MLP-Powered Smart Application to Enhance Efficiency and Productivity in Algerian Agriculture

EasyChair Preprint no. 11576

Abstract

Artificial Intelligence (AI) has emerged as a pivotal driver of global economic growth, expanding its applications across various sectors. This paper introduces a smart application for crop selection tailored to the Algerian environment, aimed at enhancing smart agriculture in Algeria. By harnessing AI’s capabilities to enhance efficiency and productivity, our system promises to stimulate economic growth and contribute to the well-being of the agricultural sector. It assists in making informed crop choices, maximizing yields, and resulting in significant time and cost savings for farmers. Our study presents a comprehensive analysis of the Multi-Layer Perceptron Classifier (MLP) model, standing out with an accuracy rate of 91.81%. This underscores its potential as a reliable tool for crop selection in Algerian agriculture.

Keyphrases: Algerian environment, Crop selection, machine learning, Smart Agriculture, Smart Application

@Booklet{EasyChair:11576,
  author = {M'Hamed Mancer and Labib Sadek Terrissa and Soheyb Ayad and Hamed Laouz},
  title = {MLP-Powered Smart Application to Enhance Efficiency and Productivity in Algerian Agriculture},
  howpublished = {EasyChair Preprint no. 11576},

  year = {EasyChair, 2023}}