Current Issue : January-March Volume : 2022 Issue Number : 1 Articles : 5 Articles
Salinity is an abiotic stress that reduces the seed germination and productivity of tomatoes. Magnetic treatment has been shown to have a positive effect on the seed germination, seedling growth, and productivity of various crop species. Therefore, three experiments were conducted to evaluate whether treating saline water or seeds with a magnetic field can improve the seed germination and productivity of tomatoes (Solanum lycopersicum) under salinity stress. To evaluate seed germination and seedling growth in response to a magnetic field, two laboratory experiments were carried out by passing four saline water solutions of NaCl (0, 5, 10, and 15 dS/m) through a magnetic field (3.5–136 mT) or exposing tomato seeds to the same magnetic field for 20 min before sowing. In a greenhouse experiment, plants were irrigated with different magnetically-treated and untreated saline water solutions to evaluate plant growth. Magnetic treatment of water or seeds improved seed germination percentage, speed of germination (lower mean time to germination), and seedling length and dry weight in the two laboratory experiments, especially under salinity stress of 5 and 10 dS/m. As the salinity level increased, germination performance and plant growth were significantly decreased. Irrigating tomato plants with magnetically-treated water improved plant height, stem diameter, and fruit yield per plant compared to untreated water, especially under salinity of 0 and 5 dS/m. In conclusion, magnetic treatment of saline water or seeds improved germination performance, plant growth, and fruit yield of tomatoes under saline conditions....
Rice has long served as the staple food in Asia, and the cultivation of high-yield rice crops draws increasing attention from academic researchers. The prediction of rice growth condition by image features realizes nondestructive prediction and it has great implications for smart agriculture. We found a special image parameter called the fractal dimension that can improve the effect of the prediction model. As an important geometric feature, the fractal dimension could be calculated from the image, but it is rarely used in the field of rice growth prediction. In this paper, we attempt to combine the fractal dimension with traditional rice image features to improve the effect of the model. The thresholding method is used to transform the cropped rice image into binary image, and the box-counting method is used to calculate the fractal dimension of the image. The correlation coefficients are calculated to select the characteristics with a strong correlation with biomass. The prediction models of dry weight, fresh weight and plant height of rice are established by using random forest, support vector regression and linear regression. By evaluating the prediction effect of the model, it can be concluded that the fractal dimension can improve the prediction effect of the model. Among the models obtained by the three methods, the multiple linear regression model has the best comprehensive effect, with the dry weight prediction model R2 reaching 0.8697, the fresh weight prediction model R2 reaching 0.8631 and the plant height prediction model R2 reaching 0.9196. The model established in this paper has a fine effect and has a certain guiding significance in rice research....
Actual crop evapotranspiration (ET) and crop coefficient (Kc) of ratoon rice crop, which are necessary for irrigation planning, have been hardly reported. ET can be directly measured by lysimeter and eddy covariance but it is expensive, so it remains difficult to determine ET, especially in developing countries. The focus of this study was to evaluate the ET and Kc of ratoon cropping in a tropical region of Myanmar using a simplified method. Our method combined the manual observation of water depth in concrete paddy tanks and the ET model estimation using Bayesian parameter inference. The ET and Kc could be determined using this method with an incomplete observation dataset. The total ET of ratoon was 60–70% less than that of the main crop, but this difference was mainly attributed to climate conditions in each cultivation. The Kc regression curve between transplanted rice and ratoon crops was different because of the tillering traits. The results suggest that irrigation scheduling of ratoon cropping in the initial growth stage should take high crop water requirements into account. In addition, the productivity of ratoon crop is equivalent to transplanted rice, which was determined for cultivation in experiment conditions of small concrete tanks. Therefore, further study on ratoon in Myanmar is necessary for clarifying the viability of ratoon cropping....
Nitrogen (N) plays a vital role in the productivity of maize (Zea mays L). To investigate the fertilizer effects of N on the yield and growth of maize hybrid variety (Gorilla), the experiment was carried out at the research farm of University of Swabi, Pakistan, during summer 2017-18. Four levels of N (Urea, Urea + Farm Yard Manure (FYM), Urea + Compost, Urea + Poultry Manure (PM)) were set in the present study. Randomized complete block design (RCBD) was used with split-plot arrangement with N administering to main plot. Results showed that yield and other traits, i.e. plant height, ear length, ear weight, kernel yield, kernels ear−1 and harvest index (HI) were significantly affected by Nitrogen. In current study, the maximum performances of plant height (231.46 cm), ear length (12.17 cm), kernels ear−1 (434.83), kernel yield (2095.7 kg∙ha−1), total kernel weight (350.75 kg·ha−1), biomass yield (4015.3 kg·ha−1) and HI (37.31) were recorded under the treatment of UREA + PM, and followed by UREA + FYM. Besides, the applications of organic manure in combination with nitrogen significantly increased yield and its components. Application of 50% of N and 50% of poultry manure produced higher performance for the traits of plant height, ear length, kernels ear−1, total kernels weight ear−1, kernel yield, and biomass yield....
The real‐time detection and counting of rice ears in fields is one of the most important methods for estimating rice yield. The traditional manual counting method has many disadvantages: it is time‐consuming, inefficient and subjective. Therefore, the use of computer vision technology can improve the accuracy and efficiency of rice ear counting in the field. The contributions of this article are as follows. (1) This paper establishes a dataset containing 3300 rice ear samples, which represent various complex situations, including variable light and complex backgrounds, overlapping rice and overlapping leaves. The collected images were manually labeled, and a data enhancement method was used to increase the sample size. (2) This paper proposes a method that combines the LC‐FCN (localization‐based counting fully convolutional neural network) model based on transfer learning with the watershed algorithm for the recognition of dense rice images. The results show that the model is superior to traditional machine learning methods and the singleshot multibox detector (SSD) algorithm for target detection. Moreover, it is currently considered an advanced and innovative rice ear counting model. The mean absolute error (MAE) of the model on the 300‐size test set is 2.99. The model can be used to calculate the number of rice ears in the field. In addition, it can provide reliable basic data for rice yield estimation and a rice dataset for research....
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