Crop Stress Detection and Classification Using Hyperspectral Remote Sensing

Agricultural production has observed many changes in technology over the last 20 years. Producers are able to utilize technologies such as site-specific applicators and remotely sensed data to assist with decision making for best management practices which can improve crop production and provide protection to the environment. It is known that plant stress can interfere with photosynthetic reactions within the plant and/or the physical structure of the plant. Common types of stress associated with agricultural crops include herbicide induced stress, nutrient stress, and drought stress from lack of water. Herbicide induced crop stress is not a new problem. However, with increased acreage being planting in varieties/hybrids that contain herbicide resistant traits, herbicide injury to non-target crops will continue to be problematic for producers. With rapid adoption of herbicide-tolerant cropping systems, it is likely that herbicide induced stress will continue to be a major concern. To date, commercially available herbicide-tolerant varieties/hybrids contain traits which allow herbicides like glyphosate and glufosinate-ammonium to be applied as a broadcast application during the growing season. Both glyphosate and glufosinate-ammonium are broad spectrum herbicides which have activity on a large number of plant species, including major crops like non-transgenic soybean, corn, and cotton. Therefore, it is possible for crop stress from herbicide applications to occur in neighboring fields that contain susceptible crop varieties/hybrids. Nutrient and moisture stress as well as stress caused by herbicide applications can interact to influence yields in agricultural fields. If remotely sensed data can be used to accurately identify specific levels of crop stress, it is possible that producers can use this information to better assist them in crop management to maximize yields and protect their investments. This research was conducted to evaluate classification of specific crop stresses utilizing hyperspectral remote sensing

Crop Stress Detection and Classification Using Hyperspectral Remote Sensing

Agricultural production has observed many changes in technology over the last 20 years. Producers are able to utilize technologies such as site-specific applicators and remotely sensed data to assist with decision making for best management practices which can improve crop production and provide protection to the environment. It is known that plant stress can interfere with photosynthetic reactions within the plant and/or the physical structure of the plant. Common types of stress associated with agricultural crops include herbicide induced stress, nutrient stress, and drought stress from lack of water. Herbicide induced crop stress is not a new problem. However, with increased acreage being planting in varieties/hybrids that contain herbicide resistant traits, herbicide injury to non-target crops will continue to be problematic for producers. With rapid adoption of herbicide-tolerant cropping systems, it is likely that herbicide induced stress will continue to be a major concern. To date, commercially available herbicide-tolerant varieties/hybrids contain traits which allow herbicides like glyphosate and glufosinate-ammonium to be applied as a broadcast application during the growing season. Both glyphosate and glufosinate-ammonium are broad spectrum herbicides which have activity on a large number of plant species, including major crops like non-transgenic soybean, corn, and cotton. Therefore, it is possible for crop stress from herbicide applications to occur in neighboring fields that contain susceptible crop varieties/hybrids. Nutrient and moisture stress as well as stress caused by herbicide applications can interact to influence yields in agricultural fields. If remotely sensed data can be used to accurately identify specific levels of crop stress, it is possible that producers can use this information to better assist them in crop management to maximize yields and protect their investments. This research was conducted to evaluate classification of specific crop stresses utilizing hyperspectral remote sensing

Remote sensing bio-control damage on aquatic invasive alien plant species

Aquatic Invasive Alien Plant (AIAP) species are a major threat to freshwater ecosystems, placing great strain on South Africa’s limited water resources. Bio-control programmes have been initiated in an effort to mitigate the negative environmental impacts associated with their presence in non-native areas. Remote sensing can be used as an effective tool to detect, map and monitor bio-control damage on AIAP species. This paper  reconciles previous and current research concerning the application of remote sensing to detect and map bio-control damage on AIAP species. Initially, the spectral characteristics of bio-control damage are  described. Thereafter, the potential of remote sensing chlorophyll content and chlorophyll fluorescence as  pre-visual indicators of bio-control damage are reviewed and synthesised. The utility of multispectral and  hyperspectral sensors for mapping different severities of bio-control damage are also discussed. Popular  machine learning algorithms that offer operational potential to classify bio-control damage are proposed. This paper concludes with the challenges of remote sensing bio-control damage as well as proposes  recommendations to guide future research to successfully detect and map bio-control damage on AIAP  species

