paddy at Raipur

The study presents a technique for computing soil moisture balance of paddy crop based on 10 years data from 1977-1988 except 1984 and 1987, from agro-meteorological observatory, Raipur Values of different components of the moisture balance have been determined and discussed. The stress values have been used to estimate possible yield reduction by devising an yield index. The study revealed that water demand of the plant is largest between tillering and reproductive stages. Dry matter production appears directly related to the consumptive water use. A significant association is observed between the yield index and the actual rice yield.

NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019

An accurate paddy rice map is crucial for ensuring food security, particularly for Southeast and Northeast Asia. MODIS satellite data are useful for mapping paddy rice at continental scales but have a mixed-pixel problem caused by the coarse spatial resolution. To reduce the mixed pixels, we designed a rule-based method for mapping paddy rice by integrating time series Sentinel-1 and MODIS data. We demonstrated the method by generating annual paddy rice maps for Southeast and Northeast Asia in 2017–2019 (NESEA-Rice10). We compared the resultant paddy rice maps with available agricultural statistics at subnational levels and existing rice maps for some countries. The results demonstrated that the linear coefficient of determination (R2) between our paddy rice maps and agricultural statistics ranged from 0.80 to 0.97. The paddy rice planting areas in 2017 were spatially consistent with the existing maps in Vietnam (R2=0.93) and Northeast China (R2=0.99). The spatial distribution of the 2017–2019 composite paddy rice map was consistent with that of the rice map from the International Rice Research Institute. The paddy rice planting area may have been underestimated in the region in which the flooding signal was not strong. The dataset is useful for water resource management, rice growth, and yield monitoring. The full product is publicly available at https://doi.org/10.5281/zenodo.5645344 (Han et al., 2021a). Small examples can be found from the following DOI: https://doi.org/10.17632/cnc3tkbwcm.1 (Han et al., 2021b).

Potential of impact-based weight sensing of individual Chinese cabbage for real-time yield monitoring

Yield monitoring is one of the basic components of precision agriculture. It provides information on the spatial variability of yield in the field. The objective of this study was to estimate the weight of Chinese cabbage through impact force measurements using a weighing sensor. The sensor was installed to receive the impact of Cabbage as it dropped off an inclined conveyor. The investigated experimental variables were fall height, plate angle to the horizontal, and conveyor speed. The load cell was calibrated, with an R2 value of 0.96. The mean absolute percentage error (MAPE), root-mean-square error (RMSE), and correlation coefficient (R2) were the statistical indicators used to describe the accuracy of the estimates. The fall height had little effect on the precision of the yield sensor. The least sensing precision was 18.75%, observed at an angle of 30° with a conveyor speed of 0.05ms-1. The MAPE for cabbage weights were<3% at 40° and 0.2 ms−1 for both fall heights. The experiments showed the potential of measuring individual cabbage weights for yield estimation.

Multi sensor data and temporal image fusion cross validation technique for Agri yield monitoring system

In recent years, the diverse application in various disciplines and the versatility has gained a huge interest for the researchers to research on the multi-sensor data fusion technology. The remote sensing process involves the measurement and recording of the data from a scene. Thus, the remote sensing systems are known to be a powerful tool as they help in the earth's atmosphere and surface monitor at different scales. The remote sensing of the data faces a serious challenge as the data captured by the multiple sensors are heterogeneous. This affects the efficient processing and the effectiveness of the data that is being sensed. Thus, the increase in the diversity in data increases the ancillary datasets. These multimodal datasets are used jointly to improve the processing performance as per the application requirement. Initially, the fusion of the temporal data with the backscattered/temporal data is possible from the data retrieved from remote sensing. Many researchers made several types of research on fusing the multi-temporal and multimodal data and gave different ideas for a different type of researchers. This paper presents the cross-validation technique for monitoring the yield. This monitoring system is developed by fusing the multi-sensor data and the temporal images. This fusion is performed, and the performance of the yield monitoring system is analyzed from the results obtained. By using the cross-validation technique, the efficiency of the system is found to be improved.

