Using self-organizing maps for determination of soil fertility (case study: Shiraz plain)

Mokarram, Marzieh; Najafi‐Ghiri, Mahdi; Zarei, Amin

doi:10.17221/139/2016-swr

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…This technique has been used popularly in agriculture sector. As an example, the study in [32] implemented SOM for classifying the influential factors of soil fertility. The study in [33] implemented Kohonen Self Organizing Feature Maps (SOFM) to analyze the effect of various soil properties that impacted the chemical and hydraulic processes in the soil.…”

Section: Methodsmentioning

confidence: 99%

Route Optimization of Agricultural Product Distribution Based on Agricultural Iot and Neural Network from the Perspective of Fabric Blockchain

Zuo

Cui

Lin

et al. 2022

Wireless Communications and Mobile Computing

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With the fast growth of AI and Internet of Things (IoT) technology, many agricultural product sales businesses and logistics sectors have started to concentrate on agricultural product distribution information operations. The requirements for the delivery service time are very high due to the features of perishable and highly easy dehydration of fresh agricultural goods. To preserve the freshness and quality of agricultural goods, the logistics and distribution process must be completed as rapidly as possible via appropriate low temperature control and the use of IoT technology. IoT technology will surely bring about the intelligent operation in the circulation of agricultural products. With the decentralized management of the Fabric blockchain, the investment and maintenance costs of the agricultural IoT will be reduced, which will help to improve the intelligence and scale of the agricultural IoT. Aiming at the specific problems, the path optimization problem in the process of agricultural product distribution is brought out. This paper completes the following work: (1) the traditional agricultural product distribution process is roughly described, and the shortcomings and problems of the traditional mode are explored and studied. On this basis, the agricultural product circulation mode under the IoT and neural network technology is introduced. (2) The TSP problem is defined, then some algorithms commonly used to solve the TSP problem are introduced, and then the theory and method of the SOM neural network and the basic principle of the ORC_SOM algorithm are introduced in detail. (3) Through a large number of experiments, the results prove the validity of the algorithm in this paper and the rationality of the theory.

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Section: Methodsmentioning

confidence: 99%

Route Optimization of Agricultural Product Distribution Based on Agricultural Iot and Neural Network from the Perspective of Fabric Blockchain

Zuo

Cui

Lin

et al. 2022

Wireless Communications and Mobile Computing

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“…The investigation demonstrates that the combined model has higher accuracy in forecasting the susceptibility of landslides than the SOMANN or ELM models. Afterward, Mokarram et al [63] proposed that SOMANN is a reliable tool to visualize data, and it can precisely categorize soil according to soil fertility evaluation results. Hopfield ANN is a symmetrical single-layer full-feedback neural network.…”

Section: Input Layermentioning

confidence: 99%

The Application of Machine Learning Techniques in Geotechnical Engineering: A Review and Comparison

Shao,

Yue,

Zhang

et al. 2023

Mathematics

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With the development of data collection and storage capabilities in recent decades, abundant data have been accumulated in geotechnical engineering fields, providing opportunities for the usage of machine learning approaches. Thus, a rising number of scholars are adopting machine learning techniques to settle geotechnical issues. In this paper, the application of three popular machine learning algorithms, support vector machine (SVM), artificial neural network (ANN), and decision tree (DT), as well as other representative algorithms in geotechnical engineering, is reviewed. Meanwhile, the applicability of diverse machine learning algorithms in settling specific geotechnical engineering issues is compared. The main findings are as follows: ANN, SVM, and DT have been widely adopted to solve a variety of geotechnical engineering issues, such as the classification of soil and rock types, predicting the properties of geotechnical materials, etc. Based on the collected relevant research, the performance of random forest (RF) in sorting soil types and assessing landslide susceptibility is satisfying; SVM has high precision in classifying rock types and forecasting rock deformation; and backpropagation ANNs and Hopfield ANNs are recommended to forecast rock compressive strength and soil settlement, respectively.

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“…Faigl J [18] applied SOM for the Multiple Traveling Salesman Problem (MTSP) with minmax objective to unravel the robotic problem of multi-goal path planning in the polygonal domain. The [19] factors influencing soil fertility in Shiraz plain, southern Iran was classified by Marzieh M, Mahdi N and Abdol R. The relationships among soil features were studied using the SOM in which the clustering tendency of soil fertility was investigated using seven parameters (N, P, K, Fe, Zn, Mn, and Cu). Subana S, Sallis P and Buckeridge J [20] researched on methods to analyze environmental effects integrated with human activities.…”

Section: Literature Reviewmentioning

confidence: 99%

The Taxonomy of Living Organisms Using Self-organizing Map

Philip¹

2020

AJAI

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The Self Organizing Map (SOM) is an unsupervised network algorithm that projects high dimensional data into low dimensional maps. The projection preserves the topology of the data so that similar data items are mapped to nearby locations on the map. The algorithm has been so popular because of its application in Computer Science and other areas; it has been applied in speech recognition, pattern identification, control engineering, earthquake detection et al. This research aimed to apply the SOM in the taxonomy of living organisms using 46 attributes. 68 animals from 6 phyla were considered and 46 attributes were used detailing their physical features, physiological features, evolution, adaptation, habitat et al. The features extracted were converted to 0s and 1s for the SOM algorithm to process. The result shows 96.569% accuracy of the SOM's classification but better accuracy can be obtained if the SOM had processed the data for about 1000 iterations. This research revealed that SOM is a veritable tool or algorithm that can be used to classify living organisms. This research will help taxonomists, biologists and students who spend much time in classifying living organism and it will be of help to researchers who want to explore the SOM algorithm as a solution to taxonomy of living organisms. The SOM will ease taxonomy and will help to minimize the stress and time involved in classifying thousands of living organisms.

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Using self-organizing maps for determination of soil fertility (case study: Shiraz plain)

Cited by 6 publications

References 21 publications

Route Optimization of Agricultural Product Distribution Based on Agricultural Iot and Neural Network from the Perspective of Fabric Blockchain

Route Optimization of Agricultural Product Distribution Based on Agricultural Iot and Neural Network from the Perspective of Fabric Blockchain

The Application of Machine Learning Techniques in Geotechnical Engineering: A Review and Comparison

The Taxonomy of Living Organisms Using Self-organizing Map

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