Alejandro Marulanda-Tobón

Interferometric Synthetic Aperture Radar (InSAR) enables the measurement of ground deformation with high spatial coverage and frequent temporal sampling, making it a valuable tool for landslide monitoring in remote mountainous regions. However, in tropical mountainous environments, dense vegetation, steep topography, and rapid surface changes often reduce interferometric coherence, limiting the reliability of C-band observations. This study evaluates the applicability of C-band and L-band spaceborne InSAR data for detecting slow-moving landslides in the northern tropical Andes. The analysis focuses on two case studies in the northern Colombian Andes, where slow-moving landslides have affected two municipalities. Time-series deformation and velocity analysis were derived using a Small Baseline Subset (SBAS) approach applied to Sentinel-1 (C-band) and SAOCOM-1 (L-band) SAR datasets. The detected deformation patterns were compared with independent field evidence reported in previous geotechnical investigations of the study area. Results show that both sensors successfully identified the spatial extent of the slow-moving landslide. However, L-band data exhibited higher coherence and reduced noise levels in densely vegetated zones. These differences highlight the improved performance of L-band observations under tropical vegetation conditions, while C-band data remain effective for detecting moderate surface displacements where coherence is preserved. The findings provide guidance for selecting appropriate SAR wavelengths for landslide detection in tropical mountainous regions and support the integration of multi-frequency InSAR for operational hazard assessment.

Spectral indices such as NDRE (Normalized Difference Red Edge Index), CCCI (Canopy Chlorophyll Content Index), and IRECI (Inverted Red Edge Chlorophyll Index), derived from the Red Edge band of MSI/Sentinel-2A (B05, B06, B07), are critical for coffee monitoring. To align the Red Edge band (20 m resolution) with the NIR band (10 m resolution), the nearest neighbor, bilinear, cubic and Lanczos resampling methods were used, available in the Terra package in the R software(4.4.0). This study evaluates these methods using two original B05 images from 24 November 2023, and 21 September 2023, covering the “Ouro Verde” (15 ha) and “Canto do Rio” (45 ha) farms in Bahia, Brazil. A total of 500 random points were analyzed using PSF, linear models, and cross-validation with R², MAE, and RMSE. PSF analysis confirmed data integrity, and the cubic method demonstrated the best performance (R² = 0.996, MAE = 0.008 and RMSE = 0.012 in the “Ouro Verde” Farm and R² = 0.995, MAE = 0.007 and RMSE = 0.011 in the “Canto do Rio” Farm). The results highlight the importance of selecting appropriate resampling methods for precise remote sensing in coffee cultivation, ensuring accurate digital processing aligned with study objectives.

Spectral indices such as NDRE (Normalized Difference Red Edge Index), CCCI (Canopy Chlorophyll Content Index), and IRECI (Inverted Red Edge Chlorophyll Index), based on the Red Edge band of MSI/Sentinel-2 (B05, B06, B07 images), are essential tools in coffee monitoring. These indices require resampling the Red Edge band (20 m resolution) to match the NIR (10 m resolution) using methods such as nearest neighbor, bilinear, cubic, and Lanczos. In this technical note, we evaluated these resampling methods using two original B05 images, selected on November 24, 2023, and September 21, 2023, with reference points from the farms "Ouro Verde" (15 hectares) in Barra do Choça (BA) and "Canto do Rio" (45 hectares) in Luís Eduardo Magalhães (BA), respectively. A total of 500 random points were generated and analyzed using PSF, linear models, and cross-validation with metrics such as R², MAE, and RMSE. The PSF analysis indicated the integrity of the data for further analysis. The cubic method showed the best performance (R² = 0.996, MAE = 20.87, RMSE = 32.67). The validation results of the resampling methods suggest that this procedure is crucial for accurate digital processing in remote sensing for coffee cultivation and should be aligned with the study objectives.

Remote sensing technologies have been successfully used for deforestation monitoring, and with the wide availability of satellite products from different platforms, forest monitoring applications have grown in recent years. The observed potential in these technologies motivates the development of forest mapping and monitoring tools that could also be used for neighboring applications like agriculture or land-use mapping. A literature review confirmed the research areas of interest in deforestation monitoring using synthetic aperture radar (SAR) and data fusion techniques, which guided the formulation of the method developed in this article consisting of a data preprocessing workflow for SAR (Sentinel-1) and multispectral (Sentinel-2) data and a procedure for the selection of a machine learning model for forest/nonforest segmentation evaluated in different combinations of Sentinel-1 and Sentinel-2 bands. The selected model is a random forest algorithm that uses C-band SAR dual-polarimetric bands, intensity features, and vegetation indices derived from optical/multispectral data. The selected random forest classifier’s balanced accuracies were 79–81%, and the f1-scores were 0.72–0.76 for the validation set. The results allow the obtention of yearly forest/nonforest and forest loss maps in the study area of Bajo Cauca in Colombia, a region with a documented high deforestation rate.

