[HTML][HTML] Predicting sustainable crop yields: Deep learning and explainable AI tools

I Malashin, V Tynchenko, A Gantimurov, V Nelyub… - Sustainability, 2024 - mdpi.com
Optimizing agricultural productivity and promoting sustainability necessitates accurate
predictions of crop yields to ensure food security. Various agricultural and climatic variables …

Fuzzy Methods in Smart Farming: A Systematic Review

IW Widayat, AA Arsyad, AJ Mantau, Y Adhitya… - Informatica, 2024 - informatica.vu.lt
Smart Farming (SF) has garnered interest from computer science researchers for its
potential to address challenges in Smart Farming and Precision Agriculture (PA). This …

[HTML][HTML] Nonlinear Dynamics and Machine Learning for Robotic Control Systems in IoT Applications

VA Knights, O Petrovska, JG Kljusurić - Future Internet, 2024 - mdpi.com
This paper presents a novel approach to robotic control by integrating nonlinear dynamics
with machine learning (ML) in an Internet of Things (IoT) framework. This study addresses …

Modeling of Unmanned Aerial Vehicles for Smart Agriculture Systems Using Hybrid Fuzzy PID Controllers

S Amertet, G Gebresenbet, HM Alwan - Applied Sciences, 2024 - mdpi.com
Unmanned aerial vehicles have a wide range of uses in the military field, non-combat
situations, and civil works. Due to their ease of operation, unmanned aerial vehicles (UAVs) …

IoT, AI, and Robotics Applications in the Agriculture Sector

A Kumar, N Karn, H Sharma - Advanced Computational Methods for …, 2024 - igi-global.com
This chapter explores the transformative impact of internet of things (IoT), artificial
intelligence (AI), and robotics in modern agriculture. By addressing challenges such as …

Emerging Trends in IoT, AI and Agricultural Science

S Abuzar, D Kumar, S Gupta - Agriculture 4.0 - taylorfrancis.com
The confluence of artificial intelligence (AI), agricultural science, and the Internet of Things
(IoT) has revolutionised the cultivation, management, and optimisation of agricultural …