Distance-Entropy: An Effective Indicator for Selecting Informative Data

Yang, Li; Chao, Xuewei

doi:10.3389/fpls.2021.818895

Cited by 31 publications

(18 citation statements)

References 38 publications

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“…8 and Additional file 1 : Table S8, and the equations are shown as linear regression models. The calculated R 2 was greater than 0.83, indicating a good regression fit between the systematic and manual measurements of wheat plant height [ 29 , 30 ]. The RMSEs in the fit results were all less than 2.5 cm.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of an intelligent artificial climate chamber for high-throughput crop phenotyping in wheat

et al. 2022

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Background The superposition of COVID-19 and climate change has brought great challenges to global food security. As a major economic crop in the world, studying its phenotype to cultivate high-quality wheat varieties is an important way to increase grain yield. However, most of the existing phenotyping platforms have the disadvantages of high construction and maintenance costs, immobile and limited in use by climatic factors, while the traditional climate chambers lack phenotypic data acquisition, which makes crop phenotyping research and development difficult. Crop breeding progress is slow. At present, there is an urgent need to develop a low-cost, easy-to-promote, climate- and site-independent facility that combines the functions of crop cultivation and phenotype acquisition. We propose a movable cabin-type intelligent artificial climate chamber, and build an environmental control system, a crop phenotype monitoring system, and a crop phenotype acquisition system. Result We selected two wheat varieties with different early vigor to carry out the cultivation experiments and phenotype acquisition of wheat under different nitrogen fertilizer application rates in an intelligent artificial climate chamber. With the help of the crop phenotype acquisition system, images of wheat at the trefoil stage, pre-tillering stage, late tillering stage and jointing stage were collected, and then the phenotypic information including wheat leaf area, plant height, and canopy temperature were extracted by the crop type acquisition system. We compared systematic and manual measurements of crop phenotypes for wheat phenotypes. The results of the analysis showed that the systematic measurements of leaf area, plant height and canopy temperature of wheat in four growth periods were highly correlated with the artificial measurements. The correlation coefficient (r) is positive, and the determination coefficient (R2) is greater than 0.7156. The root mean square error (RSME) is less than 2.42. Among them, the crop phenotype-based collection system has the smallest measurement error for the phenotypic characteristics of wheat trefoil stage. The canopy temperature RSME is only 0.261. The systematic measurement values of wheat phenotypic characteristics were significantly positively correlated with the artificial measurement values, the fitting degree was good, and the errors were all within the acceptable range. The experiment showed that the phenotypic data obtained with the intelligent artificial climate chamber has high accuracy. We verified the feasibility of wheat cultivation and phenotype acquisition based on intelligent artificial climate chamber. Conclusion It is feasible to study wheat cultivation and canopy phenotype with the help of intelligent artificial climate chamber. Based on a variety of environmental monitoring sensors and environmental regulation equipment, the growth environment factors of crops can be adjusted. Based on high-precision mechanical transmission and multi-dimensional imaging sensors, crop images can be collected to extract crop phenotype information. Its use is not limited by environmental and climatic factors. Therefore, the intelligent artificial climate chamber is expected to be a powerful tool for breeders to develop excellent germplasm varieties.

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Section: Resultsmentioning

confidence: 99%

Evaluation of an intelligent artificial climate chamber for high-throughput crop phenotyping in wheat

et al. 2022

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“…In the image recognition competition ILSVRC held in 2012, AlexNet [1] won the championship by far surpassing the second place, the power of deep learning was finally shown in front of the world. With the continuous improvement of deep learning technology and hardware capabilities, artificial intelligence has developed more and more rapidly, and remarkable achievements have been made in many fields such as smart agriculture [2][3][4][5], medical treatment, finance, driverless, and so on [6][7][8][9][10][11][12]. Nowadays, more and more scholars begin to pay attention to how to apply in deep learning in the field of smart agriculture.…”

Section: Introductionmentioning

confidence: 99%

A survey of few-shot learning in smart agriculture: developments, applications, and challenges

Yang

Guo

et al. 2022

Plant Methods

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With the rise of artificial intelligence, deep learning is gradually applied to the field of agriculture and plant science. However, the excellent performance of deep learning needs to be established on massive numbers of samples. In the field of plant science and biology, it is not easy to obtain a large amount of labeled data. The emergence of few-shot learning solves this problem. It imitates the ability of humans’ rapid learning and can learn a new task with only a small number of labeled samples, which greatly reduces the time cost and financial resources. At present, the advanced few-shot learning methods are mainly divided into four categories based on: data augmentation, metric learning, external memory, and parameter optimization, solving the over-fitting problem from different viewpoints. This review comprehensively expounds on few-shot learning in smart agriculture, introduces the definition of few-shot learning, four kinds of learning methods, the publicly available datasets for few-shot learning, various applications in smart agriculture, and the challenges in smart agriculture in future development.

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“…When the target point or obstacles change slightly, these methods need to re-plan because of the lack of adaptability and flexibility. With the rapid development of deep learning in recent years, intelligent methods have played an increasingly important role in smart agriculture [15,16], image screening [17], environmental monitoring [18], edge computing [19], and path planning [20]. At the same time, it provides an effective technical means for agricultural production, automatic driving, and other fields.…”

Section: Introductionmentioning

confidence: 99%

The Intelligent Path Planning System of Agricultural Robot via Reinforcement Learning

Yang

Chen

2022

Sensors

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Agricultural robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology and the maturity of Internet of Things (IoT) technology, people put forward higher requirements for the intelligence of robots. Agricultural robots must have intelligent control functions in agricultural scenarios and be able to autonomously decide paths to complete agricultural tasks. In response to this requirement, this paper proposes a Residual-like Soft Actor Critic (R-SAC) algorithm for agricultural scenarios to realize safe obstacle avoidance and intelligent path planning of robots. In addition, in order to alleviate the time-consuming problem of exploration process of reinforcement learning, this paper proposes an offline expert experience pre-training method, which improves the training efficiency of reinforcement learning. Moreover, this paper optimizes the reward mechanism of the algorithm by using multi-step TD-error, which solves the probable dilemma during training. Experiments verify that our proposed method has stable performance in both static and dynamic obstacle environments, and is superior to other reinforcement learning algorithms. It is a stable and efficient path planning method and has visible application potential in agricultural robots.

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Distance-Entropy: An Effective Indicator for Selecting Informative Data

Cited by 31 publications

References 38 publications

Evaluation of an intelligent artificial climate chamber for high-throughput crop phenotyping in wheat

Evaluation of an intelligent artificial climate chamber for high-throughput crop phenotyping in wheat

A survey of few-shot learning in smart agriculture: developments, applications, and challenges

The Intelligent Path Planning System of Agricultural Robot via Reinforcement Learning

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