Top Research Papers on Precision Agriculture

A survey regarding the potential use of UAVs in PA is provided, focusing on 20 relevant applications, which investigate in detail 20 UAV applications that are devoted to either aerial crop monitoring processes or spraying tasks.

The findings are that remote sensors are the most used technology, the required knowledge is an important criterion for deciding to implement precision agriculture, and no framework was found that guides its implementation.

The Support Vector Machine (SVM) is a Machine Learning (ML) algorithm which may be used for acquiring solutions towards better crop management. The applications of SVM in precision agriculture (PA) are compared by identifying its interactions with variables, comparing its model performance, highlighting its strengths and weaknesses, as well as suggestions for improvements. From the perspective of six ML applications in PA, we confirmed features which may benefit the model in general (e.g. feature selection) or specific applications (e.g. phenology). SVM was found to outperform most models, wit...

It is argued that this discourse of novelty is a reinvention of precision agriculture in the context of the growing “smart” agricultural economy, and that the smart agricultural economy is perhaps less about supporting land and its stewards than promising future tech and profits.

It is found that temperature and humidity have a larger impact on the sensor readings inside the greenhouse than initially thought, which is successfully solved through the airflow box design.

An overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in Precision agriculture is provided.

This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture.

It was shown that the most common disease is blight; fungus is the most important pathogen and grape and watermelon are the most studied crops; the most used drone type is the quadcopter and the most applied machine learning task is classification.

This review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture, and focuses on the integration of smart sensors, coupled with technologies such as the Internet of Things, big data analytics, and Artificial Intelligence.

A systematic review of ML applications in the field of agriculture shows how knowledge-based agriculture can improve the sustainable productivity and quality of the product.

An embedded system enhanced with ML functionalities, ensuring continuous detection of pest infestation inside fruit orchards and guaranteeing an extended battery life thanks to the integration of energy harvesting functionalities is presented.

Some of the latest recent advances in precision agriculture, including the Internet of Things (IoT) and how to make use of big data are examined, including drones, sensors, and machine learning.

The applications of nanotechnology and artificial intelligence in agriculture are discussed, highlighting the opportunities and challenges of using these technologies to achieve sustainable and precision agriculture.

The recent advances of the important soil sensors in agricultural applications, including temperature sensors, moisture sensors, organic matter compounds sensors, pH sensors, insect/pest sensors, and soil pollutant sensors are reviewed.

Nanofertilizers increase efficiency and sustainability of agricultural crop production. Due to their nanosize properties, they have been shown to increase productivity through target delivery or slow release of nutrients, thereby limiting the rate of fertilizer application required. Nanofertilizers can be synthesized via different approaches ranging from physical and chemical to green (biological) synthesis. The green approach is preferable because it makes use of less chemicals, thereby producing less chemical contamination and it is safer in comparison to physicochemical approaches. Hence, d...

The benefits of nanotechnology in this field include the development of advanced nanomaterials for enhanced seed germination and micronutrient supply, along with the alleviation of biotic and abiotic stress.

The results indicate that 84% of the interviewed farmers use at least one digital technology in their production system that differs according to technological complexity level, and 95% of farmers would like to learn more about new technologies to strengthen the agricultural development in their properties.

In the face of growing challenges in modern agriculture, such as climate change, sustainable resource management, and food security, drones are emerging as essential tools for transforming precision agriculture. This systematic review, based on an in-depth analysis of recent scientific literature (2020–2024), provides a comprehensive synthesis of current drone applications in the agricultural sector, primarily focusing on studies from this period while including a few notable exceptions of particular interest. Our study examines in detail the technological advancements in drone systems, includ...

The paper proposed the prediction model of Apple disease in the apple orchards of Kashmir valley using data analytics and Machine learning in IoT system and discusses the challenges faced when incorporating these technologies in the traditional farming approaches.

The main goal of this article is to acquire an awareness of the Big Data latest applications in smart agriculture and be acquainted with related social and financial challenges to be concentrated on.