Canopy Index Evaluation for Precision Management in an Intensive Olive Orchard

The evaluation of the canopy in orchard cultivation is a key aspect for the main cultivation techniques, such as pruning, thinning, harvesting, production and improved fruit quality. The possibility of having a periodic screening of the state of development of the vegetation can be of practical support to growers. Research on the application of precision agriculture has provided tools for reading and interpreting crops, and the resulting information is potentially useful. Many of the systems under study provide after monitoring information processing systems that reduce the timeliness of intervention. Especially in intensive systems such as olive groves, knowing the precise intervention points is often essential. In the present work, a multi-parameter instrument was used for field monitoring on the agricultural tractor to analyse the canopy. The system allows measuring various indicators such as height and density of the canopy and the temperature and humidity of the ambient air and at the leaf level. The first evaluation of the data made it possible to identify areas with greater vegetative concentration and greater or lesser development. The system made it possible to identify with good approximation the homogeneous areas, based on the Canopy Index (CI) evaluation to be subjected to subsequent and specific management efforts, dividing them into low, ordinary, and high vegetative growth. The results highlight the possibility of directly combining operators able to intervene with the same passage, selecting based on differences in growth, typical varietal specificities, and areas of deficient development or that are affected by plant diseases, confirming the objective of defining the areas of the orchard that require different management and workload techniques.

Download Full-text

Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing

Remote Sensing ◽

10.3390/rs11030221 ◽

2019 ◽

Vol 11 (3) ◽

pp. 221 ◽

Cited By ~ 7

Author(s):

Beatriz Rey ◽

Nuria Aleixos ◽

Sergio Cubero ◽

José Blasco

Keyword(s):

Structural Information ◽

Xylella Fastidiosa ◽

Low Cost ◽

Plant Diseases ◽

Remotely Operated Vehicle ◽

Proximal Sensing ◽

Leaf Level ◽

Olive Groves ◽

Olive Trees ◽

Field Robot

The use of remote sensing to map the distribution of plant diseases has evolved considerably over the last three decades and can be performed at different scales, depending on the area to be monitored, as well as the spatial and spectral resolution required. This work describes the development of a small low-cost field robot (Remotely Operated Vehicle for Infection Monitoring in orchards, XF-ROVIM), which is intended to be a flexible solution for early detection of Xylella fastidiosa (X. fastidiosa) in olive groves at plant to leaf level. The robot is remotely driven and fitted with different sensing equipment to capture thermal, spectral and structural information about the plants. Taking into account the height of the olive trees inspected, the design includes a platform that can raise the cameras to adapt the height of the sensors to a maximum of 200 cm. The robot was tested in an olive grove (4 ha) potentially infected by X. fastidiosa in the region of Apulia, southern Italy. The tests were focused on investigating the reliability of the mechanical and electronic solutions developed as well as the capability of the sensors to obtain accurate data. The four sides of all trees in the crop were inspected by travelling along the rows in both directions, showing that it could be easily adaptable to other crops. XF-ROVIM was capable of inspecting the whole field continuously, capturing geolocated spectral information and the structure of the trees for later comparison with the in situ observations.

Download Full-text

Precision Agriculture Technologies for Management of Plant Diseases

Sustainability in Plant and Crop Protection - Plant Disease Management Strategies for Sustainable Agriculture through Traditional and Modern Approaches ◽

10.1007/978-3-030-35955-3_13 ◽

2020 ◽

pp. 259-278

Author(s):

Siva K. Balasundram ◽

Kamlesh Golhani ◽

Redmond R. Shamshiri ◽

Ganesan Vadamalai

Keyword(s):

Precision Agriculture ◽

Plant Diseases

Download Full-text

Increase in ring width, vessel number and δ18O in olive trees infected with Xylella fastidiosa

Tree Physiology ◽

10.1093/treephys/tpaa095 ◽

2020 ◽

Vol 40 (11) ◽

pp. 1583-1594

Author(s):

Erika Sabella ◽

Samuele Moretti ◽

Holger Gärtner ◽

Andrea Luvisi ◽

Luigi De Bellis ◽

...

