Gesunde Pflanzen
1439-0345
0367-4223
Cơ quản chủ quản: Springer Verlag , SPRINGER
Lĩnh vực:
Agricultural and Biological Sciences (miscellaneous)
Các bài báo tiêu biểu
Ameliorative Effects of Brassinosteroids on Growth and Productivity of Snap Beans Grown Under High Temperature
Tập 64 Số 4 - Trang 175-182 - 2012
Salicylic Acid as a Tolerance Inducer of Drought Stress on Sunflower Grown in Sandy Soil Abstract Agricultural water rationalization expressed in irrigating the plants below their requirements became a significant strategy in crop water management. However, reduction in crop productivity under low water supply is realized. Therefore, the current study aimed to diminish sunflower yield losses associated with deficit irrigation using salicylic acid (SA). During two seasons of 2019 and 2020 at El Nubaria region, El Behaira Governorate, Egypt, combinations of three irrigation regimes (100, 85 and 70% of crop evapotranspiration, denoted WR100% , WR85% , and WR70% , respectively), and three levels of SA (0.0, 0.5, and 1 mM. abbreviated as SA0.0 , SA0.5 , and SA1.0 , respectively) on sunflower plants performance were evaluated. Treatments were arranged in a strip–plot design with three replicates. Findings revealed that treated sunflower plants with WR100% × SA1.0 contained the highest amounts of total chlorophyll and carotenoids as well as the lowest proline content. Seed yield of WR100% × SA1.0 treatment was higher than that of WR70% × SA0.0 by 109.7% in the first season and 125.9% in the second one. As averages of the two seasons, SA0.5 and SA1.0 lowered the reductions in seed yield from 21.0% to 15.8 and 14.4% as well as 46.2% to 40.8 and 40.1% under WR85% and WR70% , respectively, compared to the farmer common practice (WR100% × SA0.0 ). WR100% × SA1.0 for iodine value as well as WR100% × SA1.0 and WR100% × SA0.5 for seed oil % were recorded the highest. Application of WR100% × SA1.0 and WR100% × SA0.5 were the effective combinations for ameliorating water use efficiency. In conclusion, involving salicylic acid in irrigation programs of sunflower became a decisive action to save water and alleviate the yield losses resulting from drought stress.
- 2022
Physiological and Morphological Responses of two Quinoa Cultivars (Chenopodium quinoa Willd.) to Drought Stress
Tập 71 Số 2 - Trang 123-133 - 2019
Camera-guided Weed Hoeing in Winter Cereals with Narrow Row Distance Abstract
Farmers are facing severe problems with weed competition in cereal crops. Grass-weeds and perennial weed species became more abundant in Europe mainly due to high percentages of cereal crops in cropping systems and reduced tillage practices combined with continuous applications of herbicides with the same mode of action. Several weed populations have evolved resistance to herbicides. Precision weed hoeing may help to overcome these problems. So far, weed hoeing in cereals was restricted to cropping practices with row distances of more than 200 mm. Hoeing in cereals with conventional row distances of 125–170 mm requires the development of automatic steering systems. The objective of this project was to develop a new automatic guidance system for inter-row hoeing using camera-based row detection and automatic side-shift control. Six field studies were conducted in winter wheat to investigate accuracy, weed control efficacy and crop yields of this new hoeing technology.
A three-meter prototype and a 6-meter segmented hoe were built and tested at three different speeds in 150 mm seeded winter wheat. The maximum lateral offset from the row center was 22.53 mm for the 3 m wide hoe and 18.42 mm for the 6 m wide hoe. Camera-guided hoeing resulted in 72–96% inter-row and 21–91% intra-row weed control efficacy (WCE). Weed control was 7–15% higher at 8 km h−1 compared to 4 km h−1 . WCE could be increased by 14–22% when hoeing was combined with weed harrowing. Grain yields after camera-guided hoeing at 8 km h−1 were 15–76% higher than the untreated control plots and amounted the same level as the weed-free herbicide plots. The study characterizes camera-guided hoeing in cereals as a robust and effective method of weed control.
- 2020
Stimulation Effects of Glutamic and 5-Aminolevulinic Acids On Photosynthetic Pigments, Physio-biochemical Constituents, Antioxidant Activity, and Yield of Peanut Abstract Soil not only represents the main supporter for root growth, but also is the supplier of water and nutrients. However, several soils, i.e. sandy soils, do not adequately fulfill the plant growth requirements of the environmental resources. Therefore, it is necessary to compensate, even partially, the lack of these required resources for better plant growth and development. Amino acids could introduce a substantial solution in this respect. Therefore, two field experiments under field conditions were carried out to investigate the effect of glutamic (GLA) and 5‑aminolevulinic (ALA) acids on photosynthesis pigments, oxidative defense indicators as well as yield and seed quality of peanut. Three concentrations of glutamic acid (10, 20 and 40 mg L−1 , denoted GLA10, GLA20, and GLA40, respectively) and three concentrations of 5‑aminolevulinic acid, (10, 20 and 40 mg L−1 , abbreviated to ALA10, ALA20, and ALA40, respectively), in addition to a check treatment (tap water) were applied. Treatments were arranged in a randomized complete block design with three replicates. Findings exhibited potentiality of GLA20 treatment for recording the highest values of chlorophyll a, chlorophyll b, chlorophyll a/b, carotenoids and total pigments compared to the other treatments. The increases in indole acetic acid, phenolics and free amino acids were 68.1, 58.9 and 19.6% as well as 64.6, 51.2 and 17.7%, due to application of GLA20 and ALA20, respectively. Substantial improvements in pod yield ha−1 , oil %, flavonoids and antioxidant activity were obtained with GLA20 or ALA20. In conclusion, since glutamic or 5‑aminolevulinic acids at concentration of 20 mg L−1 showed promotive effect on physiological and biochemical status of peanut, such amino acids should be adopted as a promising practice in peanut cultivations.
- 2022
Improving Deep Learning-based Plant Disease Classification with Attention Mechanism Abstract In recent years, deep learning-based plant disease classification has been widely developed. However, it is challenging to collect sufficient annotated image data to effectively train deep learning models for plant disease recognition. The attention mechanism in deep learning assists the model to focus on the informative data segments and extract the discriminative features of inputs to enhance training performance. This paper investigates the Convolutional Block Attention Module (CBAM) to improve classification with CNNs, which is a lightweight attention module that can be plugged into any CNN architecture with negligible overhead. Specifically, CBAM is applied to the output feature map of CNNs to highlight important local regions and extract more discriminative features. Well-known CNN models (i.e. EfficientNetB0, MobileNetV2, ResNet50, InceptionV3, and VGG19) were applied to do transfer learning for plant disease classification and then fine-tuned by a publicly available plant disease dataset of foliar diseases in pear trees called DiaMOS Plant. Amongst others, this dataset contains 3006 images of leaves affected by different stress symptoms. Among the tested CNNs, EfficientNetB0 has shown the best performance. EfficientNetB0+CBAM has outperformed EfficientNetB0 and obtained 86.89% classification accuracy. Experimental results show the effectiveness of the attention mechanism to improve the recognition accuracy of pre-trained CNNs when there are few training data.
- 2023