A5064923278- Remote Sensing in Agriculture
- Smart Agriculture and AI
- Plant Water Relations and Carbon Dynamics
- Horticultural and Viticultural Research
- Spectroscopy and Chemometric Analyses
- Identification and Quantification in Food
- Remote Sensing and Land Use
- Food Supply Chain Traceability
- Date Palm Research Studies
- IoT and Edge/Fog Computing
Instituto de Ciencias Agrarias
2023-2024
Consejo Superior de Investigaciones Científicas
2023-2024
Universidad Politécnica de Madrid
2023-2024
In an effort to reduce pesticide use, agronomists and computer scientists have joined forces develop site-specific weed detection classification systems. These systems aim recognize locate species within a crop field, using precision equipment apply required herbicides timely only where needed, with the objective of reducing sprayable surface eliminate given protect crop, both economic environmental benefits. Yet, climate change on rise, common weeds are expected undergo some changes adapt...
Accurate weed species identification is crucial for effective site-specific management (SSWM), enabling targeted and timely control measures each in crop field. This study advanced the current approach to species-level during early growth stage by integrating unmanned aerial vehicles (UAVs) imagery with standard convolutional neural networks (CNNs) models such as VGG16, Resnet152 Inception-Resnet-v2. For this, a robust dataset was created 33,467 labels of weeds (Atriplex patula, Chenopodium...
Early detection of weeds is crucial to manage effectively, support decision-making and prevent potential crop losses. This research presents an innovative approach develop a specialized cognitive system for classifying detecting early-stage at the species level. The primary objective was create automated multiclass discrimination using computing, regardless weed growth stage. Initially, model trained tested on dataset 31,002 UAV images, including ten manually identified by experts early...
Abstract Precise irrigation management requires accurate knowledge of crop water demand to adequately optimize use efficiency, especially relevant in arid and semi-arid regions. While unoccupied aerial vehicles (UAV) have shown great promise improve the for crops such as vineyards, there still remains large uncertainties accurately quantify vegetation requirements, through physically-based methods. Notably, thermal remote sensing has been be a promising tool evaluate stress at different...
Abstract Purpose High resolution imagery from unmanned aerial vehicles (UAVs) has been established as an important source of information to perform precise irrigation practices, notably relevant for high value crops often present in semi-arid regions such vineyards. Many studies have shown the utility thermal infrared (TIR) sensors estimate canopy temperature inform on vine physiological status, while visible-near (VNIR) and 3D point clouds derived red–green–blue (RGB) photogrammetry also...
Identifying weed species at early-growth stages is critical for precision agriculture. Accurate classification the species-level enables targeted control measures, significantly reducing pesticide use. This paper presents a dataset of RGB images captured with Sony ILCE-6300L camera mounted on an unmanned aerial vehicle (UAV) flying altitude 11 m above ground level. The covers various agricultural fields in Spain, focusing two summer crops: maize and tomato. It designed to enhance...
In an effort to reduce pesticide use, agronomists and computer scientists have joined forces develop site-specific weed detection classification systems. These systems aim recognize locate species within a crop field, using precision equipment apply required herbicides timely only where needed, with the objective of reducing sprayable surface eliminate given protect crop, both economic environmental benefits. With climate change on rise, common weeds are expected undergo some changes adapt...