Keyword(s):

Xylella Fastidiosa ◽

Ring Width ◽

Plant Diseases ◽

Susceptible Cultivar ◽

Water Conductivity ◽

Leaf Transpiration ◽

Leaf Level ◽

Olive Quick Decline Syndrome ◽

Olive Trees ◽

Tree Stem

Abstract Xylella fastidiosa (Xf) Wells, Raju et al., 1986 is a bacterium that causes plant diseases in the Americas. In Europe, it was first detected on the Salento Peninsula (Italy), where it was found to be associated with the olive quick decline syndrome. Here, we present the results of the first tree-ring study of infected and uninfected olive trees (Olea europaea L.) of two different cultivars, one resistant and one susceptible, to establish the effects induced by the spread of the pathogen inside the tree. Changes in wood anatomical characteristics, such as an increase in the number of vessels and in ring width, were observed in the infected plants of both the cultivars Cellina di Nardò (susceptible to Xf infection) and Leccino (resistant to Xf infection). Thus, whether infection affects the mortality of the tree or not, the tree shows a reaction to it. The presence of occlusions was detected in the wood of both 4-year-old branches and the tree stem core. As expected, the percentage of occluded vessels in the Xf-susceptible cultivar Cellina di Nardò was significantly higher than in the Xf-resistant cultivar Leccino. The δ 18O of the 4-year-old branches was significantly higher in infected trees of both cultivars than in noninfected trees, while no variations in δ 13C were observed. This suggests a reduction in leaf transpiration rates during infection and seems to be related to the occlusions observed in rings of the 4-year-old branches. Such occlusions can determine effects at leaf level that could influence stomatal activity. On the other hand, the significant increase in the number of vessels in infected trees could be related to the tree’s attempt to enhance water conductivity in response to the pathogen-induced vessel occlusions.

Download Full-text

Applications of UAV Thermal Imagery in Precision Agriculture: State of the Art and Future Research Outlook

Remote Sensing ◽

10.3390/rs12091491 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1491 ◽

Cited By ~ 10

Author(s):

Gaetano Messina ◽

Giuseppe Modica

Keyword(s):

Precision Agriculture ◽

State Of The Art ◽

Plant Stress ◽

Stress Conditions ◽

Plant Diseases ◽

Basic Knowledge ◽

Plant Phenotyping ◽

Future Research ◽

Thermal Data ◽

Low Altitude

Low-altitude remote sensing (RS) using unmanned aerial vehicles (UAVs) is a powerful tool in precision agriculture (PA). In that context, thermal RS has many potential uses. The surface temperature of plants changes rapidly under stress conditions, which makes thermal RS a useful tool for real-time detection of plant stress conditions. Current applications of UAV thermal RS include monitoring plant water stress, detecting plant diseases, assessing crop yield estimation, and plant phenotyping. However, the correct use and interpretation of thermal data are based on basic knowledge of the nature of thermal radiation. Therefore, aspects that are related to calibration and ground data collection, in which the use of reference panels is highly recommended, as well as data processing, must be carefully considered. This paper aims to review the state of the art of UAV thermal RS in agriculture, outlining an overview of the latest applications and providing a future research outlook.

Download Full-text

Non-Destructive Monitoring of Maize Nitrogen Concentration Using a Hyperspectral LiDAR: An Evaluation from Leaf-Level to Plant-Level

Remote Sensing ◽

10.3390/rs13245025 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5025

Author(s):

Kaiyi Bi ◽

Zheng Niu ◽

Shunfu Xiao ◽

Jie Bai ◽

Gang Sun ◽

...

Keyword(s):

Precision Agriculture ◽

Spatial Information ◽

Nitrogen Concentration ◽

Point Clouds ◽

Future Research ◽

Least Squares Regression ◽

N Concentration ◽

Leaf Level ◽

Plant Level ◽

Remote Sensing Techniques

Advanced remote sensing techniques for estimating crop nitrogen (N) are crucial for optimizing N fertilizer management. Hyperspectral LiDAR (HSL) data, with both spectral and spatial information of the targets, can extract more plant properties than traditional LiDAR and hyperspectral imaging systems. In this study, we tested the ability of HSL in terms of estimating maize N concentration at the leaf-level by using spectral indices and partial least squares regression (PLSR) methods. Subsequently, the N estimation was scaled up to the plant-level based on HSL point clouds. Biomass, extracted with structural proxies, was utilized to exhibit its supplemental effect on N concentration. The results show that HSL has the ability to extract N concentrations at both the leaf-level and the canopy-level, and PLSR showed better performance (R2 > 0.6) than the single spectral index (R2 > 0.4). In comparison to the stem height and maximum canopy width, the plant height had the strongest ability (R2 = 0.88) to estimate biomass. Future research should utilize larger datasets to test the viability of using HSL to monitor the N concentration of crops, which is beneficial for precision agriculture.

