Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS

Nosratabadi, Saeed; Ardabili, Sina Faizollahzadeh; Lakner, Zoltán; Makó, Csaba; Mosavi, Amir

doi:10.3390/agriculture11050408

Cited by 67 publications

(19 citation statements)

References 44 publications

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“…The growth of the world’s population more than twofold, from 3 billion people in 1960 to 7.7 billion people at present, has entailed a forced increase in agricultural production [ 1 , 2 ]. This has led to increased consumption of mineral fertilizers [ 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Creation of a Nanomodified Backfill Based on the Waste from Enrichment of Water-Soluble Ores

et al. 2022

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The paper analyzes losses during the development of low-value water-soluble ore deposits. The importance of development systems with backfill is shown. The use of industrial wastes of water-soluble ores to replace specially produced inert components in the preparation of backfill seems to be a good way to utilize them. The aim of the work was to create a fill mass with improved strength properties based on industrial wastes of water-soluble ores activated with a nanomodifying material. The characteristics (chemical and granulometric compositions) of an aggregate of the backfill based on the waste from enrichment of water-soluble ores are given. The validation of the hardening mixture compositions for various mining systems was carried out considering: the specified strength, the time of the artificial mass erection, the time to achieve the required strength properties of the material, which determine the possible intensity of the mining operations; method of transporting the backfill. The expediency of using a multilayer fulleroid nanomodifier astralene as a nanomodifying additive has been proved. The effect of the backfill activation with a nanomodifying additive, astralene, on the structural changes of halite wastes from the halurgic enrichment of water-soluble ores (potash) was investigated. To study the strength properties of the fill mass, the comparative analytical method was used. The strength properties of the backfill were measured in standard test periods, taking into account the intensity of hardening of the backfill material (after 7, 14, 28, 60, 90 days). To obtain reliable results, 10 backfill samples were tested at each of the scheduled dates. The shape and chemical composition of crystalline new forms were studied. Studies were performed using X-ray phase analysis and scanning electron microscopy. As a result of uniaxial compression of hardening backfill samples, the dependence of the ultimate strength on the astralene content and the hardening time were established. It has been experimentally proved that the use of a nanomodifying additive, astralene, in the backfill allows an increase in the strength properties of the created artificial mass by 1.76–2.36 times while reducing binder consumption.

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Section: Introductionmentioning

confidence: 99%

Creation of a Nanomodified Backfill Based on the Waste from Enrichment of Water-Soluble Ores

et al. 2022

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“…To verify the effectiveness of GCLPSO on multi-threshold image segmentation, GCLPSO will be compared with CLPSO, two improved algorithms SCADE and m_SCA, and three original algorithms SSA, SCA, and SMA, respectively. To ensure the validity and fairness of the experiments (Chen et al, 2021 ; Moayedi and Mosavi, 2021d ; Nosratabadi et al, 2021 ; Yang et al, 2021 ), all the algorithms involved in the comparisons were conducted under the same experimental conditions. Such a setting is one of the most crucial rules in the artificial intelligence community (Song et al, 2020 ; Thaher et al, 2020 ; Mousavi et al, 2021 ; Tavoosi et al, 2021 ).…”

Section: Experiments and Resultsmentioning

confidence: 99%

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

et al. 2021

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Maize is a major global food crop and as one of the most productive grain crops, it can be eaten; it is also a good feed for the development of animal husbandry and essential raw material for light industry, chemical industry, medicine, and health. Diseases are the main factor limiting the high and stable yield of maize. Scientific and practical identification is a vital link to reduce the damage of diseases and accurate segmentation of disease spots is one of the fundamental techniques for disease identification. However, one single method cannot achieve a good segmentation effect to meet the diversity and complexity of disease spots. In order to solve the shortcomings of noise interference and oversegmentation in the Otsu segmentation method, a non-local mean filtered two-dimensional histogram was used to remove the noise in disease images and a new elite strategy improved comprehensive particle swarm optimization (PSO) method was used to find the optimal segmentation threshold of the objective function in this study. The experimental results of segmenting three kinds of maize foliar disease images show that the segmentation effect of this method is better than other similar algorithms and it has better convergence and stability.

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“…Some of our visual object systems, capable of shooting in the visible range, are also able to market objects in the invisible range. The information received from objects in the range of colored light can be useful in determining the maturity of plants, disease, and body and determining varieties, mating, the composition of functional properties, and contamination and disease of plants, seeds, fruits, vegetables, and fruits (Nosratabadi et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Application of neural networks in predicting the qualitative characteristics of fruits

et al. 2022

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In this research, the quality properties of persimmon were predicted using artificial intellect techniques. The persimmon samples were transferred to a computer vision lab, room temperature of 24 °C and 22% RH. The samples were divided into three groups for temperature treatment. They were kept at three temperature levels of 5 °C, 15 °C, and 24°C (control group) for 72 hours. The sample was then placed at room temperature and was imaged every second day for a 14 day period. After imaging, each sample underwent destructive tests to determine their quality attributes, including sugar content, firmness, and pH. The results indicate that the neural network's predicted values of acidity, firmness, and sugar of persimmon were not statistically significant differences from their actual values. In predicting the acidity of persimmon, the sugar RMSE is more than the two factors of firmness and acidity. For this reason, the accuracy of firmness and acidity is higher than sugar. MAPE is 10.11, 20.81, and 6.03 for acidity, firmness, and sugar, respectively. The model for sugar indicates a high difference between the actual values and the predicted values.

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Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS

Cited by 67 publications

References 44 publications

Creation of a Nanomodified Backfill Based on the Waste from Enrichment of Water-Soluble Ores

Creation of a Nanomodified Backfill Based on the Waste from Enrichment of Water-Soluble Ores

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

Application of neural networks in predicting the qualitative characteristics of fruits

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