Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture

Khan, Tamoor; Sherazi, Hafiz Husnain Raza; Ali, Mubashir; Letchmunan, Sukumar; Butt, Umair Muneer

doi:10.3390/agronomy11081551

Cited by 20 publications

(10 citation statements)

References 57 publications

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“…The CV values could be used to assign the parameters to one of three levels of variability (B. Liu et al., 2021). Namely low (CV < 10%), moderate (10% ≤ CV < 100%), or high (100% ≤ CV) (Khan et al., 2021; J. Zhang et al., 2018). The CV of nutrients in study ranged from 5 to 80% (Table 1).…”

Section: Resultsmentioning

confidence: 99%

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Spatial heterogeneity of soil nutrients in black soil areas of Northeast China

Wang

Song

et al. 2022

Agronomy Journal

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Exploring the distribution and evolution of soil nutrients, it is needed to maintain quality of land and ensure food production. Based on geostatistical theory, 1,054 soil sites were selected from the surface (0-20 cm) of the typical black soil area in Northeast China. The spatial structure of soil pH, alkali-hydrolyzed N, effective P, and available K was investigated using Kriging and spatial statistics. The soils in the study area are acidic, alkali-hydrolyzed N, effective P, and available K content at moderate to high levels. Coefficients of variation for various nutrients ranged from 5 to 80%, all showing moderate variation. The pH, alkali-hydrolyzed N, effective P, and available K increased from South to North, the value of soil nutrients near the city were high. The results provide reference values for future work targeted at improving food security and reducing the effect on the environment.

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Section: Resultsmentioning

confidence: 99%

“…Liu et al, 2021). Namely low (CV < 10%), moderate (10% ≤ CV < 100%), or high (100% ≤ CV) (Khan et al, 2021;J. Zhang et al, 2018).…”

Section: Descriptive Statistics Of Soil Nutrient Content In Typical B...mentioning

confidence: 99%

Spatial heterogeneity of soil nutrients in black soil areas of Northeast China

Wang

Song

et al. 2022

Agronomy Journal

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“…They propose AgroAVNET, a hybrid model based on AlexNet and VGGNET, with a extensive performance improvement compared to existing methods. Literature [ 14 ] is dedicated to using past agricultural production data to predict future agricultural production. The authors propose a deep learning model AGR-DL based on CNN and RNN.…”

Section: Related Workmentioning

confidence: 99%

Suitability Evaluation of Crop Variety via Graph Neural Network

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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With the continuous growth of the global population, insufficient food production has become an urgent problem to be solved in most countries. At present, using artificial intelligence technology to improve suitability between land and crop varieties to increase crop yields has become a consensus among agricultural researchers. However, there are still many problems in existing works, such as limited crop phenotypic data and the poor performance of artificial intelligence models. In this regard, we take maize as an example to collect a large amount of environmental climate and crop phenotypic traits data at multiple experimental sites and construct an extensive dataset. Then, we introduce a graph neural network model to learn crop suitability evaluation and finally achieve a good evaluation effect. The evaluation results of the model can not only provide a reference for expert evaluation but also judge the suitability of the variety to other test trial sites according to the data of the current one, so as to guide future breeding experiments.

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“…Smart agriculture has been emphasized to resolve these difficulties [2]. Smart agriculture includes the use of drones to monitor field growth [3,4], autonomous tractors to save labor [5], and machine learning to predict crop harvesting times [6]. This study specifically examines the prediction of crop harvesting times.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Harvest Time of Tomato Using Mask R-CNN

Minagawa

Kim

2022

AgriEngineering

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In recent years, the agricultural field has been confronting difficulties such as the aging of farmers, a shortage of workers, and difficulties for new farmers. Harvesting time prediction has the potential to solve these problems, and is expected to effectively utilize human resources, save labor, and reduce labor costs. To achieve harvesting time prediction, various works are being actively conducted. Methods for harvesting time prediction using meteorological information such as temperature and solar radiation, etc., and methods for harvesting time prediction using neural networks based on color information from fruit bunch images are being investigated. However, the prediction accuracy is still insufficient, and the harvesting time prediction for individual tomato fruits has not been studied. In this study, we propose a novel method to predict the harvesting time for individual tomato fruits. The method uses Mask R-CNN to detect tomato bunches and uses two types of ripeness determination to predict the harvesting time of individual tomato fruits. The experimental results showed that the accuracy of the prediction using the ratio of R values was better for the harvesting time prediction of tomatoes that are close to the harvesting time, and the accuracy of the prediction using the average of the differences between R and G in RGB values was better for the harvesting time prediction of tomatoes that are far from the harvesting time. These results show the effectiveness of the proposed method.

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Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture

Cited by 20 publications

References 57 publications

Spatial heterogeneity of soil nutrients in black soil areas of Northeast China

Spatial heterogeneity of soil nutrients in black soil areas of Northeast China

Suitability Evaluation of Crop Variety via Graph Neural Network

Prediction of Harvest Time of Tomato Using Mask R-CNN

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