Precision Oliviculture: Research Topics, Challenges, and Opportunities—A Review

Since the beginning of the 21st century, there has been an increase in the agricultural area devoted to olive growing and in the consumption of extra virgin olive oil (EVOO). The continuous change in cultivation techniques implemented poses new challenges to ensure environmental and economic sustainability. In this context, precision oliviculture (PO) is having an increasing scientific interest and impact on the sector. Its implementation depends on various technological developments: sensors for local and remote crop monitoring, global navigation satellite system (GNSS), equipment and machinery to perform site-specific management through variable rate application (VRA), implementation of geographic information systems (GIS), and systems for analysis, interpretation, and decision support (DSS). This review provides an overview of the state of the art of technologies that can be employed and current applications and their potential. It also discusses the challenges and possible solutions and implementations of future technologies such as IoT, unmanned ground vehicles (UGV), and machine learning (ML)

Positioning accuracy comparison of GNSS receivers used for mapping and guidance of agricultural machines

Global Navigation Satellite Systems (GNSS) allow the determination of the 3D position of a point on the Earth’s surface by measuring the distance from the receiver antenna to the orbital position of at least four satellites. Selecting and buying a GNSS receiver, depending on farm needs, is the first step for implementing precision agriculture. The aim of this work is to compare the positioning accuracy of four GNSS receivers, different for technical features and working modes: L1/L2 frequency survey-grade Real-Time Kinematic (RTK)-capable Stonex S7-G (S7); L1 frequency RTK-capable Stonex S5 (S5); L1 frequency Thales MobileMapper Pro (TMMP); low-cost L1 frequency Quanum GPS Logger V2 (QLV2). In order to evaluate the positioning accuracy of these receivers, i.e., the distance of the determined points from a reference trajectory, different tests, distinguished by the use or not of Real-Time Kinematic (RTK) differential correction data and/or an external antenna, were carried out. The results show that all satellite receivers tested carried out with the external antenna had an improvement in positioning accuracy. The Thales MobileMapper Pro satellite receiver showed the worst positioning accuracy. The low-cost Quanum GPS Logger V2 receiver surprisingly showed an average positioning error of only 0.550 m. The positioning accuracy of the above-mentioned receiver was slightly worse than that obtained using Stonex S7-G without the external antenna and differential correction (maximum positioning error 0.749 m). However, this accuracy was even better than that recorded using Stonex S5 without differential correction, both with and without the external antenna (average positioning error of 0.962 m and 1.368 m)

Precision Oliviculture: Research Topics, Challenges, and Opportunities—A Review

Since the beginning of the 21st century, there has been an increase in the agricultural area devoted to olive growing and in the consumption of extra virgin olive oil (EVOO). The continuous change in cultivation techniques implemented poses new challenges to ensure environmental and economic sustainability. In this context, precision oliviculture (PO) is having an increasing scientific interest and impact on the sector. Its implementation depends on various technological developments: sensors for local and remote crop monitoring, global navigation satellite system (GNSS), equipment and machinery to perform site-specific management through variable rate application (VRA), implementation of geographic information systems (GIS), and systems for analysis, interpretation, and decision support (DSS). This review provides an overview of the state of the art of technologies that can be employed and current applications and their potential. It also discusses the challenges and possible solutions and implementations of future technologies such as IoT, unmanned ground vehicles (UGV), and machine learning (ML)

