Spatial and Temporal Variability Management for All Farmers: A Cell-Size Approach to Enhance Coffee Yields and Optimize Inputs.

Coffee yield exhibits plant-level variability; however, due to operational issues, especially in smaller operations, the scouting and management of coffee yields are often hindered. Thus, a cell-size approach at the field level is proposed as a simple and efficient solution to overcome these constraints. This study aimed to present the feasibility of a cell-size approach to characterize spatio-temporal coffee production based on soil and plant attributes and yield (biennial effects) and to assess strategies for enhanced soil fertilization recommendations and economic results.

Spectral data of tropical soils using dry-chemistry techniques (VNIR, XRF, and LIBS): A dataset for soil fertility prediction.

Proximal soil sensing technologies, such as visible and near infrared diffuse reflectance spectroscopy (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS), are dry-chemistry techniques that enable rapid and environmentally friendly soil fertility analyses. The application of XRF and LIBS sensors in an individual or combined manner for soil fertility prediction is quite recent, especially in tropical soils. The shared dataset presents spectral data of VNIR, XRF, and LIBS sensors, even as the characterization of key soil fertility attributes (clay, organic matter, cation exchange capacity, pH, base saturation, and exchangeable P, K, Ca, and Mg) of 102 soil samples.

Sensor Fusion with NARX Neural Network to Predict the Mass Flow in a Sugarcane Harvester.

Measuring the mass flow of sugarcane in real-time is essential for harvester automation and crop monitoring. Data integration from multiple sensors should be an alternative to receive more reliable, accurate, and valuable predictions than data delivered by a single sensor. In this sense, the objective was to evaluate if the fusion of different sensors installed in a sugarcane harvester improves the mass flow prediction accuracy.

Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy.

Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory.

Combined Use of Vis-NIR and XRF Sensors for Tropical Soil Fertility Analysis: Assessing Different Data Fusion Approaches.

Visible and near infrared (vis-NIR) diffuse reflectance and X-ray fluorescence (XRF) sensors are promising proximal soil sensing (PSS) tools for predicting soil key fertility attributes. This work aimed at assessing the performance of the individual and combined use of vis-NIR and XRF sensors to predict clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable (ex-) nutrients (ex-P, ex-K, ex-Ca, and ex-Mg) in Brazilian tropical soils. Individual models using the data of each sensor alone were calibrated using multiple linear regressions (MLR) for the XRF data, and partial least squares (PLS) regressions for the vis-NIR data.

Spatial distribution of sorption and desorption process of C-radiolabelled hexazinone and tebuthiuron in tropical soil.

The understanding of the interaction between soil physicochemical attributes and herbicide behavior is fundamental for optimizing the efficient use of PRE-emergence herbicides in a more sustainable approach. However, it is still a poorly studied area within precision agriculture. Thus, the objective of this research was to evaluate the correlation of soil physicochemical attributes with the sorption and desorption processes of hexazinone and tebuthiuron to support application maps considering the field level variability.

Simplifying Sample Preparation for Soil Fertility Analysis by X-ray Fluorescence Spectrometry.

Portable X-ray fluorescence (pXRF) sensors allow one to collect digital data in a practical and environmentally friendly way, as a complementary method to traditional laboratory analyses. This work aimed to assess the performance of a pXRF sensor to predict exchangeable nutrients in soil samples by using two contrasting strategies of sample preparation: pressed pellets and loose powder (<2 mm). Pellets were prepared using soil and a cellulose binder at 10% w w followed by grinding for 20 min.