Growth Height Determination of Tree Walls for Precise Monitoring in Apple Fruit Production Using UAV Photogrammetry

In apple cultivation, spatial information about phenotypic characteristics of tree walls would be beneficial for precise orchard management. Unmanned aerial vehicles (UAVs) can collect 3D structural information of ground surface objects at high resolution in a cost-effective and versatile way by using photogrammetry. The aim of this study is to delineate tree wall height information in an apple orchard applying a low-altitude flight pattern specifically designed for UAVs. This flight pattern implies small distances between the camera sensor and the tree walls when the camera is positioned in an oblique view toward the trees. In this way, it is assured that the depicted tree crown wall area will be largely covered with a larger ground sampling distance than that recorded from a nadir perspective, especially regarding the lower crown sections. Overlapping oblique view images were used to estimate 3D point cloud models by applying structure-from-motion (SfM) methods to calculate tree wall heights from them. The resulting height models were compared with ground-based light detection and ranging (LiDAR) data as reference. It was shown that the tree wall profiles from the UAV point clouds were strongly correlated with the LiDAR point clouds of two years (2018: R2 = 0.83; 2019: R2 = 0.88). However, underestimation of tree wall heights was detected with mean deviations of −0.11 m and −0.18 m for 2018 and 2019, respectively. This is attributed to the weaknesses of the UAV point clouds in resolving the very fine shoots of apple trees. Therefore, the shown approach is suitable for precise orchard management, but it underestimated vertical tree wall expanses, and widened tree gaps need to be accounted for

Tree Water Status in Apple Orchards Measured by Means of Land Surface Temperature and Vegetation Index (LST–NDVI) Trapezoidal Space Derived from Landsat 8 Satellite Images

In this study, the split window (SW) method was applied for land surface temperature (LST) retrieval using Landsat 8 in two apple orchards (Glindow, Altlandsberg). Four images were acquired during high demand of irrigation water from July to August 2018. After pre-processing images, the normalized difference vegetation index (NDVI) and LST were calculated by red, NIR, and thermal bands. The results were validated by interpolated infrared thermometer (IRT) measurements using the inverse distance weighting (IDW) method. In the next step, the temperature vegetation index (TVDI) was calculated based on the trapezoidal NDVI/LST space to determine the water status of apple trees in the case studies. Results show good agreement between interpolated LST using IRT measurements and remotely sensed LST calculation using SW in all satellite overpasses, where the absolute mean error was between 0.08 to 4.00 K and root mean square error (RMSE) values ranged between 0.71 and 4.23 K. The TVDI spatial distribution indicated that the trees suffered from water stress on 7 and 23 July and 8 August 2018 in Glindow apple orchard with the mean value of 0.69, 0.57, and 0.73, whereas in the Altlandsberg orchard on 17 August, the irrigation system compensated the water deficit as indicated by the TVDI value of 0.34. Moreover, a negative correlation between TVDI and vegetation water content (VWC) with correlation coefficient (r) of −0.81 was observed. The corresponding r for LST and VWC was equal to −0.89, which shows the inverse relation between water status and temperature-based indices. The results indicate that the LST and/or TVDI calculation using the proposed methods can be effectively applied for monitoring tree water status and support irrigation management in orchards using Landsat 8 satellite images without requiring ground measurements

The Feasibility, Practicality and Uses of Detecting Crop Water Stress in Southern Ontario Apple Orchards with a UAS

UAS (Unmanned Aerial Systems) are becoming more common place in agricultural sites around the world. While the accuracy of achieving NDVI (Normalized Difference Vegetation Index) from a UAS is well understood, few studies have attempted to acquire other plant health attributes such as CWSI (Crop Water Stress Index), particularly in horticulture such as apple orchards. In addition, no academic studies up to the time of this writing have explored the perceived usefulness of data obtained from a UAS for the average farmer. This study explored the practicality and feasibility of using UAS for apple orchards in Southern Ontario. This study sought to find out if NDVI and CWSI can be accurately obtained from a UAS for apple orchards and if this data can be feasibly obtained and is practical for the average Ontario apple farmer. By flying a UAS over a volunteer orchard and conducting charrette style interviews with orchard owners with the obtained data, the results showed that data is indeed useful to the farmers, despite improvements needed for CWSI accuracy. However, this data is only useful during key times of the growing season and obtaining this data, while feasible, requires planning and logistics around weather and government red tape. This study has laid the ground work for future studies to use as a staging point to improve CWSI estimate accuracy, create new methods of observing health attributes or diseases in apple orchards, and obtain more information on the usefulness of UAS data for Ontario farmers

