Agricultural Decision Support Systems Research Articles

Agricultural Decision Support System

Abstract: In India, agriculture stands as the backbone of the economy, yet farmers encounter persistent challenges rooted in conventional farming methods that result in suboptimal yields and environmental degradation. To address these issues, a transformative shift towards precision agriculture is imperative. Our Agricultural Decision Support System (ADSS) represents a pioneering initiative harnessing advanced machine learning algorithms to confront these challenges head-on. By scrutinizing soil characteristics alongside critical weather parameters encompassing temperature, humidity, pH levels, and precipitation, our system furnishes tailored recommendations for crop selection and optimal fertilizer application. Through the implementation of our innovative solution, farmers stand poised to diversify their crop cultivation, augment yields, and pave the way for sustainable agricultural practices. The integration of our system offers farmers the opportunity to explore new crop varieties, potentially leading to heightened profitability and the mitigation of soil pollution concerns.

Integrated Agricultural Decision Support System for Precision Farming: A Multi-Faceted Approach

Abstract: As the global population continues to expand, ensuring food security becomes an increasingly critical challenge. Precision farming, leveraging advanced technologies, offers a promising solution to optimize agricultural practices and maximize crop yield. In this research paper, we present a comprehensive approach to agricultural management through leaf disease detection. By employing a deep learning approach utilizing ResNet-30 for accurate and efficient disease detection, the system can promptly identify and classify diseases, facilitating timely intervention to mitigate potential crop losses. The integration of this module results in a holistic decision support system that empowers farmers with actionable insights. In conclusion, this research contributes to the advancement of precision agriculture by offering a synergistic approach to leaf disease detection.

Multiclass classification of diseased grape leaf identification using deep convolutional neural network(DCNN) classifier

The cultivation of grapes encounters various challenges, such as the presence of pests and diseases, which have the potential to considerably diminish agricultural productivity. Plant diseases pose a significant impediment, resulting in diminished agricultural productivity and economic setbacks, thereby affecting the quality of crop yields. Hence, the precise and timely identification of plant diseases holds significant importance. This study employs a Convolutional neural network (CNN) with and without data augmentation, in addition to a DCNN Classifier model based on VGG16, to classify grape leaf diseases. A publicly available dataset is utilized for the purpose of investigating diseases affecting grape leaves. The DCNN Classifier Model successfully utilizes the strengths of the VGG16 model and modifies it by incorporating supplementary layers to enhance its performance and ability to generalize. Systematic evaluation of metrics, such as accuracy and F1-score, is performed. With training and test accuracy rates of 99.18 and 99.06%, respectively, the DCNN Classifier model does a better job than the CNN models used in this investigation. The findings demonstrate that the DCNN Classifier model, utilizing the VGG16 architecture and incorporating three supplementary CNN layers, exhibits superior performance. Also, the fact that the DCNN Classifier model works well as a decision support system for farmers is shown by the fact that it can quickly and accurately identify grape diseases, making it easier to take steps to stop them. The results of this study provide support for the reliability of the DCNN classifier model and its potential utility in the field of agriculture.

Machine vision and artificial intelligence for plant growth stress detection and monitoring: A review

The agricultural sector faces increasing challenges in ensuring food security and optimizing crop yield, necessitating innovative solutions for early detection and mitigation of plant growth stress. The integration of advanced imaging technologies with artificial intelligence (AI) has emerged as a powerful tool for non-invasive, real-time monitoring of plant health. The objective of this paper was to review the application of machine vision and AI in identifying and classifying plant growth stress, with a focus on stressors, datasets, and the use of intelligent algorithms. The significance of plant growth stress induced by environmental variables, including temperature, light, nutrient deficiencies, and water supply were addressed and the conventional stress detection methodologies, underscores their inherent limitations, and establishes the groundwork for the exploration of state-of-the-art technologies in stress assessment. Various sensor technologies were explored, encompassing traditional RGB cameras, multispectral and hyperspectral sensors, and thermal imaging, each capable of capturing distinct stress signatures. Machine vision, leveraging high-resolution imaging and spectroscopy, offers detailed insights into plant physiological responses. Coupled with AI approaches such as deep learning, neural networks, and pattern recognition, machine vision enables the automated analysis of vast datasets, enhancing the accuracy and speed of stress detection. The recent advancements in image processing techniques tailored for plant stress identification were focused and discussed the role of feature extraction, classification, and predictive modelling in achieving robust results. The potentials of AI in plant stress physiology and its role in overcoming the limitations of traditional methods, and the use of unsupervised identification of visual symptoms to quantify stress severity, allowing for the identification of different types of plant stress were studied. Moreover, the potentials of machine vision technology and AI for real-time monitoring and decision support systems in precision agriculture were discussed. The findings of this review would contribute to the growing field of agricultural technology, offering insights into the development of automated tools that could aid farmers and researchers in mitigating the impact of abiotic stressors on crop/plant health and productivity.