Evaluation of Multispectral Data Acquired from UAV Platform in Olive Orchard

Precision agriculture is a management strategy to improve resource efficiency, production, quality, profitability and sustainability of the crops. In recent years, olive tree management is increasingly focused on determining the correct health status of the plants in order to distribute the main resource using different technologies. In the olive grove, the focus is often on the use of multispectral information from UAVs (Unmanned Aerial Vehicle), but it is not known how important spectral and biometric information actually is for the agronomic management of the olive grove. The aim of this study was to investigate the ability of multispectral data acquired from a UAV platform to predict nutritional status, biometric characteristics, vegetative condition and production of olive orchard as tool to DSS. Data were collected on vegetative characteristics closely related to vigour such as trunk cross-sectional area (TCSA), Nitrogen concentration of the leaves, canopy area and canopy volume. The production was collected for each plant to create an accurate yield map. The flight was carried out with a UAV equipped with a multispectral camera, at an altitude of 50 m and with RTK correction. The flight made it possible to determine the biometric condition and the spectral features through the normalized difference vegetation index (NDVI). The NDVI map allowed to determine the canopy area. The Structure for Motion (SfM) algorithm allow to determine the 3D canopy volume. The experiment showed that the NDVI was able to determine with high accuracy the vegetative characteristic as canopy area (r = 0.87 ***), TCSA (r = 0.58 ***) and production (r = 0.63 ***). The vegetative parameters are closely correlated with the production, especially the canopy area (r = 0.75 ***). Data clustering showed that the production of individual plants is closely dependent on leaf nitrogen concentration and vigour status

Application of Precision Agriculture for the Sustainable Management of Fertilization in Olive Groves

Olive tree growing (Olea europaea L.) has considerably increased in the last decades, as has the consumption of extra virgin olive oil in the world. Precision agriculture is increasingly being applied in olive orchards as a new method to manage agronomic variability with the aim of providing individual plants with the right input amount, limiting waste or excess. The objective of this study was to develop a methodology on a GIS platform using GEOBIA algorithms in order to build prescription maps for variable rate (VRT) nitrogen fertilizers application in an olive orchard. The fertilization plan was determined for each tree by applying its own nitrogen balance, taking into account the variability of nitrogen in soil, leaf, production, and actual biometric and spectral conditions. Each olive tree was georeferenced using the S7-G Stonex instrument with real-time kinematic RTK positioning correction and the trunk cross section area (TCSA) was measured. Soil and leaves were sampled to study nutrient variability. Soil and plant samples were analyzed for all major physical and chemical properties. Spectral data were obtained using a multispectral camera (DJI multispectral) carried by an unmanned aerial vehicle (UAV) platform (DJI Phantom4). The biometric characteristics of the plants were extracted from the achieved normalized vegetation index (NDVI) map. The obtained prescription map can be used for variable rate fertilization with a tractor and fertilizer spreader connected via the ISOBUS system. Using the proposed methodology, the variable rate application of nitrogen fertilizer resulted in a 31% reduction in the amount to be applied in the olive orchard compared to the standard dose

Application of Precision Agriculture for the Sustainable Management of Fertilization in Olive Groves

Olive tree growing (Olea europaea L.) has considerably increased in the last decades, as has the consumption of extra virgin olive oil in the world. Precision agriculture is increasingly being applied in olive orchards as a new method to manage agronomic variability with the aim of providing individual plants with the right input amount, limiting waste or excess. The objective of this study was to develop a methodology on a GIS platform using GEOBIA algorithms in order to build prescription maps for variable rate (VRT) nitrogen fertilizers application in an olive orchard. The fertilization plan was determined for each tree by applying its own nitrogen balance, taking into account the variability of nitrogen in soil, leaf, production, and actual biometric and spectral conditions. Each olive tree was georeferenced using the S7-G Stonex instrument with real-time kinematic RTK positioning correction and the trunk cross section area (TCSA) was measured. Soil and leaves were sampled to study nutrient variability. Soil and plant samples were analyzed for all major physical and chemical properties. Spectral data were obtained using a multispectral camera (DJI multispectral) carried by an unmanned aerial vehicle (UAV) platform (DJI Phantom4). The biometric characteristics of the plants were extracted from the achieved normalized vegetation index (NDVI) map. The obtained prescription map can be used for variable rate fertilization with a tractor and fertilizer spreader connected via the ISOBUS system. Using the proposed methodology, the variable rate application of nitrogen fertilizer resulted in a 31% reduction in the amount to be applied in the olive orchard compared to the standard dose