Pesticide Applications in Agricultural Systems

The effective and efficient application of PPP in agriculture is a very complex task, involving a lot of aspects ranging from active substance properties to climatic conditions, from target structure to equipment features, from operator awareness to the reference legislative framework. Some of these aspects have been treated in the six papers collected in this Special Issue on “Pesticide Applications in Agricultural Systems”

Unmanned aerial vehicle based tree canopy characteristics measurement for precision spray applications

The critical components for applying the correct amount of agrochemicals are fruit tree characteristics such as canopy height, canopy volume, and canopy coverage. An unmanned aerial vehicle (UAV)-based tree canopy characteristics measurement system was developed using image processing approaches. The UAV captured images using a high-resolution red-green-blue (RGB) camera. A digital surface model (DSM) and a digital terrain model (DTM) were generated from the captured images. A tree canopy height map was generated from the subtraction of DSM and DTM. A total of 24 apple trees were randomly targeted to measure the canopy characteristics. Region of interest (ROI) was generated across the boundary of each targeted tree. The height of all pixels within each ROI was computed separately. The pixel with maximum height was considered as the height of the respective tree. For computing canopy volume, the sum of all pixel heights from individual ROI was multiplied by the square of ground sample distance (GSD) of 5.69 mm·pixel−1. A segmentation method was employed to calculate the canopy coverage of the individual trees. The segmented canopy pixel area was divided by the total pixel area within the ROI. The results showed an average relative error of 0.2 m(6.64%) while comparing automatically measured tree height with ground measurements. For tree canopy volume, a mean absolute error of 0.25 m3 and a root mean square error of 0.33 m3 were achieved. The study estimated the possible agrochemical requirement for spraying the fruit trees, ranging from 0.1 to 0.32 l based on tree canopy volumes. The overall investigations suggest that the UAV-based tree canopy characteristics measurements could be a potential tool to calculate the pesticide requirement for precision spraying applications in tree fruit orchards

UAS Application in Agriculture: A Review of Technologies Possible to Apply in Portugal

The world population has been significantly growing over the last years. Consequently, also the needs and search of raw materials and goods has been increasing. In this context, the production, in a sustainable way and in the needed quantities, of food is a source of concern and study. At the same time a big evolution in the Unmanned Aerial Vehicles (UAV) has been verified, both in terms at the level of the equipment, and the operational scenarios where they have been used. Portugal presents itself as a country where the agriculture and livestock activities have a very big predominance in the use of the available soil and the economy. However, the lack of studies and implementation of new technologies keeps on being a limiting factor to the increase of productivity, sustainable use of the available resources and of the value that agriculture adds to the national trade balance. The main objective of this dissertation was to show that it is possible to apply several new methods and techniques, more specifically UAVs, to the Portuguese agricultural scenario. For this extensive research was carried out and a set of studies with the potential to be adapted and implement in Portugal were selected, encompassing different cultures and activities associated with them. After the chosen studies had been exposed and carefully analysed it was possible to perceive that the application of UAS’ in Portuguese agriculture would be a great added value insofar as it could lead to savings of several million euros both in increasing productivity, as well as in reducing costs with chemicals and field tests that are becoming obsolete. The saving of limited natural resources, namely water is also a very important factor.A população mundial tem vindo a crescer de forma muito significativa ao longo dos últimos anos. Consequentemente, também as necessidades e a procura de matérias-primas e bens têm aumentado. Neste contexto, a produção, de forma sustentável e nas quantidades necessárias, de bens alimentares é fonte de preocupação e estudo. Paralelamente tem-se verificado uma evolução bastante grande nos Veículos Aéreos Não Tripulados (UAV), quer ao nível do equipamento em si, quer ao dos cenários operacionais nos quais têm vindo a ser empregues. Portugal apresenta-se como um país em que as atividades agropecuárias têm uma predominância muito grande no uso do solo disponível e na economia. No entanto, a falta de estudos e da implementação de novas tecnologias continuam a ser fatores limitativo ao aumento de produtividade, do aproveitamento sustentável dos recursos disponíveis e do valor que a agricultura agrega à balança comercial nacional. O objetivo principal desta dissertação foi mostrar que é possível aplicar diversos novos métodos e técnicas, e mais especificamente UAV, ao cenário agrícola português. Para tal foi efetuada uma extensa pesquisa e selecionado um conjunto de estudos considerados relevantes e com potencial de serem adaptados e implementados em Portugal, englobando diversas culturas e atividades a elas associadas. Com este propósito e depois de os estudos escolhidos terem sido expostos e cuidadosamente analisados foi possível perceber que a aplicação de UAS na agricultura portuguesa seria uma grande mais-valia na medida em que poderia conduzir à poupança de diversos milhões de euros tanto no aumento de produtividade, assim como na redução dos custos em químicos e testes de campo que se estão a tornar obsoletos. Poderá também contribuir para a poupança de recursos naturais, nomeadamente de água