A Decision Support System for Sustainable Agriculture and Food Loss Reduction under Uncertain Agricultural Policy Frameworks

The EU Green Deal requires the reduction in pesticides and fertilisers in food crop production, whilst the sustainable development goals require reductions in food loss and food waste. In a complex and interacting system like the food system, these goals are difficult to coordinate. Here, we show an approach using Bayesian network modelling for decision support. Bayesian networks are important tools for modelling complex systems which may develop emergent behaviour and for providing quantitative comparisons for different candidate policies, approaches or interventions under the Integrating Decision Support System paradigm. Using lettuce as an exemplar crop, we demonstrate that expected food loss changes under different agricultural input reduction and integrated pest management combinations can be quantified to aid decision making for growers.

Agrarian Synthesis and Precision Cultivation Optimization System

The ever-growing demand for food production calls for innovative solutions in agriculture. This research introduces a machine learning-based approach, specifically utilizing logistic regression, to predict optimal crops based on soil and weather conditions. The dataset encompasses crucial attributes including Nitrogen (N), Phosphorus (P), Potassium (K), temperature, humidity, pH, rainfall, with corresponding crop labels. The proposed methodology employs logistic regression, a powerful classification algorithm, to model the relationships between input features and crop types. Through careful feature engineering, the model is fine-tuned to enhance its predictive accuracy. Rigorous evaluation metrics validate the model's performance, ensuring its reliability in real-world applications. Results showcase the logistic regression model's efficacy in accurately predicting suitable crops for given soil and weather parameters. This predictive tool serves as a practical decision support system for farmers, aiding in crop selection and resource allocation. This research contributes to the synergy of machine learning and agriculture, showcasing logistic regression as a valuable tool for crop prediction and resource optimization. As technology continues to transform traditional farming, the integration of logistic regression in precision agriculture offers a practical and efficient approach to crop selection.

Optimization of decision support systems in agriculture using AI: an integrated approach

Contemporary agriculture faces critical challenges in terms of sustainability and efficiency, in the context of constantpopulation growth and the need for environmentally responsible agricultural practices. This study addresses theapplication of Artificial Intelligence (AI) in the optimization of Decision Support Systems (SSD) for agriculture, with theaim of improving crop management and resource utilization. By integrating machine learning methods andimplementing predictive models, crop productivity was analyzed under various environmental conditions. The resultsdemonstrate improved accuracy in yield prediction, with a 15% increase in water and fertilizer use efficiency compared toconventional techniques. The research also reveals a significant increase in scientific collaboration and publication in thisfield since 2018, highlighting the importance of AI in the future of sustainable agriculture. The study's findings underlinethe potential of AI-based SSDs to transform agriculture, promoting more efficient and sustainable practices that canadapt to global challenges.

Understanding farmers’ engagement and barriers to machine learning‐based intelligent agricultural decision support systems

AbstractThe use of intelligent decision support systems (DSS) in precision farming provides an opportunity to improve agricultural recommendations and reduce the impacts of agriculture on the environment. Despite the benefits offered by DDS, many farmers remain skeptical of using these hardware and software tools, and their adoption rates have remained low. A survey of 312 South Dakota farmers examined the barriers and opportunities for their engagement with DSS. Exploratory factor analysis was used to analyze 13 Likert scale survey items that probed farmers’ concerns about DSS. Factor loadings indicated that farmers’ concerns are related to high cost, insufficient knowledge, lack of confidence, and cyber security and privacy. A latent profile analysis (LPA) method was used to classify respondents into profiles or groups based on their dimensions of concerns (cost, knowledge, confidence, and security). Results of the LPA revealed that the sample of farmers could be grouped into four distinct profiles that ranged from low to high confidence in the use of DSS for agronomic decision‐making. Giving attention to farmer comfort/concern profiles allows for a more inclusive and better targeted engagement with farmers and potentially increase the adoption of PA. This knowledge can be vital for technology developers, policymakers, and extension services who are keen to promote PA usage among large‐, medium‐, and small‐scale farmers in the United States.