Past and future of plant stress detection: an overview from remote sensing to Positron Emission Tomography

Plant stress detection is considered one of the most critical areas for the improvement of crop yield in the compelling worldwide scenario, dictated by both the climate change and the geopolitical consequences of the Covid-19 epidemics. A complicated interconnection of biotic and abiotic stressors affect plant growth, including water, salt, temperature, light exposure, nutrients availability, agrochemicals, air and soil pollutants, pests and diseases. In facing this extended panorama, the technology choice is manifold. On the one hand, quantitative methods, such as metabolomics, provide very sensitive indicators of most of the stressors, with the drawback of a disruptive approach, which prevents follow up and dynamical studies. On the other hand qualitative methods, such as fluorescence, thermography and VIS/NIR reflectance, provide a non-disruptive view of the action of the stressors in plants, even across large fields, with the drawback of a poor accuracy. When looking at the spatial scale, the effect of stress may imply modifications from DNA level (nanometers) up to cell (micrometers), full plant (millimeters to meters) and entire field (kilometers). While quantitative techniques are sensitive to the smallest scales, only qualitative approaches can be used for the larger ones. Emerging technologies from nuclear and medical physics, such as computed tomography, magnetic resonance imaging and positron emission tomography, are expected to bridge the gap of quantitative non disruptive morphologic and functional measurements at larger scale. In this review we analyze the landscape of the different technologies nowadays available, showing the benefits of each approach in plant stress detection, with a particular focus on the gaps, which will be filled in the nearby future by the emerging nuclear physics approaches to agriculture

Proximal and remote sensing for early detection and assessment of herbicide drift damage on cotton crops

The herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) is one of the most successful selective herbicides used in agriculture to control broadleaf weeds. Unfortunately, cotton crops are highly susceptible to 2,4-D, and they are often damaged by the offtarget movement of the active ingredient when sprayed as a herbicide on surrounding farms. This action, referred to as herbicide drift, affects the cotton industry every season, causing losses of millions of dollars. Although the economic repercussions on the industry are high, the traditional (visual) assessment of damage is often imprecise and inaccurate. Crop sensing tools can offer alternative and reliable methods to overcome the typical limitations of visual assessments by providing accurate estimations of crop performance. The aim of this research project was to assess the capabilities of crop sensing techniques of providing spatial and quantitative information of cotton yield after being affected by 2,4-D herbicide drift. This information is valuable to agronomic planners for evaluating their crop management strategies in order to maximise cotton production while safeguarding the environment in the affected area. The research area was located in a cotton-growing region in Jondaryan, Queensland, Australia. Two study cases and three remote/proximal sensing approaches were tested. The first study case consisted of controlled doses to simulate accidental exposure to 2,4-D, where three doses (D) and three timing of exposures (S) were examined at four different dates after the exposure (DAE): 2, 7, 14 and 28 DAE. In this case, a hyperspectral sensor and a terrestrial laser scanner (TLS) were evaluated to assess their ability to predict yield loss, dose and canopy structure variability. The second case examined the potential capabilities of satellite imagery for yield loss assessment in an uncontrolled exposure of cotton crops to 2,4-D. For this case, several multispectral (Landsat 8 Operational Land Imager - OLI) images were analysed and a comprehensive approach was developed to overcome the potential limitation of moderate resolution imagery at the field level. The controlled case revealed that hyperspectral data can be used to predict yield loss with high accuracy (R2 = 0.88) regardless of the timing of exposure and dose, and that 7 DAE and 28 DAE (RMSECV: 2.6 bales/ha; R2 = 0.88 and RMSECV: 3.2 bales/ha; R2 = 0.84, respectively) were the best times for data collection purposes. The main difference in the model performance between the best (7 DAE) and the worst (14 DAE) prediction model was the inclusion of the NIR range, as the 14 DAE was the only model with no significant wavelengths in this range. Through this case, it was possible to better understand how the internal changes of the contaminated leaves, that is photosynthesis, stomatal conductance and hormone contents, influenced their spectral response and the lint quality of the cotton. Most of the variables analysed in this study manifested a significant relationship with hyperspectral data ( value 70%) were obtained regardless of the method, D or S. However, the timing of exposure (S) resulted in being a determinant to improve the classification accuracy to more than 90%. The analysis of laser scanner-derived data provided accurate information about the canopy height and canopy volume that could be strongly correlated (r > 0.88) with yield at different times of assessment (2 DAE, 7 DAE and 14 DAE). High R2 (> 0.90) between measured and estimated canopy height validates the height values estimated from the TLS-derived data. Furthermore, the weak relationship (R2 =0.39, value > 0.05) between point density and estimated canopy volume provided an insight that the approach implemented to estimate cotton canopy height and volume overcame the reported limitations of terrestrial laser scanners in the field. The uncontrolled case (i.e. Landsat 8 imagery) tested six different dates for optimal data collection purposes. The results demonstrated that traditional vegetation indices (VI) and individual multispectral bands were incapable of predicting yield in neither affected nor unaffected cotton areas (R2 < 0.27). However, PLS-R models optimized the information provided by the multispectral bands. As a result, the R2 increased, in some cases, by more than 60%. From the PLS- model results, it was determined that one week after the exposure was the best time for the prediction of yield in affected areas (RMSEP = 1.19 bales/ha and R2 = 0.60). Satellite imagery could be then implemented to support targeted monitoring programs in 2,4-D-injured areas. The technologies implemented in this study were proven to be reliable for damage assessment after an accidental spray drift by accurately predicting yield and dose and also by estimating canopy structure variables strongly correlated with yield in 2,4-Daffected areas. These comprehensive analytical approaches also provided information on temporal windows for optimal data collection after an incident, and also on less-recommended dates for the same purpose. These methods indicated an optimal window between seven and 14 days, or more than 28 days after the exposure, for the prediction of damage. However, as soon as two days after the cotton plant was exposed, hyperspectral measurements and TLS-derived data recorded significant differences in comparison with unaffected control plants

