This study analyses the income effects of farm households' conservation tillage adoption on agricultural income in China. Based on survey data of 704 farm households from Jianghan Plain, central China, Heckman two-stage and propensity score matching models are employed to quantitatively measure the income effect of farm households' adoption of rice and fish co-cultivation, including six aquatic animals. The results reveal that most farmers have adopted rice and fish co-cultivation methods, but with limited adoption intensity. The number of farmers engaged in co-cultivation and farmers' degree of knowledge and cognition significantly influence adoption and increase adoption intensity. Compared with the non-adoption group, the agricultural income of rice and fish co-cultivation system adoption improved adopters’ incomes by 35.15%. In conclusion, rice and fish co-cultivation publicity, technical training and compensation are highly beneficial and recommended. 

Does conservation tillage adoption improve farmers’ agricultural income? A case study of the rice and fish co-cultivation system in Jianghan Plain, China

The problem of small and multi-object polished rice image segmentation has always been one of importance and difficulty in the field of image segmentation. In the appearance quality detection of polished rice, image segmentation is a crucial part, directly affecting the results of follow-up physicochemical indicators. To avoid leak detection and inaccuracy in image segmentation qualifying polished rice, this paper proposes a new image segmentation method (YO-LACTS), combining YOLOv5 with YOLACT. We tested the YOLOv5-based object detection network, to extract Regions of Interest (RoI) from the whole image of the polished rice, in order to reduce the image complexity and maximize the target feature difference. We refined the segmentation of the RoI image by establishing the instance segmentation network YOLACT, and we eventually procured the outcome by merging the RoI. Compared to other algorithms based on polished rice datasets, this constructed method was shown to present the image segmentation, enabling researchers to evaluate polished rice satisfactorily.

/pdf/a-method-of-polished-rice-image-segmentation-based-on-yo-ght5ji0w.pdf

A Method of Polished Rice Image Segmentation Based on YO-LACTS for Quality Detection

Soil salinization poses a significant global challenge, exerting adverse effects on both agriculture and ecosystems. Planting halophytes has the potential ability to improve saline-alkali land and enhance ecosystem multifunctionality (EMF). However, it remains unclear which halophytes are effective in improving saline-alkali land and what impact they have on the rhizosphere microbial communities and EMF. In this study, we evaluated the Na 

Planting halophytes increases the rhizosphere ecosystem multifunctionality via reducing soil salinity

Remote sensing (RS) instance segmentation is an important but challenging task due to multi-oriented, densely arranged objects and lack of mask annotation. Compared with redundant horizontal bounding-box (HBB) and expensive pixel-level annotation, oriented bounding box (OBB) annotations can provide compact object depicts with lower annotation costs. Therefore, we propose the first weakly supervised remote sensing instance segmentation method with OBB supervision (namely OBBInst) to reduce the annotation burden and make full use of existing abundant OBB annotations. Based on BoxInst (a high-performance instance segmentation method with box annotations), OBBInst has customized a framework for OBB annotation to unify the incompatibility between existing HBB-based and OBB-based methods. In addition, we propose an oriented projection method with a corresponding loss function to achieve more precise target depicts of OBB annotation. Moreover, we propose an edge similarity loss to incorporate Canny edge prior into deep learning framework for more precise edge identification of densely arranged objects. We have conducted extensive experiments on iSAID and HRSC datasets, and the experimental results demonstrate that OBBInst can achieve the state-of-the-art performance as compared to existing box-supervised methods. In addition, OBBInst dramatically narrows the performance gap between weakly and fully supervised instance segmentation (23.9% vs. 35.6% in iSAID dataset and 79.5% vs. 84.9% in HRSC dataset). 

OBBInst: Remote sensing instance segmentation with oriented bounding box supervision

Antibiotic resistance genes (ARGs) serving as a newly recognized pollutant that poses potential risks to global human health, which in the paddy soil can be potentially altered by different agricultural production patterns. To elucidate the impacts and mechanisms of the widely used and sustainable agricultural production pattern, namely integrated rice-fish farming, on the antibiotic resistomes, we applied metagenomic sequencing to assess ARGs, mobile genetic elements (MGEs), bacteria, archaea, and viruses in paddy soil. There were 20 types and 359 subtypes of ARGs identified in paddy soil. The integrated rice-fish farming reduced the ARG and MGE diversities and the abundances of dominant ARGs and MGEs. Significantly decreased ARGs were mainly antibiotic deactivation and regulator types and primarily ranked level IV based on their potential threat to human health. The integrated rice-fish farming decreased the alpha diversities and altered microbial community compositions. MGEs, bacteria, archaea, and virus exhibited significant correlations with ARGs, while integrated rice-fish farming effectively changed their interrelationships. Viruses, bacteria, and MGEs played crucial roles in affecting the ARGs by the integrated rice-fish farming. The most crucial pathway by which integrated rice-fish farming affected ARGs was through the modulation of viral communities, thereby directly or indirectly influencing ARG abundance. Our research contributed to the control and restoration of ARGs pollution from a new perspective and providing theoretical support for the development of clean and sustainable agricultural production. 

Variations in antibiotic resistomes associated with archaeal, bacterial, and viral communities affected by integrated rice-fish farming in the paddy field ecosystem.

Rice–fish coculture system enhances paddy soil fertility, bacterial network stability and keystone taxa diversity

Cereal grains are a vital part of the human diet. The appearance quality and size distribution of cereal grains play major roles as deciders or indicators of market acceptability, storage stability, and breeding. Computer vision is popular in completing quality assessment and size analysis tasks, in which an accurate instance segmentation is a key step to guaranteeing the smooth completion of tasks. This study proposes a fast deployable instance segmentation method based on a generative marker-based watershed segmentation algorithm, which combines two strategies (one strategy for optimizing kernel areas and another for comprehensive segmentation) to overcome the problems of over-segmentation and under-segmentation for images with dense and small targets. Results show that the average segmentation accuracy of our method reaches 98.73%, which is significantly higher than the marker-based watershed segmentation algorithm (82.98%). To further verify the engineering practicality of our method, we count the size distribution of segmented cereal grains. The results keep a high degree of consistency with the manually sketched ground truth. Moreover, our proposed algorithm framework can be used as a great reference in other segmentation tasks of dense targets.

/pdf/a-fast-deployable-instance-elimination-segmentation-319cznhw.pdf

A Fast Deployable Instance Elimination Segmentation Algorithm Based on Watershed Transform for Dense Cereal Grain Images

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Rice–fish coculture system enhances paddy soil fertility, bacterial network stability and keystone taxa diversity

A Fast Deployable Instance Elimination Segmentation Algorithm Based on Watershed Transform for Dense Cereal Grain Images