Jul 2026· 2026 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC)· pp. 1-6· 0 citations· 17 references
Abstract
Looking at the exponentially growing population the effective and automatic farming techniques are important to provide major percentage of humanity's nutrition. To increase sustainable food production and reducing waste the optimized decision-making and classification of agricultural produce is crucial. To overcome the time-consuming manual inspection technique the automatic and cost-effective techniques of nutrient management, water utilization, and pest control are proposed. In this work, Naive Bayes, Multi-Layer Perceptron (MLP), and Kernel Support Vector Machine (K-SVM) models are applied to the dry beans data to multiclass classification of bean varieties. The efficacy of these algorithms is measured based on the standard evaluation parameters such as accuracy and F1_macro. The K-SVM classifier achieves the highest overall accuracy, with a value of 0.93 (93%), followed by MLP at 0.92 (92%) and Naive Bayes at 0.90(90%), as shown in the results section.
Dry beans are a staple food of strategic importance worldwide and in our country due to their high protein content and nutritional value. In agricultural production, preserving seed quality and ensuring product standardization play a critical role in food safety and commercial sustainability. In this context, the accur...
Hasan Selim Sındır, Y. Taşpınar· Intelligent Methods in Engin...· 0 citations
This research technique focuses on dry bean classification using a machine-learning (ML) algorithm to enhance the quality of agricultural products, improve market value, and auto-mate processes, thereby overcoming the limitations of conventional techniques. This technique supports the food industry business by improvin...
Aniket Sudke, Harshad V. Jadhav, Aishwarya Bangar et al.· Sigma Journal of Engineering...· 0 citations
Feature contribution analysis indicates that the NPK parameters, moisture content and electrical conductivity provide predictive information for distinguishing soil fertility classes, although the magnitude of their contribution varies across the different algorithms.
Fathiah Alatas, Sujiyo Miranto, Brian Abdurafi Rambu Basae et al.· Cybersecurity and Innovative...· 0 citations
The adoption of Internet of Things (IoT) and Machine Learning (ML) in agriculture presents a revolutionary step toward efficient agricultural practices using data to address global issues surrounding food security. This review conducted a systematic search of the Scopus, IEEE Xplore, ScienceDirect, and SpringerLink dat...
M. Umamaheswari, K. D· Frontiers in Artificial Inte...· 0 citations
ABSTRACT The present study was conducted at the Federal Rural University of Rio de Janeiro, in the years 2024 and 2025, to evaluate the performance of three supervised classifiers - maximum likelihood, random forest, and support vector machine - for spectral discrimination between maize (Zea mays L.) and the weed (Cype...
T. M. de Souza, M. M. de Barros, J. C. de Siqueira et al.· Revista Brasileira de Engenh...· 0 citations
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