3. Results and Discussions 1. Irrigation requirement
3.3. Irrigation Water Use Efficiency
The impacts of three climate change scenarios compared with the baseline scenario on IWUE on three soil texture classes and two crops in Phu Thien district are shown in Figure 4. Whereby, IWUE of rice increased gradually with the wetness of climate. The wet climate scenario recorded the strongest increase in IWUE with 26.20% compared to the baseline scenario, followed by normal (18.34%), dry (13.66%) scenarios. Under climate change impact, rice farming on sandy loam soils was most effective when it harvested 0.56 - 0.64 kg of rice per m³ of irrigation water. This value is below the average of 0.7 kg of rice per m³ of irrigation water (IRRI, 2022). In contrast, rice cultivation on clay soils was least effective when it consumes one m³ of irrigation water for 0.20 - 0.25 kg of harvested rice.
For rice cultivation season, it was recommended to cultivate in spring crop to optimize irrigation water use with 0.50 - 0.59 kg of rice per m³ of irrigation water. Meanwhile, it was necessary to consume 1.15 - 1.17 m³ of irrigation water in summer-autumn crop to harvest the same amount of rice in spring crop.
Figure 4. Climate change impact on IWUE
a) clay, sandy clay loam, and sandy loams; b) Spring and summer-autumn.
4. Conclusions
The increase in temperature and rainfall variation in three high greenhouse gas emission scenarios of normal, wet, and dry year according to RCP 8.5 (2046 - 2065) was projected to significantly reduce the use efficiency irrigation water for rice crop in Phu Thien district compared to the baseline (1986 - 2005). It is recommended to plant rice in the spring crop in order to maximize irrigation water productivity. In the next 20-40 years, the irrigation water demand for rice in Phu Thien district may increase up to 9.88%. Therefore, managers and
authorities need to plan the implementation of engineering measures to meet with the above water demand. Besides, proper rice farming planning with a late growing period coinciding with the months of heavy rainfall is crucial to improve water resource efficiency. Various methods of soil and crop water management need to be adopted for economically sustainable production.
The limitation of this study is that specific climate variables such as wind speed, relative humidity, and sunshine duration were assumed to be constant in future analytical scenarios. In addition, the rice area and soil characteristics were assumed to be the same as the present one.
The impact of salt and fertilizer concentrations on rice yield has not considered in the AquaCrop model. The validation of AquaCrop model was performed based on limited observed rice yield data.
Author contributions: methodology, Liem D. Nguyen; formal analysis, Liem D. Nguyen;
writing - original draft preparation, Khuong L. N. Tran, & Liem D. Nguyen. All authors have read and agreed to the published version of the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
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APPLYING CONVOLUTION NEURAL NETWORKS FOR LEAF IMAGE RECOGNITION WITH THE VIETNAMESE LEAF IMAGE DATABASE
Phan D. Long1*, Tran T. Son2
1Faculty of Information Technology, Nong Lam University, Ho Chi Minh City, Vietnam
2Faculty of Information Technology, University of Natural Science, Ho Chi Minh City, Vietnam
*Email: [email protected] Abstract
In the current literature, there appears to be ample evidence that convolutional neural networks (CNNs) are effective and efficient for solving computer vision tasks such as image recognition, image classification and object recognition. To date, many CNN-based models have been proposed such as AlexNet - 2012, ZFNet - 2013, Google LeNet -2014, ResNet – 2015, etc., and widely applied to the classification of problems with high performance. In this paper, we propose a model by combining AlexNet and ZFNet models for image classification tasks with our collected dataset including 6900 leaf images of 12 common plants in Vietnam.
Experimental results showed that the proposed method outperforms other competitive models (AlexNet and ZFNet) in terms of accuracy, precision, recall and F1 measures. Additionally, the proposed method significantly reduced the execution time in comparison to AlexNet and ZFNet.
Keywords: Vietnamese leaf Database, CNN, computer vision, convolutional neural networks, leaf image recognition.
1. Introduction
Vietnam has a lengthy agricultural legacy that includes the cultivation of corn, rice, and other crops. To guarantee food security, research on plant types to increase productivity has been performed. The study has aided in the discovery of novel hybrid plants that are capable of meeting production targets as well as coping with harsh climates and pests. Recently, the advancement in information technology also helps to resolve the difficulties of the agricultural sector.
The research on using information technology for the classification of plants based on the morphology of their components is still limited. Biological traits like leaves, stems, blooms, and roots are frequently used to identify and distinguish tree species. Leaves are a good choice for distinguishing plants since they grow all year and are easy to collect without interfering with the plant's growth (Wu et al., 2015). Leaves provide a data set with a broad range of distinguishing traits to identify the plants
With the development of artificial intelligence (AI), particularly convolutional neural networks (CNN), the applications of AI for recognizing and categorizing crops based on leaf features have emerged. There are two well-known convolutional neural network models including AlexNet (Krizhevsky, & Sutskever, 2012) and ZFNet (Zeiler & Fergus Zeiler, 2014). AlexNet is the object recognition model suggested in the ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC). AlexNet consists of 11 layers, including 5 convolution layers, 3 Max Pooling layers, and 3 fully connected layers. ZFNet is the proposed ILSVRC 2013 model.
ZFNet has 11 layers, including 5 convolution layers, 3 Max Pooling layers, and 3 fully connected layers. Both models have good accuracy in passing the ILSVRC competition test results
We chose and gathered leaf samples to analyze to apply two network models, AlexNet and ZFNet. We chose 12 popular and easy-to-collect leaf crops, including corn, sweet potato,
tapioca, potato, rice, cocoa, rubber, coffee, cashew, pepper, mangosteen, and durian. The photographs were taken in the provinces of Lam Dong, Dong Nai, Long An, Binh Duong, and Ho Chi Minh City.
In this paper, we present a database of 12 distinct types of collected trees and compare our training results to those of AlexNet and ZFNet. We also introduce Android software that can recognize tree leaves based on training outcomes.