The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era.

Smart agriculture is gaining a lot of attention recently, owing to technological advancement and promotion of sustainable habits. Unmanned aerial vehicles (UAVs) play a crucial role in smart agriculture by aiding in different phases of agriculture. The contribution of UAVs to sustainable and precision agriculture is a critical and challenging issue to be taken into account, particularly for smallholder farmers in order to save time and money, and improve their agricultural skills. Thence, this study targets to propose an integrated group decision-making framework to determine the best agricultural UAV. Previous studies on UAV evaluation, (i) could not model uncertainty effectively, (ii) weights of experts are not methodically determined; (iii) importance of experts and criteria types are not considered during criteria weight calculation, and (iv) personalized ranking of UAVs is lacking along with consideration to dual weight entities. Herein, nine critical selection criteria are identified, drawing upon the relevant literature and experts' opinions, and five extant UAVs are considered for evaluation. To circumvent the gaps, in this work, a new integrated framework is developed considering q-rung orthopair fuzzy numbers (q-ROFNs) for apt UAV selection. Specifically, methodical estimation of experts' weights is achieved by presenting the regret measure. Further, weighted logarithmic percentage change-driven objective weighting (LOPCOW) technique is formulated for criteria weight calculation, and an algorithm for personalized ranking of UAVs is presented with visekriterijumska optimizacija i kompromisno resenje (VIKOR) approach combined with Copeland strategy. The findings show that the foremost criteria in agricultural UAV selection are "camera," "power system," and "radar system," respectively. Further, it is inferred that the most promising UAV is the DJ AGRAS T30. Since the applicability of UAV in agriculture will get inevitable, the developed framework can be an effective decision support system for farmers, managers, policymakers, and other stakeholders.

A Soft Sensor to Estimate the Opening of Greenhouse Vents Based on an LSTM-RNN Neural Network.

In greenhouses, sensors are needed to measure the variables of interest. They help farmers and allow automatic controllers to determine control actions to regulate the environmental conditions that favor crop growth. This paper focuses on the problem of the lack of monitoring and control systems in traditional Mediterranean greenhouses. In such greenhouses, most farmers manually operate the opening of the vents to regulate the temperature during the daytime. Therefore, the state of vent opening is not recorded because control systems are not usually installed due to economic reasons. The solution presented in this paper consists of developing a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) as a soft sensor to estimate vent opening using the measurements of different inside and outside greenhouse climate variables as input data. A dataset from a traditional greenhouse located in Almería (Spain) was used. The data were processed and analyzed to study the relationships between the measured climate variables and the state of vent opening, both statistically (using correlation coefficients) and graphically (with regression analysis). The dataset (with 81 recorded days) was then used to train, validate, and test a set of candidate LSTM-based networks for the soft sensor. The results show that the developed soft sensor can estimate the actual opening of the vents with a mean absolute error of 4.45%, which encourages integrating the soft sensor as part of decision support systems for farmers and using it to calculate other essential variables, such as greenhouse ventilation rate.

Artificial intelligence-based decision support systems in smart agriculture: Bibliometric analysis for operational insights and future directions

