Towards application of various machine learning techniques in agriculture

Jagtap, Santosh T.; Phasinam, Khongdet; Kassanuk, Thanwamas; Jha, Subhesh Saurabh; Ghosh, Tanmay; Thakar, Chetan M.

doi:10.1016/j.matpr.2021.06.236

Cited by 33 publications

(8 citation statements)

References 13 publications

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“…Thus, not all models present good performance, which can be connected to the quantity of data. Furthermore, the number of samples used was insufficient to present more robust predictions, and not all machine learning algorithms classify the same data equally (JAGTAP et al, 2022).…”

Section: Resultsmentioning

confidence: 99%

Data mining for ranking sorghum seed lots

et al. 2023

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The ranking of seed lots is a fundamental process for all companies in the seed industry. This work aims to demonstrate data mining methods for ranking sorghum seed lots during the seed processing through analysis of quality control data. Germination and cold tests were performed to verify the physiological quality of the lots. Seed samples from each lot were evaluated in two moments: post-cleaning and finished product (ready for marketing). The results after pre-processing totaled 188 rows of data with six attributes, encompassing 150 lots accepted for marketing, 6 rejected, and 32 intermediate lots. The classifiers used were J48, Random Forest, Classification Via Regression, Naive Bayes, Multilayer Perceptron, and IBk. The Resample filter was used for adjustment of the data. The k-fold technique was used for training, with ten folds. The metrics of Accuracy, Precision, Recall, F-measure, and ROC Area were used to verify the accuracy of the algorithms. The results obtained were used to determine the best machine-learning algorithm. IBk and J48 presented the highest accuracy of data; the IBk technique presented the best results. The Resample filter was essential for solving the data imbalance problem. Sorghum seed lots can be classified with great accuracy and precision through artificial intelligence and machine learning technique.

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Section: Resultsmentioning

confidence: 99%

Data mining for ranking sorghum seed lots

et al. 2023

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“…These models are used by individuals, organizations, and government agencies to anticipate and learn from data (Ebrahimi et al 2022b(Ebrahimi et al , 2022dPallathadka et al 2021) Virtual assistance solutions are powered by machine learning, which combines numerous deep learning models to give appropriate context and analyze spoken speech. We can now live happier, healthier, and more productive lives thanks to machine learning (Jagtap et al 2022).…”

Section: Application Of Machine Learning In E-commercementioning

confidence: 99%

Grocery Apps and Consumer Purchase Behavior: Application of Gaussian Mixture Model and Multi-Layer Perceptron Algorithm

Salamzadeh

Ebrahimi

Soleimani

et al. 2022

JRFM

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The purpose of this study is to investigate and compare the popularity of common grocery apps in Hungary as well as Iran. The data were gathered from Iranian and Hungarian users who had at least one online purchase experience using a grocery app. A Gaussian mixture model (GMM) and multi-layer perceptron (MLP) are used as supervised and unsupervised machine learning algorithms with Python programming to cluster customers and predict consumer behavior. The results revealed that Wolt in Hungary and Snappfood in Iran are the most popular grocery apps. Users in Iran are divided into three groups of users of app services and the type of full covariance has higher accuracy compared to the other three types (96%). Meanwhile, we found that the five apps used in Hungary have provided 95% accuracy from the users’ point of view based on the diagonal covariance. The MSE value (overfitting and cross-validation) is less than 0.1 in the MLP algorithm, which shows an acceptable amount of error. The results of overfitting indicate the proper fit of the MLP model. The findings of this study could be important for managers of online businesses. In the clustering section, the accuracy and value of consumer demographic information have been emphasized. Additionally, in the classification and prediction section, a kind of “customization” has been performed with an emphasis on market segmentation. This research used GMM and MLP machine learning algorithms as a creative way to cluster and classify consumers.

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“…In 2016, the supercomputer "deep mind" defeated the world champion of GO, Lee Sedol, which made the most intelligent human question the creativity of human beings. [7] Since then, ML has developed into a useful tool to serve human beings in a variety of fields, such as visual data processing, [8] agriculture, [9] and sports. [10] Although ML can indeed find the better local optimum points than human beings, it is still difficult to conduct ML in the field of polymer discovery.…”

Section: Introductionmentioning

confidence: 99%

“…In 2016, the supercomputer “deep mind” defeated the world champion of GO, Lee Sedol, which made the most intelligent human question the creativity of human beings. [ 7 ] Since then, ML has developed into a useful tool to serve human beings in a variety of fields, such as visual data processing, [ 8 ] agriculture, [ 9 ] and sports. [ 10 ]…”

Section: Introductionmentioning

confidence: 99%

The Rise of Machine Learning in Polymer Discovery

Yan

2023

Advanced Intelligent Systems

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In the recent decades, with rapid development in computing power and algorithms, machine learning (ML) has exhibited its enormous potential in new polymer discovery. Herein, the history of ML is described and the basic process of ML accelerated polymer discovery is summarized. Next, the four steps in this process are reviewed, that is, dataset selection, fingerprinting, ML framework, and new polymer generation. Finally, a couple of main challenges for ML accelerated polymer discovery is presented and the outlooks in this field are prospected. It is expected that this review can service as a useful tool for the people who just step into this field and deepen the understanding for the people who are already in this field.

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Towards application of various machine learning techniques in agriculture

Cited by 33 publications

References 13 publications

Data mining for ranking sorghum seed lots

Data mining for ranking sorghum seed lots

Grocery Apps and Consumer Purchase Behavior: Application of Gaussian Mixture Model and Multi-Layer Perceptron Algorithm

The Rise of Machine Learning in Polymer Discovery

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