Download Full-text

Hyperspectral Reflectance of Light-Adapted Leaves Can Predict Both Dark- and Light-Adapted Chl Fluorescence Parameters, and the Effects of Chronic Ozone Exposure on Date Palm (Phoenix dactylifera)

International Journal of Molecular Sciences ◽

10.3390/ijms21176441 ◽

2020 ◽

Vol 21 (17) ◽

pp. 6441 ◽

Cited By ~ 1

Author(s):

Lorenzo Cotrozzi ◽

Giacomo Lorenzini ◽

Cristina Nali ◽

Elisa Pellegrini ◽

Vincenzo Saponaro ◽

...

Keyword(s):

Precision Agriculture ◽

Large Scale ◽

Goodness Of Fit ◽

Date Palm ◽

Full Range ◽

Ambient Air ◽

Hyperspectral Data ◽

Ozone Exposure ◽

Plant Phenotyping ◽

Spectral Models

High-throughput and large-scale measurements of chlorophyll a fluorescence (ChlF) are of great interest to investigate the photosynthetic performance of plants in the field. Here, we tested the capability to rapidly, precisely, and simultaneously estimate the number of pulse-amplitude-modulation ChlF parameters commonly calculated from both dark- and light-adapted leaves (an operation which usually takes tens of minutes) from the reflectance of hyperspectral data collected on light-adapted leaves of date palm seedlings chronically exposed in a FACE facility to three ozone (O3) concentrations (ambient air, AA; target 1.5 × AA O3, named as moderate O3, MO; target 2 × AA O3, named as elevated O3, EO) for 75 consecutive days. Leaf spectral measurements were paired with reference measurements of ChlF, and predictive spectral models were constructed using partial least squares regression. Most of the ChlF parameters were well predicted by spectroscopic models (average model goodness-of-fit for validation, R2: 0.53–0.82). Furthermore, comparing the full-range spectral profiles (i.e., 400–2400 nm), it was possible to distinguish with high accuracy (81% of success) plants exposed to the different O3 concentrations, especially those exposed to EO from those exposed to MO and AA. This was possible even in the absence of visible foliar injury and using a moderately O3-susceptible species like the date palm. The latter view is confirmed by the few variations of the ChlF parameters, that occurred only under EO. The results of the current study could be applied in several scientific fields, such as precision agriculture and plant phenotyping.

Download Full-text

Computational Intelligence for Pathological Issues in Precision Agriculture

Handbook of Research on Computational Intelligence for Engineering, Science, and Business ◽

10.4018/978-1-4666-2518-1.ch026 ◽

2013 ◽

pp. 672-698

Author(s):

Sanjeev S. Sannakki ◽

Vijay S. Rajpurohit ◽

V. B. Nargund ◽

Arun R. Kumar ◽

Prema S. Yallur

Keyword(s):

Precision Agriculture ◽

Accurate Method ◽

Plant Diseases ◽

Machine Learning Techniques ◽

Physiological Factors ◽

Intelligent Decision Support System ◽

Learning Techniques ◽

Intelligent Decision ◽

Information And Communication ◽

Processing Techniques

Plant Pathology is the scientific study of plant diseases, caused by pathogens and environmental conditions (physiological factors). Detection and grading of plant diseases by machine vision is an essential research topic as it may prove useful in monitoring large fields of crops. This can be of great benefit to those users, who have little or no information about the crop they are growing. Also, in some developing countries, farmers may have to go long distances to contact experts to dig up information which is expensive and time consuming. Therefore, looking for a fast, automatic, less expensive, and accurate method to detect plant diseases is of great realistic significance. Such an efficient system can be modeled by integrating the various tools/techniques of information and communication technology (ICT) in agriculture. The objective of the present chapter is to model an intelligent decision support system for detection and grading of plant diseases which encompasses image processing techniques and soft computing/machine learning techniques.

Download Full-text

Influence of weed cover on leaf-level CO2 and H2O fluxes in an olive grove

10.5194/egusphere-egu2020-5181 ◽

2020 ◽

Author(s):

Sergio Aranda-Barranco ◽

Andrew S Kowalski ◽

Penélope Serrano-Ortiz ◽

Enrique P Sánchez-Cañete

Keyword(s):

