The effectiveness of methods and algorithms for detecting and isolating factors that negatively affect the growth of crops

Yessenova, Moldir; Abdikerimova, Gulzira; Ayazbaev, Talgatbek; Murzabekova, Gulden; Ismailova, Aisulu; Beldeubayeva, Zhanar; Ainagulova, Aliya; Mukhanova, Ayagoz

doi:10.11591/ijece.v13i2.pp1669-1679

Cited by 12 publications

(14 citation statements)

References 20 publications

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“…𝑦 𝑡 = 𝑊 ℎ𝑣 ℎ 𝑡 (16) This RNN takes the sequence of inputs from 𝑥 0 and it outputs the sequence as ℎ 0 which combines with the 𝑥 1 which is the input for the next process. Then, the 𝑥 1 and ℎ 0 are inputs for next process and similarly, ℎ 1 and 𝑥 2 are inputs for the next step.…”

Section: Classificationmentioning

confidence: 99%

“…The mathematical studies and historical data from the previous years are helpful in certain regions, but cannot be employed frequently [15]. Due to the limited resources, crop yield is utilized in remote areas where the meteorological data is unavailable, but it struggles to handle the temporal dependencies and patterns in time-series data [16]. Paudel et al [17] implemented a machine learning technique for crop yield production using the data from European commission's MARS crop yield forecasting system (MCYFS).…”

Section: Introductionmentioning

confidence: 99%

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Agriculture crop yield prediction using inertia based cat swarm optimization

Reddy,

Madapuri

2024

IJECE

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Crop yield prediction is among the most important and main sources of income in the Indian economy. In this paper, the improved cat swarm optimization (ICSO) based recurrent neural network (RNN) model is proposed for crop yield prediction using time series data. The inertia weight parameter is added to position equation that is selected randomly, and a new velocity equation is produced which enhances the searching ability in the best cat area. By using inertia weight, the ICSO enhances performance of feature selection and obtains better convergence in minimum iteration. The RNN is applied to produce direct graph using sequence of data and decides current layer output by involving all other existing calculations. The performance of the model is estimated using coefficient of determination (R2), root mean square error (RMSE), mean squared error (MSE), and mean absolute error (MAE) on the yield from the years 2011 to 2021 with an annual prediction for 120 records of approximately 8 million nuts. The evaluated result shows that the proposed ICSO-RNN model delivers metrics such as R2, MAE, MSE, and RMSE values of 0.99, 0.77, 0.68, and 0.82 correspondingly, which ensures accurate yield prediction when compared with the existing methods which are hybrid reinforcement learning-random forest (RL-RF) and machine learning (ML) methods.

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Section: Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Agriculture crop yield prediction using inertia based cat swarm optimization

Reddy,

Madapuri

2024

IJECE

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“…The steppe regions of Kazakhstan are crucial for the country's agriculture, mainly grain and livestock farming [3]. These areas are vast and often subject to a variety of biotic stresses, such as pests and diseases [4], which can significantly impact productivity and sustainability. The study is in line with Kazakhstan's strategic goals set in the state program for 2021 -2025, which include improving public services and introducing digital technologies into agriculture.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Disease and Pest Dynamics in Steppe Crop Using Structured Data

Tussupov,

Abdikerimova,

Ismailova

et al. 2024

IEEE Access

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In this study, an analysis of the spectral brightness coefficients (SBC) of agricultural crops in the steppe regions of Kazakhstan was carried out using remote field and space measurements during the growing season. The possibility of assessing structural ch anges in plants using near-infrared data was explored. An electronic database of corrected spectral data of SBC for the studied crops has been created. The results obtained confirm that the dynamics of SBC values reflect morphophysiological changes in crops during their growing season. Analysis of the seasonal dynamics of the spectral brightness of pests and diseases makes it possible to diagnose their species composition and the physiological state of plants. Spectrophotometric information obtained from sa tellite data of various crops can be used to build simulation models that provide scientifically based forecasts of the dynamics of the spatial distribution of the species composition of vegetation cover. The study highlights the potential of using subtle spectral differences to study the spatial distribution of different vegetation types and their ecological status. The results of the analysis of satellite information from Sentinel confirm the prospects of its use for assessing the morphophysiological state of agricultural crops using ground-based spectrometric calibration, as well as for predicting yields.INDEX TERMS Structured data, crops, spectral brightness coefficients, diseases and pests, data verification

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“…Also, in the future, they can be used in the monitoring system of forestry and agriculture using unmanned aerial vehicles (UAVs). To solve the problems posed in the work, methods of remote sensing data preprocessing, the Laws texture mask method [11] as weights in machine learning, and the clustering method (k-means) were used.…”

Section: Introductionmentioning

confidence: 99%

Applying textural Law’s masks to images using machine learning

Abdikerimova,

Yessenova,

Yerzhanova

et al. 2023

IJECE

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Currently, artificial neural networks are experiencing a rebirth, which is primarily due to the increase in the computing power of modern computers and the emergence of very large training data sets available in global networks. The article considers Laws texture masks as weights for a machine-learning algorithm for clustering aerospace images. The use of Laws texture masks in machine learning can help in the analysis of the textural characteristics of objects in the image, which are further identified as pockets of weeds. When solving problems in applied areas, in particular in the field of agriculture, there are often problems associated with small sample sizes of images obtained from aerospace and unmanned aerial vehicles and insufficient quality of the source material for training. This determines the relevance of research and development of new methods and algorithms for classifying crop damage. The purpose of the work is to use the method of texture masks of Laws in machine learning for automated processing of high-resolution images in the case of small samples using the example of problems of segmentation and classification of the nature of damage to crops.

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The effectiveness of methods and algorithms for detecting and isolating factors that negatively affect the growth of crops

Cited by 12 publications

References 20 publications

Agriculture crop yield prediction using inertia based cat swarm optimization

Agriculture crop yield prediction using inertia based cat swarm optimization

Analyzing Disease and Pest Dynamics in Steppe Crop Using Structured Data

Applying textural Law’s masks to images using machine learning

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