Applications of Artificial Intelligence in Agriculture: A Review [Download]
Abstract—The application of Artificial Intelligence (AI) has been evident in the agricultural sector recently. The sector faces numerous challenges in order to maximize its yield including improper soil treatment, disease and pest infestation, big data requirements, low output, and knowledge gap between farmers
and technology. The main concept of AI in agriculture is its flexibility, high performance, accuracy, and cost-effectiveness.
This paper presents a review of the applications of AI in soil management, crop management, weed management and disease
management. A special focus is laid on the strength and limitations of the application and the way in utilizing expert systems for higher productivity.
Application of AI techniques and robotics in agriculture: A review [Download]
A Review
The aim of the proposed work is to review the various AI techniques (fuzzy logic (FL), artificial neural network (ANN), genetic algorithm (GA), particle swarm optimization (PSO), artificial potential field (APF), simulated an-
nealing (SA), ant colony optimization (ACO), artificial bee colony algorithm (ABC), harmony search algorithm (HS), bat algorithm (BA), cell decomposition (CD) and firefly algorithm (FA)) in agriculture, focusing on expert
systems, robots developed for agriculture, sensors technology for collecting and transmitting data, in an attempt to reveal their potential impact in the field of agriculture. None of the literature highlights the application of
AI techniques and robots in (Cultivation, Monitoring, and Harvesting) to understand their contribution to the agriculture sector and the simultaneous comparison of each based on its usefulness and popularity. This work
investigates the comparative analysis of three essential phases of agriculture: Cultivation, Monitoring, and Harvesting, by knowing the depth of AI involved and the robots utilized. The current study presents a systematic
review of more than 150 papers based on the existing automation application in agriculture from 1960 to 2021. It highlights the future research gap in making intelligent autonomous systems in agriculture. The paper concludes
with tabular data and charts comparing the frequency of individual AI approaches for specific applications in the agriculture field