Mobile Applications for Crops Management

Lagos-Ortíz, Katty; Medina-Moreira, José; Sinche-Guzmán, Andrea; Garzón-Goya, Mayra; Vergara-Lozano, Vanessa; Valencia‐García, Rafael

doi:10.1007/978-3-030-00940-3_5

Cited by 3 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main applications of ICT in agriculture include the use of GPS and Geographic Information Systems (GIS) for precision farming, smartphone apps for e-learning (i.e., agroadvisory services) and crop management, RFID (Radio-Frequency Identification) for product tracking, etc. Apps in the agronomy domain can be classified in the following categories [8]: weather, soil preparation, sowing scheduling, farm management, soil fertility and crop nutrition, pest management, irrigation and drainage, precision agriculture, teaching and research apps. The number of ICT-enabled solutions for the recognition of plant pests and diseases is ever increasing [9,10].…”

Section: Introductionmentioning

confidence: 99%

Knowledge-Based System for Crop Pests and Diseases Recognition

2021

Self Cite

View full text Add to dashboard Cite

With the rapid increase in the world’s population, there is an ever-growing need for a sustainable food supply. Agriculture is one of the pillars for worldwide food provisioning, with fruits and vegetables being essential for a healthy diet. However, in the last few years the worldwide dispersion of virulent plant pests and diseases has caused significant decreases in the yield and quality of crops, in particular fruit, cereal and vegetables. Climate change and the intensification of global trade flows further accentuate the issue. Integrated Pest Management (IPM) is an approach to pest control that aims at maintaining pest insects at tolerable levels, keeping pest populations below an economic injury level. Under these circumstances, the early identification of pests and diseases becomes crucial. In this work, we present the first step towards a fully fledged, semantically enhanced decision support system for IPM. The ultimate goal is to build a complete agricultural knowledge base by gathering data from multiple, heterogeneous sources and to develop a system to assist farmers in decision making concerning the control of pests and diseases. The pest classifier framework has been evaluated in a simulated environment, obtaining an aggregated accuracy of 98.8%.

show abstract

Section: Introductionmentioning

confidence: 99%