Precision Agriculture Technologies for Management of Plant Diseases

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.

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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.

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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.

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Hyperspectral Sensors and Imaging Technologies in Phytopathology: State of the Art

Annual Review of Phytopathology ◽

10.1146/annurev-phyto-080417-050100 ◽

2018 ◽

Vol 56 (1) ◽

pp. 535-558 ◽

Cited By ~ 54

Author(s):

A.-K. Mahlein ◽

M.T. Kuska ◽

J. Behmann ◽

G. Polder ◽

A. Walter

Keyword(s):

Plant Pathogen ◽

Precision Agriculture ◽

Plant Protection ◽

Imaging Techniques ◽

Plant Diseases ◽

Plant Phenotyping ◽

Protection Measures ◽

Imaging Technologies ◽

Hyperspectral Sensors ◽

Plant Pathogen Interactions

Plant disease detection represents a tremendous challenge for research and practical applications. Visual assessment by human raters is time-consuming, expensive, and error prone. Disease rating and plant protection need new and innovative techniques to address forthcoming challenges and trends in agricultural production that require more precision than ever before. Within this context, hyperspectral sensors and imaging techniques—intrinsically tied to efficient data analysis approaches—have shown an enormous potential to provide new insights into plant-pathogen interactions and for the detection of plant diseases. This article provides an overview of hyperspectral sensors and imaging technologies for assessing compatible and incompatible plant-pathogen interactions. Within the progress of digital technologies, the vision, which is increasingly discussed in the society and industry, includes smart and intuitive solutions for assessing plant features in plant phenotyping or for making decisions on plant protection measures in the context of precision agriculture.

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Computational Intelligence for Pathological Issues in Precision Agriculture

Image Processing ◽

10.4018/978-1-4666-3994-2.ch043 ◽

2013 ◽

pp. 850-873

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.

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A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades

Remote Sensing ◽

10.3390/rs12193188 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3188 ◽

Cited By ~ 1

Author(s):

Ning Zhang ◽

Guijun Yang ◽

Yuchun Pan ◽

Xiaodong Yang ◽

Liping Chen ◽

...

Keyword(s):

Precision Agriculture ◽

Plant Disease ◽

Visual Assessment ◽

Plant Diseases ◽

Disease Detection ◽

Biotic And Abiotic Stresses ◽

Non Invasive ◽

Host Pathogen Interaction ◽

Typical Type ◽

Disease Analysis

The detection, quantification, diagnosis, and identification of plant diseases is particularly crucial for precision agriculture. Recently, traditional visual assessment technology has not been able to meet the needs of precision agricultural informatization development, and hyperspectral technology, as a typical type of non-invasive technology, has received increasing attention. On the basis of simply describing the types of pathogens and host–pathogen interaction processes, this review expounds the great advantages of hyperspectral technologies in plant disease detection. Then, in the process of describing the hyperspectral disease analysis steps, the articles, algorithms, and methods from disease detection to qualitative and quantitative evaluation are mainly summarizing. Additionally, according to the discussion of the current major problems in plant disease detection with hyperspectral technologies, we propose that different pathogens’ identification, biotic and abiotic stresses discrimination, plant disease early warning, and satellite-based hyperspectral technology are the primary challenges and pave the way for a targeted response.

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Plant Disease Detection by Imaging Sensors – Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping

Plant Disease ◽

10.1094/pdis-03-15-0340-fe ◽

2016 ◽

Vol 100 (2) ◽

pp. 241-251 ◽

Cited By ~ 291

Author(s):

Anne-Katrin Mahlein

Keyword(s):

Precision Agriculture ◽

Plant Disease ◽

Early Time ◽

Plant Diseases ◽

Plant Production ◽

Sensor Data ◽

Disease Assessment ◽

Plant Phenotyping ◽

Additional Information ◽

Molecular Approaches

Early and accurate detection and diagnosis of plant diseases are key factors in plant production and the reduction of both qualitative and quantitative losses in crop yield. Optical techniques, such as RGB imaging, multi- and hyperspectral sensors, thermography, or chlorophyll fluorescence, have proven their potential in automated, objective, and reproducible detection systems for the identification and quantification of plant diseases at early time points in epidemics. Recently, 3D scanning has also been added as an optical analysis that supplies additional information on crop plant vitality. Different platforms from proximal to remote sensing are available for multiscale monitoring of single crop organs or entire fields. Accurate and reliable detection of diseases is facilitated by highly sophisticated and innovative methods of data analysis that lead to new insights derived from sensor data for complex plant-pathogen systems. Nondestructive, sensor-based methods support and expand upon visual and/or molecular approaches to plant disease assessment. The most relevant areas of application of sensor-based analyses are precision agriculture and plant phenotyping.