Remote Sensing Energy Balance Model for the Assessment of Crop Evapotranspiration and Water Status in an Almond Rootstock Collection

One of the objectives of many studies conducted by breeding programs is to characterize and select rootstocks well-adapted to drought conditions. In recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks. However, none of these studies have been able to quantify the behavior of crop evapotranspiration in almond rootstocks under different water regimes. In this study, remote sensing phenotyping methods were used to assess the evapotranspiration of almond cv. “Marinada” grafted onto a rootstock collection. In particular, the two-source energy balance and Shuttleworth and Wallace models were used to, respectively, estimate the actual and potential evapotranspiration of almonds grafted onto 10 rootstock under three different irrigation treatments. For this purpose, three flights were conducted during the 2018 and 2019 growing seasons with an aircraft equipped with a thermal and multispectral camera. Stem water potential (Ψstem) was also measured concomitant to image acquisition. Biophysical traits of the vegetation were firstly assessed through photogrammetry techniques, spectral vegetation indices and the radiative transfer model PROSAIL. The estimates of canopy height, leaf area index and daily fraction of intercepted radiation had root mean square errors of 0.57 m, 0.24 m m–1 and 0.07%, respectively. Findings of this study showed significant differences between rootstocks in all of the evaluated parameters. Cadaman® and Garnem® had the highest canopy vigor traits, evapotranspiration, Ψstem and kernel yield. In contrast, Rootpac® 20 and Rootpac® R had the lowest values of the same parameters, suggesting that this was due to an incompatibility between plum-almond species or to a lower water absorption capability of the rooting system. Among the rootstocks with medium canopy vigor, Adesoto and IRTA 1 had a lower evapotranspiration than Rootpac® 40 and Ishtara®. Water productivity (WP) (kg kernel/mm water evapotranspired) tended to decrease with Ψstem, mainly in 2018. Cadaman® and Garnem® had the highest WP, followed by INRA GF-677, IRTA 1, IRTA 2, and Rootpac® 40. Despite the low Ψstem of Rootpac® R, the WP of this rootstock was also high.info:eu-repo/semantics/publishedVersio

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects

The ornamental crop industry is an important contributor to the economy in the United States. The industry has been facing challenges due to continuously increasing labor and agricultural input costs. Sensing and automation technologies have been introduced to reduce labor requirements and to ensure efficient management operations. This article reviews current sensing and automation technologies used for ornamental nursery crop production and highlights prospective technologies that can be applied for future applications. Applications of sensors, computer vision, artificial intelligence (AI), machine learning (ML), Internet-of-Things (IoT), and robotic technologies are reviewed. Some advanced technologies, including 3D cameras, enhanced deep learning models, edge computing, radio-frequency identification (RFID), and integrated robotics used for other cropping systems, are also discussed as potential prospects. This review concludes that advanced sensing, AI and robotic technologies are critically needed for the nursery crop industry. Adapting these current and future innovative technologies will benefit growers working towards sustainable ornamental nursery crop production

