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Winter wheat is a significant global food crop, and it is crucial to monitor its distribution for better agricultural management, land planning, and environmental sustainability. However, the distribution style of winter wheat planting fields is not consistent due to different terrain conditions. In mountainous areas, winter wheat planting units are smaller in size and fragmented in distribution compared to plain areas. Unfortunately, most crop-mapping research based on deep learning ignores the impact of topographic relief on crop distribution and struggles to handle hilly areas effectively. In this paper, we propose a cross-modal segmentation network for winter wheat mapping in complex terrain using remote-sensing multi-temporal images and DEM data. First, we propose a diverse receptive fusion (DRF) module, which applies a deformable receptive field to optical images during the feature fusion process, allowing it to match winter wheat plots of varying scales and a fixed receptive field to the DEM to extract evaluation features at a consistent scale. Second, we developed a distributed weight attention (DWA) module, which can enhance the feature intensity of winter wheat, thereby reducing the omission rate of planting areas, especially for the small-sized regions in hilly terrain. Furthermore, to demonstrate the performance of our model, we conducted extensive experiments and ablation studies on a large-scale dataset in Lanling county, Shandong province, China. Our results show that our proposed CM-Net is effective in mapping winter wheat in complex terrain. [ABSTRACT FROM AUTHOR]

Rice is an important food crop in China, and diseases and pests are the main factors threatening its safety, ecology, and efficient production. The development of remote sensing technology provides an important means for non-destructive and rapid monitoring of diseases and pests that threaten rice crops. This paper aims to provide insights into current and future trends in remote sensing for rice crop monitoring. First, we expound the mechanism of remote sensing monitoring of rice diseases and pests and introduce the applications of different commonly data sources (hyperspectral data, multispectral data, thermal infrared data, fluorescence, and multi-source data fusion) in remote sensing monitoring of rice diseases and pests. Secondly, we summarize current methods for monitoring rice diseases and pests, including statistical discriminant type, machine learning, and deep learning algorithm. Finally, we provide a general framework to facilitate the monitoring of rice diseases or pests, which provides ideas and technical guidance for remote sensing monitoring of unknown diseases and pests, and we point out the challenges and future development directions of rice disease and pest remote sensing monitoring. This work provides new ideas and references for the subsequent monitoring of rice diseases and pests using remote sensing. [ABSTRACT FROM AUTHOR]

Agricultural direct energy use is responsible for about 1–2% of global emissions and is the major emitting sector for methane (2.9 GtCO2eq y−1) and nitrous oxide (2.3 GtCO2eq y−1). In the last century, farm mechanisation has brought higher productivity levels and lower land demands at the expense of an increase in fossil energy and agrochemicals use. The expected increase in certain food and bioenergy crops and the uncertain mitigation options available for non-CO2 emissions make of vital importance the assessment of the use of energy and the related emissions attributable to this sector. The aim of this paper is to present a simulation framework able to forecast energy demand, technological diffusion, required investment and land use change of specific agricultural crops. MUSE-Ag & LU, a novel energy systems-oriented agricultural and land use model, has been used for this purpose. As case study, four main crops (maize, soybean, wheat and rice) have been modelled in mainland China. Besides conventional direct energy use, the model considers inputs such as fertiliser and labour demand. Outputs suggest that the modernisation of agricultural processes in China could have the capacity to reduce by 2050 on-farm emissions intensity from 0.024 to 0.016 GtCO2eq PJcrop−1 (−35.6%), requiring a necessary total investment of approximately 319.4 billion 2017$US. [ABSTRACT FROM AUTHOR]