As the world population is expected to touch 9.73 billion by 2050, according to the Food and Agriculture Organization (FAO), the demand for agricultural needs is increasing proportionately. Smart Agriculture is replacing conventional farming systems, employing advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) to ensure higher productivity and precise agriculture management to overcome food demand. In recent years, there has been an increased interest in researchers within Smart Agriculture. Previous literature reviews have also conducted similar bibliometric analyses; however, there is a lack of research in Operations Research (OR) insights into Smart Agriculture. This paper conducts a Bibliometric Analysis of past research work in OR knowledge which has been done over the last two decades in Agriculture 4.0, to understand the trends and the gaps. Biblioshiny, an advanced data mining tool, was used in conducting bibliometric analysis on a total number of 1,305 articles collected from the Scopus database between the years 2000–2022. Researchers and decision makers will be able to visualize how newer advanced OR theories are being applied and how they can contribute toward some research gaps highlighted in this review paper. While governments and policymakers will benefit through understanding how Unmanned Aerial Vehicles (UAV) and robotic units are being used in farms to optimize resource allocation. Nations that have arid climate conditions would be informed how satellite imagery and mapping can assist them in detecting newer irrigation lands to assist their scarce agriculture resources.

Rethinking ‘responsibility’ in precision agriculture innovation: lessons from an interdisciplinary research team

ABSTRACT We examine the interactions, decisions, and evaluations of an interdisciplinary team of researchers tasked with developing an artificial intelligence-based agricultural decision support system that can provide farmers site-specific information about managing nutrients on their land. We answer the following research questions: (1) How does a relational perspective help an interdisciplinary team conceptualize ‘responsibility' in a project that develops precision agriculture (PA)? and (2) What are some lessons for a research team embarking on a similar interdisciplinary technology development project? We show that how RI is materialized in practice within an interdisciplinary research team can produce different understandings of responsibility, notions of measurement of ‘matter,’ and metrics of success. Future interdisciplinary projects should (1) create mechanisms for project members to see how power and privilege are exercised in the design of new technology and (2) harness social sciences as a bridge between natural sciences and engineering for organic and equitable collaborations.

Using AI to Improve Sustainable Agricultural Practices: A Literature Review and Research Agenda

The world is confronted with the grand challenge of food insecurity amidst growing populations and the climate crisis. Artificial intelligence (AI) deployed in agricultural decision support systems (AgriDSS) raises both hopes and concerns for increasing agricultural productivity in sustainable ways. In this paper, we conduct a scoping review to uncover the roadblocks to the use of AI-supported AgriDSS in sustainable agriculture. Based on the corpus of 121 articles, we find that the effective use of AI-supported AgriDSS is hindered at technical, social, ethical, and ecological levels. Then, drawing on the experiential learning perspective, we propose how conjoint experiential learning (CEL) can enhance sustainable agricultural practices by enhancing both AI and human learning and overcoming roadblocks in using AgriDSS. Based on this conceptual framework, we build a research agenda that suggests blind spots and possible directions for future research.

A farmer-centric agricultural decision support system for market dynamics in a volatile agricultural supply chain

PurposeIn a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.Design/methodology/approachThe present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.FindingsEmpirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.Practical implicationsAs a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.Originality/valueBased on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

Mapping Productivity-related Spatial Characteristics in Rice-based Cropping Systems in Sri Lanka

Accurate and timely information on the productivity-related spatial characteristics in paddy fields will facilitate accurate decision-making to achieve food security needs. The aim of the present study was to map rice-cultivating regions in Sri Lanka based on crop productivity and related agronomic decisions made. A grid-based map coupled with a paddy land map of the country was used for the sampling protocol, and the generated data were mapped using ArcGIS software. The study revealed the spatial heterogeneity in the productivity of lowland paddy lands, rice varieties cultivated according to their age classes and breeding stations, water sources used for cultivation, and crops grown in lowlands during the water-limited cropping seasons. The constructed maps carrying the productivity-related spatial characteristics of rice across the country could facilitate agricultural decision support systems. The maps could be used to develop novel approaches for the management of rice productivity in rice-based cropping systems in the country.

Developing Visual-Assisted Decision Support Systems across Diverse Agricultural Use Cases

Decision support systems (DSSs) in agriculture are becoming increasingly popular, and have begun adopting visualisations to facilitate insights into complex data. However, DSSs for agriculture are often designed as standalone applications, which limits their flexibility and portability. They also rarely provide interactivity, visualise uncertainty and are evaluated with end-users. To address these gaps, we developed six web-based visual-assisted DSSs for various agricultural use cases, including biological efficacy correlation analysis, water stress and irrigation requirement analysis, product price prediction, etc. We then evaluated our DSSs with domain experts, focusing on usability, workload, acceptance and trust. Results showed that our systems were easy to use and understand, and participants perceived them as highly performant, even though they required a slightly high mental demand, temporal demand and effort. We also published the source code of our proposed systems so that they can be re-used or adapted by the agricultural community.

