Recent advances in computer vision are primarily driven by the usage of deep learning, which is known to require large amounts of data, and creating datasets for this purpose is not a trivial task. Larger benchmark datasets often have detailed processes with multiple stages and users with different roles during annotation. However, this can be difficult to implement in smaller projects where resources can be limited. Therefore, in this work we present our processes for creating an image dataset for kernel fragmentation and stover overlengths in Whole Plant Corn Silage. This includes the guidelines for annotating object instances in respective classes and statistics of gathered annotations. Given the challenging image conditions, where objects are present in large amounts of occlusion and clutter, the datasets appear appropriate for training models. However, we experience annotator inconsistency, which can hamper evaluation. Based on this we argue the importance of having an evaluation form independent of the manual annotation where we evaluate our models with physically based sieving metrics. Additionally, instead of the traditional time-consuming manual annotation approach, we evaluate Semi-Supervised Learning as an alternative, showing competitive results while requiring fewer annotations. Specifically, given a relatively large supervised set of around 1400 images we can improve the Average Precision by a number of percentage points. Additionally, we show a significantly large improvement when using an extremely small set of just over 100 images, with over 3× in Average Precision and up to 20 percentage points when estimating the quality.
Maize ( L.), a key staple crop in Sub-Saharan Africa, is particularly vulnerable to concurrent drought and heat stress, which threatens crop yield and food security. Plant growth-promoting rhizobacteria (PGPR) have shown potential as biofertilizers to enhance plant resilience under such abiotic stresses.
Root-lesion nematodes (Pratylenchus spp.) are significant plant parasites, causing substantial crop damage worldwide. This study aimed to characterize Pratylenchus spp.
Maize ( L.) is a major food and feed crop and an important raw material for energy, chemicals, and livestock. The NF-Y family of transcription factors in maize plays a crucial role in the regulation of plant development and response to environmental stress.
Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA; Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA, USA. Electronic address:
Health and population status of bees is negatively affected by anthropogenic stressors, many of which co-occur in agricultural settings. While pollinator habitat (often involving plantings of native forbs) holds promise to benefit both managed and wild bees, important issues remain unresolved. These include whether conventional, broad-spectrum insecticide use negates these benefits and how non-native, managed honey bees affect wild bees in these areas.
In recent years, the fall armyworm, has rapidly emerged as a global invasive pest, challenging the maize production and leading to considerable economic losses. Developing resistant hybrids is essential for sustainable maize cultivation, which requires a comprehensive understanding of resistance traits and the underlying mechanisms in parental lines. To address this need, the present study aimed to identify the sources of resistance, age and stage-specific effects and role of phytochemicals in plant defense against in thirty diverse maize parental lines [17 female (A) and 13 male (R) lines].