Image Analysis and Machine Learning in Agricultural Research

Agricultural research has been a focus for academia and industry to improve human well-being. Given the challenges in water scarcity, global warming, and increased prices of fertilizer, and fossil fuel, improving the efficiency of agricultural research has become even more critical. Data collection by humans presents several challenges including: 1) the subjectiveness and reproducibility when doing the visual evaluation, 2) safety when dealing with high toxicity chemicals or severe weather events, 3) mistakes cannot be avoided, and 4) low efficiency and speed. Image analysis and machine learning are more versatile and advantageous in evaluating different plant characteristics, and this could help with agricultural data collection. In the first chapter, information related to different types of imaging (e.g., RGB, multi/hyperspectral, and thermal imaging) was explored in detail for its advantages in different agriculture applications. The process of image analysis demonstrated how target features were extracted for analysis including shape, edge, texture, and color. After acquiring features information, machine learning can be used to automatically detect or predict features of interest such as disease severity. In the second chapter, case studies of different agricultural applications were demonstrated including: 1) leaf damage symptoms, 2) stress evaluation, 3) plant growth evaluation, 4) stand/insect counting, and 5) evaluation for produce quality. Case studies showed that the use of image analysis is often more advantageous than visual rating. Advantages of image analysis include increased objectivity, speed, and more reproducibly reliable results. In the third chapter, machine learning was explored using romaine lettuce images from RD4AG to automatically grade for bolting and compactness (two of the important parameters for lettuce quality). Although the accuracy is at 68.4 and 66.6% respectively, a much larger data base and many improvements are needed to increase the model accuracy and reliability. With the advancement in cameras, computers with high computing power, and the development of different algorithms, image analysis and machine learning have the potential to replace part of the labor and improve the current data collection procedure in agricultural research. Advisor: Gary L. Hei

Detection, identification, and quantification of fungal diseases of sugar beet leaves using imaging and non-imaging hyperspectral techniques