A Grain Yield Sensor for Yield Mapping with Local Rice Combine Harvester

Rice grain yield was estimated from a locally made Thai combine harvester using a specially developed sensing and monitoring system. The yield monitoring and sensing system, mounted on the rice combine harvester, collected and logged grain mass flow rate and moisture content, as well as pertinent information related to field, position and navigation. The developed system comprised a yield meter, GNSS receiver and a computer installed with customized software, which, when assembled on a local rice combine, mapped real-time rice yield along with grain moisture content. The performance of the developed system was evaluated at three neighboring (identically managed) rice fields. ArcGIS® software was used to create grain yield map with geographical information of the fields. The average grain yield values recorded were 3.63, 3.84 and 3.60 t ha−1, and grain moisture contents (w.b.) were 22.42%, 23.50% and 24.71% from the three fields, respectively. Overall average grain yield was 3.84 t ha−1 (CV = 63.68%) with 578.10 and 7761.58 kg ha−1 as the minimum and maximum values, respectively. The coefficients of variation in grain yield of the three fields were 57.44%, 63.68% and 60.41%, respectively. The system performance was evaluated at four different cutter bar heights (0.18, 0.25, 0.35 and 0.40 m) during the test. As expected, the tallest cutter bar height (0.40 m) offered the least error of 12.50% in yield estimation. The results confirmed that the developed grain yield sensor could be successfully used with the local rice combine harvester; hence, offers and ‘up-gradation’ potential in Thai agricultural mechanization.

AsiaRiceMap10m: high-resolution annual paddy rice maps for Southeast and Northeast Asia from 2017 to 2019

An accurate paddy rice map is crucial for food security, particularly for Southeast and Northeast Asia. The MODIS satellite data is useful for mapping paddy rice at continental scales but has a problem of mixed-pixel due to coarse spatial resolution. To reduce the mixed pixels, we designed a rule-based method for mapping paddy rice by integrating time-series Sentinel-1 and MODIS data. We demonstrated the method in generating annual paddy rice maps for Southeast and Northeast Asia in 2017–2019 (AsiaRiceMap10m). The resultant paddy rice maps were compared with available agricultural statistics at subnational levels and existing rice maps for some countries. The results show that the linear coefficient of determination (R2) between our paddy rice maps and agricultural statistics ranges from 0.80 to 0.97. The paddy rice planting areas in 2017 were spatially consistent with the existing maps in Vietnam (R2 = 0.93) and Northeast China (R2 = 0.99). The spatial distribution of the 2017–2019 composite paddy rice map is consistent with the rice map from the International Rice Research Institute. The paddy rice planting area may be underestimated in the region where the flooding signal is not strong. The dataset is useful for water resource management, rice growth, and yield monitoring. The full product is publicly available at https://doi.org/10.17632/j34b3jsvr9.1 (Han et al., 2021a). Find small examples here (https://doi.org/10.17632/cnc3tkbwcm.1) (Han et al., 2021b).

Перспективи розвитку зерновиробництва в Кіровоградській області

The purpose of the article is to explore the prospects for the development of grain production in the Kirovograd region. Research methods. The study used dialectical methods of cognition, a systematic approach to the study of economic phenomena, monographic (generalizing domestic and foreign experience in determining the economic essence of food security), analysis and synthesis (in studying the levels of production of cereals and legumes and the level of grain supply), statistical groups (in identifying the place of regions in the national production of cereals), economic and statistical (in the processing of mass statistics), abstract and logical (in substantiating theoretical generalizations and drawing conclusions). Research results. The potential of grain production development in Kirovohrad region is considered. The analysis is based on statistical data of the crop industry. Problems of industry development are revealed and ways of solutions are offered. Scientific novelty. Analysis of the level of crop production in the Kirovohrad region in terms of using the latest technologies and yield monitoring. Practical significance. Along with some positive changes in the region's grain production, obstacles to their development have been identified. Proposals have been made to eliminate them with active investments and state support programs for the agricultural sector of the region. Tabl.: 4. Figs.: 1. Refs.: 18.