Characterization of variables for modeling agroclimatic and phytosanitary events in agricultural crops using deep learning models

Abstract

Digital transformation is increasingly present in the agricultural sector thanks to the development and availability of information technologies. However, the agricultural sector is one of the least digitized worldwide, including in countries with highly competitive and technologically advanced agricultural chains and processes. To optimize production using technological tools, it is necessary to combine variables associated with soil, climate, and plant type to estimate crop yields. Therefore, it is important to identify the physical variables that are of interest for the modeling of agroclimatic and phytosanitary events in agricultural crops. This study presents an exploratory type of documentary research to determine what variables are of interest for the modeling of agroclimatic and phytosanitary events in agricultural crops, using a critical analysis based on the results of related studies. Results show the characterization of (i) agroclimatic and biotic variables required to model the mentioned events, (ii) detection of physical evidence associated to biotic factors by means of the analysis of anomalies in the wavelengths of the spectral reflectance of the productive units, (iii) the computational model based on deep learning for the processing of these variables. Furthermore, a data pipeline is proposed that indicates the flow that the characterized variables must go through the analytical tasks.

Agroclimatic and Phytosanitary Events and Emerging Technologies for Their Identification in Avocado Crops: A Systematic Literature Review

“…Therefore, it is necessary to conduct a study related to avocado crops and the application of technologies in their production, to analyze the use of technological advances in the management of biotic and abiotic factors in avocado production farms, especially in tropical regions such as Colombia and the Andean region. Therefore, it is crucial to identify the factors associated with APEs in avocado crops [9,33]. For this reason, this paper is interested in finding the most relevant variables associated with APEs in avocado production, the incidence of these events in avocado crop productivity and their relationship with risk management, and the technologies used to detect and analyze pests and diseases in avocado.…”

Analysis of Weather Prediction, Resource Management, and Land Optimization on the Application of Big Data Analytics in Agricultural Land Utilization in Agrarian Areas of West Java

“…The use of big data technology can determine algorithm models for agricultural big data technology and the application system of the agricultural Internet of Things, leading to advancements in agricultural planting technology, economic management, and industry upgrading [6]. Additionally, data mining and machine learning techniques can be employed to analyze agricultural conditions and scenarios, optimize parameters, and maximize crop yield [7]. By adopting digital technologies and big data processing, intelligent agriculture can provide decision-making support for agricultural activities, improving productivity and economic returns [8].…”