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Canopy Index Evaluation for Precision Management in an Intensive Olive Orchard

Sustainability ◽

10.3390/su13158266 ◽

2021 ◽

Vol 13 (15) ◽

pp. 8266

Author(s):

Alberto Assirelli ◽

Elio Romano ◽

Carlo Bisaglia ◽

Enrico Maria Lodolini ◽

Davide Neri ◽

...

Keyword(s):

Precision Agriculture ◽

Ambient Air ◽

Plant Diseases ◽

Precision Management ◽

Practical Support ◽

Index Evaluation ◽

Leaf Level ◽

Olive Groves ◽

Cultivation Techniques ◽

Monitoring Information

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.

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Fuzzy C-Means (FCM) Clustering with Probabilistic Neural Network (PNN) Model for Detection and Classification of Rice Plant Diseases in Internet of Things-Cloud Centric Precision Agriculture

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2021.9400 ◽

2021 ◽

Vol 18 (4) ◽

pp. 1194-1200

Author(s):

P. Sindhu ◽

G. Indirani ◽

P. Dinadayalan

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Internet Of Things ◽

Precision Agriculture ◽

Rice Plant ◽

Probabilistic Neural Network ◽

Plant Diseases ◽

Brown Spot ◽

Fuzzy C Means ◽

Fcm Clustering

Presently, the field of Internet of Things (loT) has been employed in diverse applications like Smart Grid, Surveillance, Smart homes, and so on. Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop health. Recognition of diseases from the plant images is an active research topic which makes use of machine learning (ML) approaches. This paper introduces an effective rice plant disease identification and classification model to identify the type of disease from infected rice plants. The proposed method aims to detect three rice plant diseases such as Bacterial leaf blight, Brown spot, and Leaf smut. The proposed method involves a set of different processes namely image acquisition, preprocessing, segmentation, feature extraction and classification. At the earlier stage, IoT devices will be used to capture the image and stores it with a cloud server, which executes the classification process. In the cloud, the rice plant images under preprocessing to improvise the quality of the image. Then, fuzzy c-means (FCM) clustering method is utilized for the segmentation of disease portion from a leaf image. Afterwards, feature extraction takes place under three kinds namely color, shape, and texture. Finally, probabilistic neural network (PNN) is applied for multi-class classification. A detailed experimental analysis ensured the effective classification performance of the proposed method under all the test images applied.

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Chilli Plant Disease Detection and Classification using DenseNet CNN Approach

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.36171 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 4469-4477

Author(s):

Sheenu N V

Keyword(s):

Precision Agriculture ◽

Plant Disease ◽

Learning Algorithm ◽

Plant Diseases ◽

Disease Detection ◽

Deep Learning Algorithm ◽

Affected Plant ◽

Main Motive ◽

Important People ◽

Micro Organisms

To fulfil the food requirement and economic growth, farming plays a very important role. Thus Farmers are the most important people in the world. Be it the smallest or the largest country, Because of them only we are able to live on the planet. Precision agriculture is the new trending term in the field of technology whose main motive is to reduce the workload of the farmers and increase the productivity of the farms by using technologies. So the aim of this work is to detect the disease of the plant by classifying their leaves using deep learning algorithm. For this work chilli plants are considered, because of their economic importance. And there are various problems in chilli production due to the presence of various micro-organisms and pathogens and The plant disease detection can be done by observing the spot on the leaves of the affected plant. The method here adopting to detect plant diseases is image processing using Dense Net based Convolution neural network (CNN). CNN will be used for leaf image classification and will produce the good results with a good accuracy.

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