We propose a generalized decision-theoretic system for a heterogeneous team of autonomous agents who are tasked with online identification of phenotypically expressed stress in crop fields.. This system employs four distinct types of agents, specific to four available sensor modalities: satellites (Layer 3), uninhabited aerial vehicles (L2), uninhabited ground vehicles (L1), and static ground-level sensors (L0). Layers 3, 2, and 1 are tasked with performing image processing at the available resolution of the sensor modality and, along with data generated by layer 0 sensors, identify erroneous differences that arise over time. Our goal is to limit the use of the more computationally and temporally expensive subsequent layers. Therefore, from layer 3 to 1, each layer only investigates areas that previous layers have identified as potentially afflicted by stress. We introduce a reinforcement learning technique based on Perkins' Monte Carlo Exploring Starts for a generalized Markovian model for each layer's decision problem, and label the system the Agricultural Distributed Decision Framework (ADDF). As our domain is real-world and online, we illustrate implementations of the two major components of our system: a clustering-based image processing methodology and a two-layer POMDP implementation.
Concerning the goal of disease identification and intervention, the wide array of contemporary efforts leverage phenotypic expressions of stress largely via thermal detection @cite_12 and are often specific to the expression from a specific disease @cite_4 . What remains is a generalized model that encompasses the variety of stresses in a model-free way. That is, instead of seeking a particular expression, learn the correlation between erroneous growth patterns, leaf and fruit necrosis or chlorosis, leaf spots, leaf striations and wilting (as caused by stress) and the available image and environmental data.
@cite_4 @cite_12
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Plant Diseases and Pests are a major challenge in the agriculture sector. An accurate and a faster detection of diseases and pests in plants could help to develop an early treatment technique while substantially reducing economic losses. Recent developments in Deep Neural Networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. In this paper, we present a deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions. Our goal is to find the more suitable deep-learning architecture for our task. Therefore, we consider three main families of detectors: Faster Region-based Convolutional Neural Network (Faster R-CNN), Region-based Fully Convolutional Network (R-FCN), and Single Shot Multibox Detector (SSD), which for the purpose of this work are called “deep learning meta-architectures”. We combine each of these meta-architectures with “deep feature extractors” such as VGG net and Residual Network (ResNet). We demonstrate the performance of deep meta-architectures and feature extractors, and additionally propose a method for local and global class annotation and data augmentation to increase the accuracy and reduce the number of false positives during training. We train and test our systems end-to-end on our large Tomato Diseases and Pests Dataset, which contains challenging images with diseases and pests, including several inter- and extra-class variations, such as infection status and location in the plant. Experimental results show that our proposed system can effectively recognize nine different types of diseases and pests, with the ability to deal with complex scenarios from a plant’s surrounding area. The use of high-throughput phenotyping systems and non-destructive imaging is widely regarded as a key technology allowing scientists and breeders to develop crops with the ability to perform well under diverse environmental conditions. However, many of these phenotyping studies have been optimized using the model plant Arabidopsis thaliana. In this study, The Plant Accelerator® at The University of Adelaide, Australia, was used to investigate the growth and phenotypic response of the important cereal crop, Sorghum bicolor L. Moench and related hybrids to water-limited conditions and different levels of fertilizer. Imaging in different spectral ranges was used to monitor plant composition, chlorophyll, and moisture content. Phenotypic image analysis accurately measured plant biomass. The data set obtained enabled the responses of the different sorghum varieties to the experimental treatments to be differentiated and modelled. Plant architectural instead of architecture elements were determined using imaging and found to correlate with an improved tolerance to stress, for example diurnal leaf curling and leaf area index. Analysis of colour images revealed that leaf ‘greenness’ correlated with foliar nitrogen and chlorophyll, while near infrared reflectance (NIR) analysis was a good predictor of water content and leaf thickness, and correlated with plant moisture content. It is shown that imaging sorghum using a high-throughput system can accurately identify and differentiate between growth and specific phenotypic traits. R scripts for robust, parsimonious models are provided to allow other users of phenomic imaging systems to extract useful data readily, and thus relieve a bottleneck in phenotypic screening of multiple genotypes of key crop plants. article i nfo The remote detection of water stress in a citrus orchard was investigated using leaf-level measurements of chlorophyll fluorescence and Photochemical Reflectance Index (PRI) data, seasonal time-series of crown tem- perature and PRI, and high-resolution airborne imagery. The work was conducted in an orchard where a reg- ulated deficit irrigation (RDI) experiment generated a gradient in water stress levels. Stomatal conductance (Gs) and water potential (Ψ) were measured over the season on each treatment block. The airborne data consisted on thermal and hyperspectral imagery acquired at the time of maximum stress differences among treatments, prior to the re-watering phase, using a miniaturized thermal camera and a micro-hyperspectral imager on board an unmanned aerial vehicle (UAV). The hyperspectral imagery was acquired at 40 cm resolution and 260 spectral bands in the 400-885 nm spectral range at 6.4 nm full width at half maximum (FWHM) spectral resolution and 1.85 nmsampling interval,enablingthe identificationof pure crownsfor extractingradiance andreflectance hyperspectral spectra from each tree. The FluorMOD model was used to investigate the retrieval of chlorophyll fluorescence by applying the Fraunhofer Line Depth (FLD) principle using three spectral bands (FLD3), which demonstrated that fluorescence retrievalwas feasible with the configuration of the UAV micro-hyperspectral in- strument flown over the orchard. Results demonstrated the link between seasonal PRI and crown temperature acquired from instrumented trees and field measurements of stomatal conductance and water potential. The sensitivity of PRI and Tc-Ta time-series to water stress levels demonstrated a time delay of PRI vs Tc-Ta during the recovery phase after re-watering started. At the time of the maximum stress difference among treatment blocks, the airborneimagery acquired fromthe UAV platform demonstrated that the crown temperature yielded the best coefficient of determination for Gs (r 2 Repeated exposures to psychological stress can lead to or worsen diseases of slow accumulation such as heart diseases and cancer. The main challenge in addressing the growing epidemic of stress is a lack of robust methods to measure a person's exposure to stress in the natural environment. Periodic self-reports collect only subjective aspects, often miss stress episodes, and impose significant burden on subjects. Physiological sensors provide objective and continuous measures of stress response, but exhibit wide between-person differences and are easily confounded by daily activities (e.g., speaking, physical movements, coffee intake, etc.). In this paper, we propose, train, and test two models for continuous prediction of stress from physiological measurements captured by unobtrusive, wearable sensors. The first model is a physiological classifier that predicts whether changes in physiology represent stress. Since the effect of stress may persist in the mind longer than its acute effect on physiology, we propose a perceived stress model to predict perception of stress. It uses the output of the physiological classifier to model the accumulation and gradual decay of stress in the mind. To account for wide between-person differences, both models self-calibrate to each subject. Both models were trained using data collected from 21 subjects in a lab study, where they were exposed to cognitive, physical, and social stressors representative of that experienced in the natural environment. Our physiological classifier achieves 90 accuracy and our perceived stress model achieves a median correlation of 0.72 with self-reported rating. We also evaluate the perceived stress model on data collected from 17 participants in a two-day field study, and find that the average rating of stress obtained from our model has a correlation of 0.71 with that obtained from periodic self-reports.