Plant diseases influence the optical properties of plants in different ways. Depending on the host pathogen system and disease specific symptoms, different regions of the reflectance spectrum are affected, resulting in specific spectral signatures of diseased plants. The aim of this study was to examine the potential of hyperspectral imaging and non-imaging sensor systems for the detection, differentiation, and quantification of plant diseases. Reflectance spectra of sugar beet leaves infected with the fungal pathogens Cercospora beticola, Erysiphe betae, and Uromyces betae causing Cercospora leaf spot, powdery mildew, and sugar beet rust, respectively, were recorded repeatedly during pathogenesis. Hyperspectral data were analyzed using various methods of data and image analysis and were compared to ground truth data. Several approaches with different sensors on the measuring scales leaf, canopy, and field have been tested and compared. Much attention was paid on the effect of spectral, spatial, and temporal resolution of hyperspectral sensors on disease recording. Another focus of this study was the description of spectral characteristics of disease specific symptoms. Therefore, different data analysis methods have been applied to gain a maximum of information from spectral signatures. Spectral reflectance of sugar beet was affected by each disease in a characteristic way, resulting in disease specific signatures. Reflectance differences, sensitivity, and best correlating spectral bands differed depending on the disease and the developmental stage of the diseases. Compared to non-imaging sensors, the hyperspectral imaging sensor gave extra information related to spatial resolution. The preciseness in detecting pixel-wise spatial and temporal differences was on a high level. Besides characterization of diseased leaves also the assessment of pure disease endmembers as well as of different regions of typical symptoms was realized. Spectral vegetation indices (SVIs) related to physiological parameters were calculated and correlated to the severity of diseases. The SVIs differed in their sensitivity to the different diseases. Combining the information from multiple SVIs in an automatic classification method with Support Vector Machines, high sensitivity and specificity for the detection and differentiation of diseased leaves was reached in an early stage. In addition to the detection and identification, the quantification of diseases was possible with high accuracy by SVIs and Spectral Angle Mapper classification, calculated from hyperspectral images. Knowledge from measurements under controlled condition was carried over to the field scale. Early detection and monitoring of Cercospora leaf spot and powdery mildew was facilitated. The results of this study contribute to a better understanding of plant optical properties during disease development. Methods will further be applicable in precision crop protection, to realize the detection, differentiation, and quantification of plant diseases in early stages.Nachweis, Identifizierung und Quantifizierung pilzlicher Blattkrankheiten der Zuckerrübe mit abbildenden und nicht-abbildenden hyperspektralen Sensoren Pflanzenkrankheiten wirken sich auf die optischen Eigenschaften von Pflanzen in unterschiedlicher Weise aus. Verschiedene Bereiche des Reflektionsspektrums werden in Abhängigkeit von Wirt-Pathogen System und krankheitsspezifischen Symptomen beeinflusst. Hyperspektrale, nicht-invasive Sensoren bieten die Möglichkeit, optische Veränderungen zu einem frühen Zeitpunkt der Krankheitsentwicklung zu detektieren. Ziel dieser Arbeit war es, das Potential hyperspektraler abbildender und nicht abbildender Sensoren für die Erkennung, Identifizierung und Quantifizierung von Pflanzenkrankheiten zu beurteilen. Zuckerrübenblätter wurden mit den pilzlichen Erregern Cercospora beticola, Erysiphe betae bzw. Uromyces betae inokuliert und die Auswirkungen der Entwicklung von Cercospora Blattflecken, Echtem Mehltau bzw. Rübenrost auf die Reflektionseigenschaften erfasst und mit optischen Bonituren verglichen. Auf den Skalenebenen Blatt, Bestand und Feld wurden Messansätze mit unterschiedlichen Sensoren verglichen. Besonders berücksichtigt wurden hierbei Anforderungen an die spektrale, räumliche und zeitliche Auflösung der Sensoren. Ein weiterer Schwerpunkt lag auf der Beschreibung der spektralen Eigenschaften von charakteristischen Symptomen. Verschiedene Auswerteverfahren wurden mit dem Ziel angewendet, einen maximalen Informationsgehalt aus spektralen Signaturen zu gewinnen. Jede Krankheit beeinflusste die spektrale Reflektion von Zuckerrübenblättern auf charakteristische Weise. Differenz der Reflektion, Sensitivität sowie Korrelation der spektralen Bänder zur Befallsstärke variierten in Abhängigkeit von den Krankheiten. Eine höhere Präzision durch die pixelweise Erfassung räumlicher und zeitlicher Unterschiede von befallenem und gesundem Gewebe konnte durch abbildende Sensoren erreicht werden. Spektrale Vegetationsindizes (SVIs), mit Bezug zu pflanzenphysiologischen Parametern wurden aus den Hyperspektraldaten errechnet und mit der Befallsstärke korreliert. Die SVIs unterschieden sich in ihrer Sensitivität gegenüber den drei Krankheiten. Durch den Einsatz von maschinellem Lernen wurde die kombinierte Information der errechneten Vegetationsindizes für eine automatische Klassifizierung genutzt. Eine hohe Sensitivität sowie eine hohe Spezifität bezüglich der Erkennung und Differenzierung von Krankheiten wurden erreicht. Eine Quantifizierung der Krankheiten war neben der Detektion und Identifizierung mittels SVIs bzw. Klassifizierung mit Spektral Angle Mapper an hyperspektralen Bilddaten möglich. Die Ergebnisse dieser Arbeit tragen zu einem besseren Verständnis der optischen Eigenschaften von Pflanzen unter Pathogeneinfluss bei. Die untersuchten Methoden bieten die Möglichkeit in Anwendungen des Präzisionspflanzenschutzes implementiert zu werden, um eine frühzeitige Erkennung, Differenzierung und Quantifizierung von Pflanzenkrankheiten zu ermöglichen