Random Trees ◽

The Other ◽

Organic Carbon Content ◽

Olive Grove ◽

Weed Cover ◽

Leaf Level ◽

Olive Groves ◽

Ecosystem Level ◽

Olive Trees

The management of olive groves has a direct impact on the environment in the Mediterranean region since it is one of the most representative crops in this area. In order to prevent erosion and improve the physical-chemical conditions of the soil in these crops, the maintenance of weed cover in the alleys is an increasingly common practice. It increases the organic carbon content in the soil, improves biodiversity indices and enhances various ecosystem services such as pollination and infiltration. Now, the role of vegetation cover in olive groves on biogeochemical cycles is being studied. Although previous studies have quantified the combined effect of weed cover and olive trees on carbon and water at ecosystem level, the role of this conservation practice at the leaf level has not yet been explored.The aim of this study is to quantify the effect of weed cover on the net CO2 assimilation (An) and transpiration (T) rates in an irrigated olive grove. To do this, two plots of olive trees with irrigation (Olea europea L. "Arbequina") in southeast Spain were sampled. In the weed-cover one (WC), spontaneous vegetation is maintained until it is mechanically mowed and left in place. In the weed-free (WF) a glyphosate-based herbicide is applied. The data were taken with a portable gas analyzer (LI-6800, Li-Cor) controlling the following environmental variables on olive leaves: atmospheric CO2, relative humidity, photosynthetic active radiation and temperature. One campaign per month was carried out (from January-2018 to January-2019) where 10 random trees were analysed in each treatment. In addition, an eddy covariance tower provided CO2 and H2O fluxes at ecosystem level and they were compared with the fluxes obtained from leaf-level campaigns.The results shown significant differences for T only in the period after mowing with Twc= 2.0 &#177; 0.7 mmol H2O m-2s-1 vs Twf = 2.5 &#177; 1.0 mmol H2O m-2s-1. However, in this period ET is equal in both treatments, which suggests that the alleys with mowed weed has more ET than bare soil in the other treatment. On the other hand, there are significant differences for Anet only in the period before mowing with Anet-wc = 5.5 &#177; 3.1 &#956;mol CO2 m-2s-1 vs Anet-wf = 8.0 &#177; 3.6 &#956;mol CO2 m-2s-1. When the weeds are mowed, Anet is matched in both treatments. However, higher values of NEEwc than NEEwf &#160;are observed in the period before mowing. This suggest that the weed-cover olive groves at ecosystem level take up more carbon when the weed-cover is established although the leaves of olive trees are capturing less CO2.

Download Full-text

Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops

Sensors ◽

10.3390/s21010171 ◽

2020 ◽

Vol 21 (1) ◽

pp. 171

Author(s):

Thomas Fahey ◽

Hai Pham ◽

Alessandro Gardi ◽

Roberto Sabatini ◽

Dario Stefanelli ◽

...

Keyword(s):

Crop Yield ◽

Optical Sensors ◽

Precision Agriculture ◽

Multispectral Imaging ◽

Plant Stress ◽

Plant Diseases ◽

Added Value ◽

High Rate ◽

Future Research ◽

Atmospheric Conditions

In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field.

Download Full-text

Pemberdayaan Masyarakat Desa Terhadap Prospek Unggulan Jambu Gondangmanis Menjadi Desa Wisata (Di Desa Gondangmanis, Kec. Bandar KedungMulyo, Kab. Jombang)

JPM (Jurnal Pemberdayaan Masyarakat) ◽

10.21067/jpm.v4i2.3618 ◽

2019 ◽

Vol 4 (2) ◽

pp. 328-333

Author(s):

Eny Dyah Yuniwati ◽

M. Dullah ◽

M Cholil ◽

Yulianita Verlandes

Keyword(s):

Animal Husbandry ◽

Plant Diseases ◽

Regional Government ◽

Livestock Waste ◽

Department Of Agriculture ◽

Community Group ◽

Coordination Process ◽

Cultivation Techniques ◽

Very High

Gondangmanis guava production decreases every year, this is caused by pests and plant diseases, due to decreased soil quality, and soil fertility. In addition there are no good cultivation techniques so that Gondangmanis guava only grows conventionally. The purpose of this activity, for assistance, training and development of Gondangmanis guava picking tourism village. Implementation activities begin from April 2019 until August 2019, in Gondangmanis Village, kec. Bandar kedungmulyo, Jombang. The method used is a demonstration plot, and in-depth interviews. From the results of the assistance in this 3rd year, it can be concluded that there has been assistance, training and cultivation of Gondangmanis guava production. Community participation, especially those involved in guava development activities, and utilization of livestock waste is very high. Likewise, support from community leaders and village and district level officials was very supportive. Also pioneered the formation of tourism Gondangmanis guava village. The Guava Gondangmanis Community Group and the Tourism Awareness Group (POKDARWIS) have been formed. The role of the Department of Agriculture and Animal Husbandry and the Regional Government of Bappeda in Jombang Regency is also very high, because during the preparation and coordination process, it always receives attention from the leadership of the Regional Government, as evidenced by the formation of leading tourism in Jombang.

Download Full-text