by Vijay Kumar, Jai Singh Malhotra, Saurav Sharma and Parth Bhardwaj
Journal of Engineering Research and Sciences, Volume 1, Issue 3, Page # 09-18, 2022; DOI: 10.55708/js0103002
Abstract: Information about soil properties help the farmers to do effective and efficient farming, and yield mo . An attempt has been made in this paper to predict the soil properties using machine learning approaches. The main properties of soil prediction are Calcium, Phosphorus, pH, Soil Organic Carbon, and Sand. These properties greatly affect the production… Read More

(This article belongs to the Section Environmental Engineering (EVE))

by Hanan Hassan Ali Adlan, Reham Al Zamanan, Leen Almufleh, Tala Almuqrin and Jory Alhassoun
Journal of Engineering Research and Sciences, Volume 5, Issue 2, Page # 1-8, 2026; DOI: 10.55708/js0502001
Abstract: The Traditional date harvesting is labor-intensive and inefficient, leading to losses and quality issues. This paper introduces an AI-powered robotic system that automates date harvesting using computer vision, LiDAR sensors, and a robotic arm with a suction mechanism. The robot is capable of perception, it detects, classifies, and harvests ripe dates autonomously, ensuring minimal damage… Read More

(This article belongs to the Section Automation and Control Systems (ACS))

by Chris Nikolopoulos and Ryan Koralik
Journal of Engineering Research and Sciences, Volume 1, Issue 4, Page # 48-53, 2022; DOI: 10.55708/js0104006
Abstract: Prediction of plant growth and yield is one of the essential tasks that enables growers of food and agricultural products to effectively manage their crops. In this paper, a hybrid evolutionary/fuzzy machine learning approach is introduced where a genetic algorithm is deployed to learn the optimum membership functions of relevant fuzzy sets and a knowledge… Read More

(This article belongs to the Section Artificial Intelligence – Computer Science (AIC))