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Integrated DSS for entire process of crop production

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The system has the following highlights in addition to the aforementioned DSS features:

Variable pesticide and fertilizer application. Experts are involved in developing prescription maps based on optimal results and cost control. Then targeted spraying operations are conducted.

Machine learning modules are added to DSS where manual intervention is required, such as pest control, operation scheduling. For the added machine learning module, the longer the DSS is used the more fully trained, in the following time human intervention can be gradually removed.

Decision-making of agricultural machinery operation trajectory for optimal efficiency. Precise location information and operation status information of farm machinery are used to account for operation area, operation time and operation efficiency, then formulate reasonable farm machinery operation path based on the accounting result.

Autopilot operation. According to the planning path of farm machinery operation, combined with high-precision position data and farm machinery operation data, it is possible to use radio remote control equipment and self-provided program to operate the unmanned vehicle to achieve automatic driving.

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lack of multispectral, thermal imaging,

lack of machine learning modules,

lack of automated fertilization,

lack of determination of crop growth status, unwanted weeds,  pests and diseases.

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DSS based on LoRaWAN for crop yield management

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a smart agriculture DSS based on a novel wireless communication technology LoRaWAN for optimizing crop yield. This DSS has multiple environmental status data inputs including air temperature and humidity, soil conductivity, NPK content, pH, soil moisture, and air quality. It can be used for automatic crop irrigation, water oversupply warning, nutrient deficiency & oversupply warning. It uses LoRaWAN, a low-cost open-source wireless technology with long-range communication capability.

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soil quality will gradually deteriorate due to fertilizers,

high initial investment,

fierce market competition.

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Greenhouse DSS based on IoT and machine learning

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This intelligent Greenhouse DSS that integrates IoT and machine learning. It can be used to perform real-time monitoring of environmental variables and can automatically perform different tasks  by Central actuator manager , it also provides an interface to communicate with experts and workers to engage them when necessary. It supports AI decision making in addition to using rules to make decisions. For example: image recognition to determine if plants are growing in a good state or identify if there are weeds, predict abnormal conditions that may arise in the nearest future based on environmental variables and given recommendations

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lack of hyperspectral technology,

lack of machine learning modules,

lack of automated execution units

lack of experts, staff online communication and auxiliary support interfaces

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optical and photonic technologies based DSS for health care

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an novel optical and photonic technologies based DSS to measure crop health so that timely and effective measures can be taken to improve overall crop yield and quality.

This DSS has multiple complex data input sources, including climate change information, satellite imagery, fusion databases, real-time data from sensor environments, multispectral data, and infrared thermal imaging data.
The data mining engine handles large-scale complex input data that can be used in agriculture for monitoring pests and diseases, soil fertility, crop growth, etc. For example, thermal imaging monitors crop hydrogen content threshold and issues warnings, multispectral assessment of fruit ripeness, UV detection of pests and diseases.

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lack of fertilization process decision,

lack of data such as multispectral, thermal imaging, weather forecast, satellite, etc.,

lack of machine learning module,

lack of judgment on plant growth status, redundant weeds as well as pests and diseases,

need for expert participation to set valves manually according to different plant types,

high GPRS power consumption.

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a DSS for irrigation management based on sensor collected data. This DSS contains weather prediction, real-time sensor data, and water balance model to continuously monitor the crop growing environment and to implement remote control of irrigation.

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lack of leaf images for analysis of other diseases,

high power consumption for 3G / GPRS wireless communication,

lack of automated execution unit,

low cost-effective of IoT infrastructure investment and return, irrigation decisions should also be added to the original infrastructure.

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This DSS can acquire real-time ambient temperature and humidity to assess the risk of potato late blight disease.

When the monitored ambient temperature and humidity reach the risk threshold.  the DSS sends treatment notifications to relevant staff via SMS.

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119 journals are indexed,  52 articles matching research objectives are retrieved manually.
With the inclusion and exclusion criteria "Language English", "Time line Between 2012 and 2022", "Literature type journal articles and conference papers", a sum of 16 articles were screened.
Abstracts and the main contents of all the articles were examined thoroughly for the analysis and purification of the articles to ensure the quality. After that 8 articles with practical DSS case were analyzed and extracted.  A study conducted to discusses the structure, characteristics, and limitations of these DSS design and application.

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The purpose of this paper is to provide an overview of the application of DSS in the field of precision agriculture. It describes and analyzes the development of agricultural DSS from rule-driven to data-driven, from handling simple input to complex inputs, show that the use of modern information technologies such as multi-sensor fusion, Internet of Things, data mining, cloud computing, RTK high-precision composite positioning, unmanned aerial vehicles, machine learning, and graphical recognition can promote precision agriculture in the direction of more refined, more intelligent, and more intensive development.

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Based on the summarized advantages and disadvantages of DSS, we propose a new integrated DSS system applied in precision agriculture, which includes 4 parts: automatic irrigation, health management, prescription map, and automatic operation, basically covering the whole process of crop production.

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Each group member read these articles to obtain and understand the main ideas of the articles, and extracted system illustrations from them separately,then make critical comments through group discussion. For example, the advantages as well as the limitations of DSS system.

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(PRISMA) are employed in the current research  including resources, inclusion and exclusion criteria, systematic review process, data abstraction, analysis
We use search key words  “precision” or "agriculture" or "DSS" or "crop" or "decision support system" on scopus database,

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DSS plays an active role in healthcare, disaster management, airlines, Currently, many countries are facing food shortage, so we choose the theme of DSS in agriculture and hope to contribute.
Agricultural decision support systems are used in different areas of agriculture, mainly to improve economic benefits, enhance crop quality, and reduce environmental risks.  DSS can be an important tool for achieving more sustainable agricultural practices.Precision agriculture is based mainly on technology and data collected from the crops,  Precision agriculture gives more power to DSS, and combiles agriculture and high-tech technology in order to maintain sustainable agricultural development and improve agricultural productivity without environmental pollution and high costs. What's more, recent IoT and Autopilot technology can even save labor costs and make crops growing easier.

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Read and understand the requirements of the assignment and choose a topic for group discussion. Identify as the application of DSS in the field of precision agriculture.