Computer vision is a powerful technology that has enabled solutions in various fields by analyzing visual attributes in images. One field that has taken advantage of computer vision is agricultural automation, which promotes high-quality crop production. The nutritional status of a crop is a crucial factor in determining its productivity. This status is mediated by approximately 14 chemical elements acquired by the plant, and their determination plays a pivotal role in farm management. To address the timely identification of nutritional disorders, this study focuses on the classification of three levels of phosphorus deficiencies through individual leaf analysis. The methodological steps include: (1) generating a database with laboratory-grown maize plants that were induced to total phosphorus deficiency, medium deficiency, and total nutrition, using different capture devices; (2) processing the images with state-of-the-art transfer learning architectures (i.e. VGG16, ResNet50, GoogLeNet, DenseNet201, and MobileNetV2); and (3) evaluating the classification performance of the models using the created database. The results show that the VGG16 model achieves superior performance, with 98% classification accuracy. However, the other studied architectures also demonstrate competitive performance and are considered state-of-the-art automatic leaf deficiency detection tools. The proposed method can be a starting point to fine-tune machine vision-based solutions tailored for real-time monitoring of crop nutritional status.

Avocado is one of the most commercialized and profitable fruits in the international market. Its cultivation and production are centered in countries characterized by tropical and subtropical climatic conditions, many of them with emerging economies. Moreover, the use of technology is key to agricultural production improvement strategies. Using avocado crop data to forecast the potential impacts of biotic and abiotic factors, combined with smart farming technologies, growers can apply measures during a single production phase to reduce the risks caused by pests and weather variations. Therefore, this paper aims to distinguish the most relevant variables related to agroclimatic and phytosanitary events in avocado crops, their incidence on production and risk management, as well as the emerging technologies used for the identification and analysis of pests and diseases in avocados. A scientific literature search was performed, and the first search found 608 studies, and once the screening process was applied, 37 papers were included in this review. In the results, three research questions were answered that described the pests and diseases with high impact on avocado production, along with the data sources and the principal enabling technologies used in the identification of agroclimatic and phytosanitary events in avocados. Some challenges and trends in the parameterization of the technology in field conditions for data collection are also highlighted.

Fruit counting and ripeness detection are computer vision applications that have gained strength in recent years due to the advancement of new algorithms, especially those based on artificial neural networks (ANNs), better known as deep learning. In agriculture, those algorithms capable of fruit counting, including information about their ripeness, are mainly applied to make production forecasts or plan different activities such as fertilization or crop harvest. This paper presents the RipSetCocoaCNCH12 dataset of cocoa pods labeled at four different ripeness stages: stage 1 (0–2 months), stage 2 (2–4 months), stage 3 (4–6 months), and harvest stage (>6 months). An additional class was also included for pods aborted by plants in the early stage of development. A total of 4116 images were labeled to train algorithms that mainly perform semantic and instance segmentation. The labeling was carried out with CVAT (Computer Vision Annotation Tool). The dataset, therefore, includes labeling in two formats: COCO 1.0 and segmentation mask 1.1. The images were taken with different mobile devices (smartphones), in field conditions, during the harvest season at different times of the day, which could allow the algorithms to be trained with data that includes many variations in lighting, colors, textures, and sizes of the cocoa pods. As far as we know, this is the first openly available dataset for cocoa pod detection with semantic segmentation for five classes, 4116 images, and 7917 instances, comprising RGB images and two different formats for labels. With the publication of this dataset, we expect that researchers in smart farming, especially in cocoa cultivation, can benefit from the quantity and variety of images it contains.

Frosty pod rot, caused by Moniliophthora roreri, is the most damaging disease of cacao in Latin America and, to better comprehend its epidemiology, we must understand its dissemination and proliferation. However, we do not know how M. roreri spores loads fluctuate in time and space due to the lack of a reliable technique to quantify M. roreri spores in the fields. Therefore, we developed a method that relies on spore traps and qPCR to detect and quantify M. roreri spore loads. This study demonstrated that the qPCR protocol can detect down to 0.025 ng of M. roreri DNA and quantify between 0.006 ng and 60 ng. Moreover, it demonstrated that qPCR protocol can detect and quantify DNA extracted from spore suspension and spore traps containing at least 2.9 × 10⁴ M. roreri spores. However, the variability of the estimates for spore samples was high. Finally, we described a spore-trap device designed to carry spore traps in the field. The qPCR protocol and spore-trap device here developed will help in the understanding of the M. roreri dissemination patterns since they can be used to assess the environmental loads of M. roreri spore in cacao fields.

Frosty pod rot, caused by Moniliophthora roreri, is the most damaging disease of cacao in Latin America. However, to better comprehend its epidemiology, we must understand its dissemination and proliferation. Still, we ignore how loads of M. roreri spores fluctuate across growing seasons since we lack a reliable technique to quantify M. roreri spores in the fields. Therefore, we developed a method that uses a spore trap to capture M. roreri spores and qPCR to quantify them. This study demonstrated that this technique could quantify 3.9 x104 M. roreri spores with a 95 % confidence level. However, it could not differentiate between M. roreri and its close relative, M. perniciosa. Despite this limitation, we could detect and quantify Moniliophthora spores from environmental samples taken from a cacao field. This technique can help the phytopathologist address studies more accurately in disseminating cacao pathogens.