In a natural environment, due to the small size of caterpillar fungus, its indistinct features, similar color to surrounding weeds and background, and overlapping instances of caterpillar fungus, identifying caterpillar fungus poses significant challenges. To address these issues, this paper proposes a new MRAA network, which consists of a feature fusion pyramid network (MRFPN) and the backbone network N-CSPDarknet53. MRFPN is used to solve the problem of weak features. In N-CSPDarknet53, the Da-Conv module is proposed to address the background and color interference problems in shallow feature maps. The MRAA network significantly improves accuracy, achieving an accuracy rate of 0.202 APS for small-target recognition, which represents a 12% increase compared to the baseline of 0.180 APS. Additionally, the model size is small (9.88 M), making it lightweight. It is easy to deploy in embedded devices, which greatly promotes the development and application of caterpillar fungus identification.
Posture transition behavior in sows approaching parturition provides an important physiological cue for farrowing prediction. However, manual monitoring is time-consuming, labor-intensive and difficult to sustain under nighttime production conditions, while existing machine visio…
This study developed an integrated Geographical Information System (GIS) based Multi-Criteria Decision Analysis (GIS-MCDA) framework based on hydrological marginality, soil suitability, land cover marginality, slope, social marginality, and contaminated land indicators to identif…
Leaf nitrogen content (LNC) is an important physiological indicator for evaluating citrus nutritional status, photosynthetic capacity, and fertilization demand. However, conventional LNC determination mainly relies on field sampling and laboratory chemical analysis, which are des…
Improving the quality of agriculture-related market entry is central to rural industrial upgrading, yet existing studies mainly measure entry quantity. The primary objective of this study is to assess whether the county-level business environment is associated with the structural…
Dairy cow management depends on repeated observations of behavior and physical condition to support health, welfare, and operational decisions, but these observations remain labor-intensive. Deep learning (DL)-based computer vision can automate parts of this work, although deploy…
Digital transformation is a key driver of green innovation in agribusiness. While the positive impact of digital transformation on firm innovation has been well documented, its multidimensional nature and heterogeneous associations on agribusiness green innovation remain underexp…