In pursuit of responsible innovation for precision agriculture technologies

ABSTRACT Agricultural decision support systems (DSSs) are hardware and software tools that utilize big data collected from satellites and drones, ground-based sensors, and analyzed with machine learning algorithms to provide site-specific farming recommendations. Despite the promise of DSSs to address many challenges of the farm economy, there are social and ethical concerns that need to be addressed. Utilizing a mixed-methods approach that consisted of focus group discussions and a follow-up survey questionnaire, we highlight the experiences and affectations of heterogeneous food system actors from Vermont and South Dakota. We find that DSSs transform agricultural knowledge production, reconfigure labor arrangements and unevenly distribute benefits and burdens among farmers. We suggest that agritech developers implement inclusive and deliberative processes when redesigning DSSs to engender ethical, equitable and sustainable improvements to food production systems. Inclusive processes of open deliberation are modalities of responsible innovation, tasked with mitigating frictions within socio-technical systems.

FPGA based intelligent embedded system for predicting the productivity using fuzzy logic

This work focuses on developing an intelligent embedded fuzzy decision support system (IEFDSS) in agriculture. The Intelligent Decision Support System (DSS) can manage and ensure the required minimum returns for farmers. Unlike other agricultural intelligent systems, the FPGA device is used as the computing architecture design framework for the proposed intelligent agrarian system. In this proposed system, a fuzzy logic used hardware module provides future productivity and recommendations. The status quo separated by such requests is based on international standards and the challenges faced by the field experts. Therefore, this has been developed into an IEFDSS and transformed into the Register Transfer Level (RTL) netlist using Xfuzzy. The proposed approach has been thoroughly tested at ground level, and designed structural test results have made significant improvements compared to the lack of proper technique. The experiment's results show that the accuracy is 96% higher than the present methods.

Smart Pre-Seeding Decision Support System for Agriculture

In recent years, the Internet of Things (IoT) brings a new dimension for establishing a precision network connectivity of sensors, especially in the agriculture and farming industry, medical, economic, and several sectors of modern society. Agriculture is an important area for the sustainability of mankind engulfing manufacturing, security, and resource management. Due to the exponential diminishing of the resources, innovative techniques to support the subsistence of agriculture and farming. IoT aims to extend the use of internet technology to a large number of distributed and connected devices by representing standard and interoperable communication protocols. This paper brings up a solution by IoT, presents the design and implementation of a smart pre-seeding decision support system for agricultural modernization. This project is accomplished by understanding the real-time circumstances in the agriculture field using wireless technology that highlighted the features including pH and temperature sensors, hardware, mobile application, system’s frontend, and backend analysis, and stores the extracted information in the cloud using IoT. The system is made up of frontend data acquisition, data transmission, data processing, and reception, and is also experimentally validated to find out all possible crops that can be cultivated in a specific land with the required amount of fertilizers as well as the overall crops distribution lists.

Smart Agricultural Decision Support Systems for Predicting Soil Nutrition Value Using IoT and Ridge Regression

Cost effective agricultural crop productivity is an everlasting demand, this predominant expedition has raised a global shift towards practicing smart agricultural methods to increase the productivity and the efficiency of the agricultural sector, using IoT. This research identified the benefits and the challenges in IoT adoption as an alternate for out-of-date agricultural practices. The proposed decision support system using IoT for Smart Soil Nutrition Prediction (SSNP) adopts IR sensors and implements diffuse reflectance infrared spectroscopy. Information is transferred using Arduino and Zigbee protocol. It has indicated precise outcomes in various studies giving a high repeatable, low cost and fast estimation of soil properties. The measure of light absorbed by a soil example is estimated, inside several particular wavebands over a scope of frequencies to yield an infrared range utilizing an IR sensor. Using the given values, the experimental analysis using the dataset and the nutrition values of the soil such as Ca, P, SOC, Sand and pH are predicted. This proposed IoT framework would enhance the farmer’s knowledge regarding the type of crops they should grow to get maximum profit from their agricultural produce.