Yafit Cohen

Purpose: Zones for spatial irrigation management are often assumed to be static, dictated by non- or slowly-changing parameters, including elevation and slope, soil depth, texture and hydraulic properties, and other landscape properties. Sensing allows for the integration of static management zones (MZ) with a dynamic characterization of water status. Temporal remote sensing data, indicating crop status and, particularly, thermal imaging, indicating water status, suggests that, rather than static, dynamic re-zoning within crop seasons could be beneficial to precision water management. Methods: MZ delineation and precision irrigation for cotton were evaluated using a variable rate center pivot sprinkler system. The objectives were to evaluate precision management over time with both static and intra-seasonal dynamic zoning. Spatial distribution and irrigation decision-making were re-evaluated on a weekly basis. Irrigation followed commercial best management, based on target values of plant size (growth rate) in the early vegetative growth period and of plant water status (leaf water potential, LWP) in the reproductive and cotton boll production periods. Site-specific decision-making used drone-acquired normalized difference vegetation index as a proxy for crop height and growth rate and a thermal image-based crop water stress index as a proxy for LWP. Results and conclusion: Dynamic precision irrigation of cotton based on weekly decisions using the remote sensing products slightly decreased spatial variability while improving water productivity by 12% using static MZs and by 19% using dynamic MZs, compared to commercial uniform irrigation. The costs and benefits of implementing intra-seasonal dynamic zoning are discussed.

Cover crops (CC) effectively reduce soil erosion, yet field-scale quantification remains limited. This study used UAV imagery to assess rill erosion in a 20-ha field with and without CC. Data were collected 33 days after CC sowing, following heavy rainfall that created three rills. Vegetation pixels were removed, and a structure-from-motion algorithm generated pre- and post-erosion digital surface models. Change-detection analysis identified rill-affected areas enabling farmers to map low-yield zones and optimize seeding, fertilization, and irrigation. Results showed that CC significantly reduced rill erosion, with soil loss per m2 being 48%, 58% and 29% lower in CC plots than in bare soil. These findings confirm that CC mitigate rill erosion, even at early growth stages, while aiding precision agriculture by improved yield zone mapping.

Vertical greenery systems (VGS) have been proposed as a nature-based solution to mitigate the adverse effects of urban heat islands and climate change in cities. However, large-scale VGS are costly and require ongoing maintenance, typically carried out manually through trial and error based on professional experience. Advanced management is essential for the sustainability of VGS due to its limited accessibility and associated costs. To address these challenges, we examined the use of remote sensing methods for outdoor VGS monitoring as a basis for a precision agriculture approach for VGS management and maintenance. This study presents the first ongoing monitoring of real-scale VGS using thermal, hyperspectral, and RGB vegetation indices. These indices were employed for the early detection of vegetation stress, focusing on two case studies exhibiting visible yellowing symptoms. Through the application of unsupervised classification techniques, stressed pixels were successfully detected 14–35 days before visual yellowing, achieving an accuracy of 0.85–0.91. Additionally, the thermal index provided valuable information regarding the spatial distribution of watering along the VGS. Stress maps based on noninvasive methods were demonstrated, forming the basis of a spatial decision support system capable of detecting issues related to plant vitality and VGS irrigation management.

Sustainability in our food and fiber agriculture systems is inherently knowledge intensive. It is more likely to be achieved by using all the knowledge, technology, and resources available, including data-driven agricultural technology and precision agriculture methods, than by relying entirely on human powers of observation, analysis, and memory following practical experience. Data collected by sensors and digested by artificial intelligence (AI) can help farmers learn about synergies between the domains of natural systems that are key to simultaneously achieve sustainability and food security. In the quest for agricultural sustainability, some high-payoff research areas are suggested to resolve critical legal and technical barriers as well as economic and social constraints. These include: the development of holistic decision-making systems, automated animal intake measurement, low-cost environmental sensors, robot obstacle avoidance, integrating remote sensing with crop and pasture models, extension methods for data-driven agriculture, methods for exploiting naturally occurring Genotype x Environment x Management experiments, innovation in business models for data sharing and data regulation reinforcing trust. Public funding for research is needed in several critical areas identified in this paper to enable sustainable agriculture and innovation.

Precision agriculture is a reality in agriculture and is playing a key role as the industry comes to terms with the environment, market forces, quality requirements, traceability, vehicle guidance ...

Precision agriculture is a reality in agriculture and is playing a key role as the industry comes to terms with the environment, market forces, quality requirements, traceability, vehicle guidance ...

Accurate canopy extraction and temperature calculations are crucial to minimizing inaccuracies in thermal image-based estimation of orchard water status. Currently, no quantitative comparison of canopy extraction methods exists in the context of precision irrigation. The accuracies of four canopy extraction methods were compared, and the effect on water status estimation was explored for these methods: 2-pixel erosion (2PE) where non-canopy pixels were removed by thresholding and morphological erosion; edge detection (ED) where edges were identified and morphologically dilated; vegetation segmentation (VS) using temperature histogram analysis and spatial watershed segmentation; and RGB binary masking (RGB-BM) where a binary canopy layer was statistically extracted from an RGB image for thermal image masking. The field experiments occurred in a four-hectare commercial peach orchard during the primary fruit growth stage (III). The relationship between stem water potential (SWP) and crop water stress index (CWSI) was established in 2018. During 2019, a large dataset of ten thermal infrared and two RGB images was acquired. The canopy extraction methods had different accuracies: on 12 August, the overall accuracy was 83% for the 2PE method, 77% for the ED method, 84% for the VS method, and 90% for the RGB-BM method. Despite the high accuracy of the RGB-BM method, canopy edges and between-row weeds were misidentified as canopy. Canopy temperature and CWSI were calculated using the average of 100% of canopy pixels (CWSI_T100%) and the average of the coolest 33% of canopy pixels (CWSI_T33%). The CWSI_T33% dataset produced similar SWP–CWSI models irrespective of the canopy extraction method used, while the CWSI_T100% yielded different and inferior models. The results highlighted the following: (1) The contribution of the RGB images is not significant for canopy extraction. Canopy pixels can be extracted with high accuracy and reliability solely with thermal images. (2) The T33% approach to canopy temperature calculation is more robust and superior to the simple mean of all canopy pixels. These noteworthy findings are a step forward in implementing thermal imagery in precision irrigation management.