Nitrogen and Phosphorus Accumulation in Pasture Soil from Repeated Poultry Litter Application

Poultry litter (PL) is a traditionally inexpensive and effective fertilizer to improve soil quality and agricultural productivity. However, over application to soil has raised concern because excess nutrients in runoff could accelerate the eutrophication of fresh water. In this work, we determined the contents of total phosphorus (P), Mehlich 3 extracted P, total nitrogen (N), ammonium (NH4)-N, and nitrate (NO3)-N, in pasture soils receiving annual poultry litter applications of 0, 2.27, 2.27, 3.63, and 1.36 Mg/ha/ yr, respectively, for 0, 5, 10, 15, and 20 years. Samples were collected from three soil depths (0–20, 20–40, and 40–60 cm) of the Hartsells series (fine-loamy, siliceous, subactive, thermic, Typic Hapludults) on a 3–8% slope in the Sand Mountain region of north Alabama. PL application increased levels of total P, Mehlich-3 extractable P, and total N significantly. However, the change in NH4-N and NO3-N contents by the PL application was not statistically significant. Correlation analysis indicated that the contents of total P, Mehlich 3 extracted P, and total N were more related to cumulative amounts of poultry litter applied than the years of application or annual application rates alone. This observation suggested that N and P from poultry litter accumulated in soil. Predicting the build-up based on the cumulative amounts of PL application, rather than isolated factors (i.e., application year or rate), would improve the accuracy of evaluating long-term impacts of poultry litter application on soil nutrient levels

In Vitro Regeneration of \u3ci\u3eRudbeckia hirta\u3c/i\u3e ‘Plainview Farm’ from Leaf Tissue

Rudbeckia hirta ‘Plainview Farm’, a new multiple-layered ray flowered cultivar, shows potential for potted plant production. After years of seed germination, this specific flower morphological trait was still unstable from generation to generation. To maintain its unique features, leaf sections (0.25 cm2 ) were cultured on Murashige and Skoog (MS) medium supplemented with either BA (0.5, 1.0, or 2.0 mg·L1 ), KIN (2.5, 5, or 10 mg·L-1 ), or ZT (0.5, 1.0, or 2.0 mg·L-1 )toinduce callus and microshoots. After cultivation for 33 days, all cytokinin treatments significantly induced callus and the callus size were 1.5- to-2.4-fold bigger than those withoutcytokinin. KIN at 2.5 mg·L-1 was the best treatment for callus induction and microshoot formation. Four microshoots per explant wereproduced at KIN of 2.5 mg·L-1 . For rooting, all induced microshoots were cultured on MS medium at its one-quarter strength containing either IBA or NAA at 0.5, 1.5, or 3.0 mg·L-1 . All microshoots formed roots at 0.5 or 1.5 mg·L-1 IBA, or 0.5 mg·L-1 NAA. There were no significant differences in number of roots per shoot and length of roots among treatments. The plantlets were transplanted, acclimated in a mist system, and grown in a greenhouse. A total of 96.4% of the plants derived from tissue culture had multiple layers of ray flowers, while only 9.6% of the plants from seed propagation did. Therefore, in vitro regeneration of R. hirta ‘Plainview Farm’ was a feasible way to rapidly produce uniform plants with multiple layers of ray flowers

Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation

In this paper, we mainly focus on the problem of how to learn additional feature representations for few-shot image classification through pretext tasks (e.g., rotation or color permutation and so on). This additional knowledge generated by pretext tasks can further improve the performance of few-shot learning (FSL) as it differs from human-annotated supervision (i.e., class labels of FSL tasks). To solve this problem, we present a plug-in Hierarchical Tree Structure-aware (HTS) method, which not only learns the relationship of FSL and pretext tasks, but more importantly, can adaptively select and aggregate feature representations generated by pretext tasks to maximize the performance of FSL tasks. A hierarchical tree constructing component and a gated selection aggregating component is introduced to construct the tree structure and find richer transferable knowledge that can rapidly adapt to novel classes with a few labeled images. Extensive experiments show that our HTS can significantly enhance multiple few-shot methods to achieve new state-of-the-art performance on four benchmark datasets. The code is available at: https://github.com/remiMZ/HTS-ECCV22.Comment: 22 pages, 9 figures and 4 tables Accepted by ECCV 202

Attributes-Guided and Pure-Visual Attention Alignment for Few-Shot Recognition

The purpose of few-shot recognition is to recognize novel categories with a limited number of labeled examples in each class. To encourage learning from a supplementary view, recent approaches have introduced auxiliary semantic modalities into effective metric-learning frameworks that aim to learn a feature similarity between training samples (support set) and test samples (query set). However, these approaches only augment the representations of samples with available semantics while ignoring the query set, which loses the potential for the improvement and may lead to a shift between the modalities combination and the pure-visual representation. In this paper, we devise an attributes-guided attention module (AGAM) to utilize human-annotated attributes and learn more discriminative features. This plug-and-play module enables visual contents and corresponding attributes to collectively focus on important channels and regions for the support set. And the feature selection is also achieved for query set with only visual information while the attributes are not available. Therefore, representations from both sets are improved in a fine-grained manner. Moreover, an attention alignment mechanism is proposed to distill knowledge from the guidance of attributes to the pure-visual branch for samples without attributes. Extensive experiments and analysis show that our proposed module can significantly improve simple metric-based approaches to achieve state-of-the-art performance on different datasets and settings.Comment: An expanded version of the same-name paper accepted by AAAI-202

A Fine-Grained Image Description Generation Method Based on Joint Objectives

The goal of fine-grained image description generation techniques is to learn detailed information from images and simulate human-like descriptions that provide coherent and comprehensive textual details about the image content. Currently, most of these methods face two main challenges: description repetition and omission. Moreover, the existing evaluation metrics cannot clearly reflect the performance of models on these two issues. To address these challenges, we propose an innovative Fine-grained Image Description Generation model based on Joint Objectives. Furthermore, we introduce new object-based evaluation metrics to more intuitively assess the model's performance in handling description repetition and omission. This novel approach combines visual features at both the image level and object level to maximize their advantages and incorporates an object penalty mechanism to reduce description repetition. Experimental results demonstrate that our proposed method significantly improves the CIDEr evaluation metric, indicating its excellent performance in addressing description repetition and omission issues

Data Fusion of Electronic Nose and Electronic Tongue for Detection of Mixed Edible-Oil

For the problem of the waste of the edible-oil in the food processing, on the premise of food security, they often need to add new edible-oil to the old frying oil which had been used in food processing to control the cost of the production. Due to the fact that the different additive proportion of the oil has different material and different volatile gases, we use fusion technology based on the electronic nose and electronic tongue to detect the blending ratio of the old frying oil and the new edible-oil in this paper. Principal component analysis (PCA) is used to distinguish the different proportion of the old frying oil and new edible-oil; on the other hand we use partial least squares (PLS) to predict the blending ratio of the old frying oil and new edible-oil. Two conclusions were proposed: data fusion of electronic nose and electronic tongue can be used to detect the blending ratio of the old frying oil and new edible-oil; in contrast to single used electronic nose or single used electronic tongue, the detection effect has increased by using data fusion of electronic nose and electronic tongue