Drones: Innovative Technology for Use in Precision Pest Management.

Arthropod pest outbreaks are unpredictable and not uniformly distributed within fields. Early outbreak detection and treatment application are inherent to effective pest management, allowing management decisions to be implemented before pests are well-established and crop losses accrue. Pest monitoring is time-consuming and may be hampered by lack of reliable or cost-effective sampling techniques. Thus, we argue that an important research challenge associated with enhanced sustainability of pest management in modern agriculture is developing and promoting improved crop monitoring procedures. Biotic stress, such as herbivory by arthropod pests, elicits physiological defense responses in plants, leading to changes in leaf reflectance. Advanced imaging technologies can detect such changes, and can, therefore, be used as noninvasive crop monitoring methods. Furthermore, novel methods of treatment precision application are required. Both sensing and actuation technologies can be mounted on equipment moving through fields (e.g., irrigation equipment), on (un)manned driving vehicles, and on small drones. In this review, we focus specifically on use of small unmanned aerial robots, or small drones, in agricultural systems. Acquired and processed canopy reflectance data obtained with sensing drones could potentially be transmitted as a digital map to guide a second type of drone, actuation drones, to deliver solutions to the identified pest hotspots, such as precision releases of natural enemies and/or precision-sprays of pesticides. We emphasize how sustainable pest management in 21st-century agriculture will depend heavily on novel technologies, and how this trend will lead to a growing need for multi-disciplinary research collaborations between agronomists, ecologists, software programmers, and engineers

Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images

The global demand for fossil energy is triggering oil exploration and production projects in remote areas of the world. During the last few decades hydrocarbon production has caused pollution in the Amazon forest inflicting considerable environmental impact. Until now it is not clear how hydrocarbon pollution affects the health of the tropical forest flora. During a field campaign in polluted and pristine forest, more than 1100 leaf samples were collected and analysed for biophysical and biochemical parameters. The results revealed that tropical forests exposed to hydrocarbon pollution show reduced levels of chlorophyll content, higher levels of foliar water content and leaf structural changes. In order to map this impact over wider geographical areas, vegetation indices were applied to hyperspectral Hyperion satellite imagery. Three vegetation indices (SR, NDVI and NDVI705) were found to be the most appropriate indices to detect the effects of petroleum pollution in the Amazon forest