IPM2.0: PRECISION AGRICULTURE FOR SMALL-SCALE CROP PRODUCTION

In order to manage pests impacting New England crop production integrated pest management (IPM) practices should be reevaluated or updated regularly to ensure that effective control of crop pests is being achieved. Three fungal taxa, Colletotrichum gloeosporioides, C. acutatum, and Glomerella cingulata, are currently associated with bitter-rot of apple (Malus domestica), with C. acutatum typically being the dominant species found in the northeastern United States. However, a recent phylogenetic study demonstrated that both C. gloeosporioides and C. acutatum are species complexes with over 10 distinct species being recovered from apple between the two studies. Based on this recent information, the objectives of this study were 1) to complete a phylogenetic analysis to determine species diversity and distribution of Colletotrichum isolates associated with bitter-rot and Glomerella leaf spot in the northeastern United States and 2) to evaluate the sensitivity of these isolates to several commercially used fungicides. A multi-gene phylogenetic analysis was completed using ITS, GADPH and BT gene sequences in order to determine which species and how many species of Colletotrichum were infecting apples in the northeastern U.S. The results of this study demonstrated that C. fioriniae is the primary pathogen causing both bitter rot and Glomerella leaf spot in the northeastern U.S. A second experiment was conducted in order to update management practices for apple scab, caused by the ascomycete Venturia inaequalis. The objective of this project was to evaluate the ability of RIMpro, an apple scab warning system, to control apple scab in New England apple orchards in addition to evaluating the performance of potassium bicarbonate + sulfur as a low-cost alternative spray material for the control of apple scab suitable for organic apple production. Use of RIMpro allowed for the reduction in the total number of spray applications made during the primary scab season by two sprays in 2013 and one spray in 2014 (28% and 25% reductions, respectively). Also, the potassium bicarbonate + sulfur treatment was shown to provide the same level of control as Captan. Finally, disease outbreaks, insect infestation, nutrient deficiencies, and weather variation constantly threaten to diminish annual yields and profits in orchard crop production systems. Automated crop inspection with an unmanned aerial vehicle (UAV) can allow growers to regularly survey crops and detect areas affected by disease or stress and lead to more efficient targeted applications of pesticides, water and fertilizer. The overall goal of this project was to develop a low cost aerial imaging platform coupling imaging sensors with UAVs to be used for monitoring crop health. Following completion of this research, we have identified a useful tool for agricultural and ecological applications

Monitoring of emerging water stress situations by thermal and vegetation indices in different almond cultivars

In recent years, the area dedicated to modern irrigated almond plantations has increased significantly in Spain. However, the legal irrigation allocations are lower than the maximum water requirements of the crop in most cases. Therefore, almond growers are forced to implement regulated deficit irrigation strategies on their farms, applying water stress in certain resistant phenological periods and avoiding it in sensitive periods. Given the need to monitor the water status of the crop, especially in the most sensitive periods to water stress, the objective of this work was to evaluate the sensitivity of two UAV-based crop water status indicators to detect early water stress conditions in four almond cultivars. The field trial was conducted during 2020 in an experimental almond orchard, where two irrigation strategies were established: full irrigation (FI), which received 100% of irrigation requirements (IR), and regulated deficit irrigation (RDI), which received 70% of IR during the whole irrigation period except during the kernel-filling stage when received 40% IR. The UAV flights were performed on four selected dates of the irrigation season. The Crop Water Status Index (CWSI) and the Normalized Difference Vegetation Index (NDVI) were derived from thermal and multispectral images, respectively, and compared to classical water status indicators, i.e., stem water potential (Ψstem ), stomatal conductance (gs ), and photosynthetic rate (AN ). Of the four flights performed, three corresponded to mild water stress conditions and a single flight was performed under moderate water stress conditions. Under mild water stress, CWSI was not able to capture the differences between FI and RDI trees that were observed with Ψstem . Under moderate stress conditions, CWSI was sensitive to the water deficit reached in the trees and showed significant differences among both irrigation treatments. No differences were observed in the CWSI and NVDI response to water stress among cultivars. Although NDVI and CWSI were sensitive to water stress, the low signal intensity observed in NDVI makes this index less robust than CWSI to monitor crop water stress. It can be concluded that UAV-based CWSI measurements are reliable to monitor almond water status, although for early (mild) levels of water stress, Ψstem seems to be the preferred option.Junta de Andalucía AVA.AVA2019.05