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.
The most recent advance in precision agriculture is the FarmBeats initiative driven by Microsoft AI @cite_15 , in which a variety of network-accessible sensor modalities, including soil water potential sensors and AUAVs, are arranged to provide automated and targeted water intervention. This methodology is powerful for tackling stresses due to underwatering, but is incapable of detecting the presence of pathogens, pests and nutrient deficiency, which express themselves phenotypically.
@cite_15
2605165679 1659349535 2107774608 2615516218
Data-driven techniques help boost agricultural productivity by increasing yields, reducing losses and cutting down input costs. However, these techniques have seen sparse adoption owing to high costs of manual data collection and limited connectivity solutions. In this paper, we present FarmBeats, an end-to-end IoT platform for agriculture that enables seamless data collection from various sensors, cameras and drones. FarmBeats's system design that explicitly accounts for weather-related power and Internet outages has enabled six month long deployments in two US farms. Addressing the challenges of feeding the burgeoning world population with limited resources requires innovation in sustainable, efficient farming. The practice of precision agriculture offers many benefits towards addressing these challenges, such as improved yield and efficient use of such resources as water, fertilizer and pesticides. We describe the design and development of a light-weight, multi-spectral 3D imaging device that can be used for automated monitoring in precision agriculture. The sensor suite consists of a laser range scanner, multi-spectral cameras, a thermal imaging camera, and navigational sensors. We present techniques to extract four key data products — plant morphology, canopy volume, leaf area index, and fruit counts — using the sensor suite. We demonstrate its use with two systems: multi-rotor micro aerial vehicles and on a human-carried, shoulder-mounted harness. We show results of field experiments conducted in collaboration with growers and agronomists in vineyards, apple orchards and orange groves. The Remote Sensing Technologies Center (RSTC) at Mississippi State University (MSU) is investigating the application of high-resolution imagery in the areas of precision agriculture, forest resource management, and environmental assessments of transportation systems. For precision agriculture, the focus of the research is to develop the expertise to utilize this imagery to facilitate management decisions at the farm level. Decision making at the farm level requires high spatial resolution imagery (on the order of 1 meter or less) in order to develop prescriptions for nutrient supplements, integrated pest management (insects, weeds), irrigation, and to serve as a harvesting aid. This degree of analysis of the plant and soil also requires that higher spectral resolution be available to ascertain the nuances in the target vegetation's reflectance and to be able to relate this to specific agricultural problems. In addition, decisions at the farm level must be made within a few days of onset of a problem. Therefore, revisit frequencies for precision agriculture are on the order of 10-14 days. The requirement for high spatial, spectral, and temporal resolution imagery creates special image processing and analysis challenges. These include-data management, geometric and radiometric corrections, ground control points and georeferencing, and data analysis via such approaches as data fusion. The purpose of this paper is twofold. First, to illustrate the diversity of high spatial, spectral, and temporal resolution imagery required for precision agriculture applications. Second, to offer the opportunity to other data fusion researchers to join the author of this paper in developing new data fusion techniques for high-resolution imagery. Abstract Precision agriculture (PA) utilizes tools and technologies to identify in-field soil and crop variability for improving farming practices and optimizing agronomic inputs. Traditionally, optical remote sensing (RS) that utilizes visible light and infrared regions of the electromagnetic spectrum has been used as an integral part of PA for crop and soil monitoring. Optical RS, however, is slow in differentiating stress levels in crops until visual symptoms become noticeable. Surface temperature is considered to be a rapid response variable that can indicate crop stresses prior to their visual symptoms. By measuring estimates of surface temperature, thermal RS has been found to be a promising tool for PA. Compared to optical RS, applications of thermal RS for PA have been limited. Until recently (i.e., before the advancement of low cost RS platforms such as unmanned aerial systems (UAVs)), the availability of high resolution thermal images was limited due to high acquisition costs. Given recent developments in UAVs, thermal images with high spatial and temporal resolutions have become available at a low cost, which has increased opportunities to understand in-field variability of crop and soil conditions useful for various agronomic decision-making. Before thermal RS is adopted as a routine tool for crop and environmental monitoring, there is a need to understand its current and potential applications as well as issues and concerns. This review focuses on current and potential applications of thermal RS in PA as well as some concerns relating to its application. The application areas of thermal RS in agriculture discussed here include irrigation scheduling, drought monitoring, crop disease detection, and mapping of soil properties, residues and tillage, field tiles, and crop maturity and yield. Some of the issues related to its application include spatial and temporal resolution, atmospheric conditions, and crop growth stages.