A New Low-Cost Device Based on Thermal Infrared Sensors for Olive Tree Canopy Temperature Measurement and Water Status Monitoring

Abstract

In recent years, many olive orchards, which are a major crop in the Mediterranean basin, have been converted into intensive or super high-density hedgerow systems. This configuration is more efficient in terms of yield per hectare, but at the same time the water requirements are higher than in traditional grove arrangements. Moreover, irrigation regulations have a high environmental (through water use optimization) impact and influence on crop quality and yield. The mapping of (spatio-temporal) variability with conventional water stress assessment methods is impractical due to time and labor constraints, which often involve staff training. To address this problem, this work presents the development of a new low-cost device based on a thermal infrared (IR) sensor for the measurement of olive tree canopy temperature and monitoring of water status. The performance of the developed device was compared to a commercial thermal camera. Furthermore, the proposed device was evaluated in a commercially managed olive orchard, where two different irrigation treatments were established: a full irrigation treatment (FI) and a regulated deficit irrigation (RDC), aimed at covering 100% and 50% of crop evapotranspiration (ETc), respectively. Predawn leaf water potential (ΨPD) and stomatal conductance (gs), two widely accepted indicators for crop water status, were regressed to the measured canopy temperature. The results were promising, reaching a coefficient of determination R2 ≥ 0.80. On the other hand, the crop water stress index (CWSI) was also calculated, resulting in a coefficient of determination R2 ≥ 0.79. The outcomes provided by the developed device support its suitability for fast, low-cost, and reliable estimation of an olive orchard’s water status, even suppressing the need for supervised acquisition of reference temperatures. The newly developed device can be used for water management, reducing water usage, and for overall improvements to olive orchard management.

Musculoskeletal Risks of Farmers in the Olive Grove (Jaén-Spain)

“…Progress could also be made towards sustainable mechanization that reduces the use of synthetic phytosanitary products and takes advantage of other emerging technologies (drones, robots, artificial intelligence, machine learning, big data, infrared sensors, and deep learning, etc.) [63][64][65][66][67][68][69][70][71][72][73][74], thus helping to maintain the desired balance. A curious and promising piece of data is the feeling of there being fewer ailments in organic olive groves (1.56%) compared to the traditional olive grove, with and without slopes.…”

Automatic Crop Canopy Temperature Measurement Using a Low-Cost Image-Based Thermal Sensor: Application in a Pomegranate Orchard under a Permanent Shade Net House

“…Global population growth and climate change will accentuate the need for a more efficient use of water resources [ 1 ]. Consequently, the objective of optimizing water consumption for irrigated agriculture has been widely addressed in the scientific literature, leading to the proposal of alternative irrigation techniques such as RDI strategies [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. These strategies are based on the reduction in irrigation endowment during non-critical periods for the crop.…”

Multispectral remote sensing for accurate acquisition of rice phenotypes: Impacts of radiometric calibration and unmanned aerial vehicle flying altitudes

“…For example, hyperspectral cameras were exploited to estimate crop leaf area index (LAI), leaf chlorophyll content, and leaf nitrogen content ( Delegido et al, 2010 ; Xie et al, 2014 ; Raj et al, 2021 ). Thermal infrared sensors were utilized to retrieve the temperature and water status of the olive tree canopy ( Noguera et al, 2020 ) and help to predict the soybean yield ( Maimaitijiang et al, 2020 ). Moreover, the UAV-based LiDAR sensors were recently employed to determine the height of different crops (ten Harkel et al, 2020 ).…”