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Data-Driven Agricultural Innovation Technology for Digital Agriculture

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 13779

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Dr. Wan-Soo Kim

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Department of Bio-Industrial Machinery Engineering, Kyungpook National University (KNU)-Daegu Campus, 80 Daehak-ro, Buk-gu, Daegu, Republic of Korea
Interests: digital agriculture; precision agriculture; soil characteristics; agricultural machinery load characteristics

Special Issue Information

Dear Colleagues,

Food security issues are emerging around the world and the need for improved agricultural productivity and high-convenience agriculture is increasing. Digital agriculture can be an effective solution to the above issues. In digital agriculture, data are the most valuable thing, and the effect of digital agriculture will be different depending on how well it is processed and reproduced. This is why we should pay attention to data-driven agricultural innovation technology. Even at this moment, various attempts are being made to implement digital agriculture by many researchers around the world. However, there is still an opening for new agricultural innovation technologies to be developed.

This Special Issue will focus on data-driven agricultural innovation technology for digital agriculture for the digitization of the data-driven entire agricultural cycle. We welcome research and reviews covering all relevant topics, including various agricultural innovation technologies such as sensing, control, data measurement, mapping, computational modelling, digital twins, artificial intelligence, intelligent systems, and solutions for digital agriculture.

Dr. Wan-Soo Kim
Guest Editor

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Keywords

digital agriculture
data agriculture
precision agriculture
agricultural informatics
digitalization
computational modelling
digital twin
artificial intelligence
intelligent systems

Published Papers (7 papers)

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Editorial

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3 pages, 182 KiB

Open AccessEditorial

Data-Driven Agricultural Innovation Technology for Digital Agriculture

by Wan-Soo Kim

Appl. Sci. 2023, 13(20), 11163; https://doi.org/10.3390/app132011163 - 11 Oct 2023

Viewed by 1156

Abstract

Food security issues are emerging worldwide due to rapid climate change and war [...] Full article

(This article belongs to the Special Issue Data-Driven Agricultural Innovation Technology for Digital Agriculture)

Research

Jump to: Editorial

21 pages, 1588 KiB

Open AccessArticle

The Role of Data-Driven Methodologies in Weather Index Insurance

by Luis F. Hernández-Rojas, Adriana L. Abrego-Perez, Fernando E. Lozano Martínez, Carlos F. Valencia-Arboleda, Maria C. Diaz-Jimenez, Natalia Pacheco-Carvajal and Juan J. García-Cárdenas

Appl. Sci. 2023, 13(8), 4785; https://doi.org/10.3390/app13084785 - 11 Apr 2023

Cited by 2 | Viewed by 1813

Abstract

There are several index insurance methodologies. Most of them rely on linear piece-wise methods. Recently, there has been studies promoting the potential of data-driven methodologies in construction index insurance models due to their ability to capture intricate non-linear structures. However, these types of frameworks have mainly been implemented in high-income countries due to the large amounts of data and high-frequency requirements. This paper adapts a data-driven methodology based on high-frequency satellite-based climate indices to explain flood risk and agricultural losses in the Antioquia area (Colombia). We used flood records as a proxy of crop losses, while satellite data comprises run-off, soil moisture, and precipitation variables. We analyse the period between 3 June 2000 and 31 December 2021. We used a logistic regression model as a reference point to assess the performance of a deep neural network. The results show that a neural network performs better than traditional logistic regression models for the available loss event data on the selected performance metrics. Additionally, we obtained a utility measure to derive the costs associated for both parts involved including the policyholder and the insurance provider. When using neural networks, costs associated with the policyholder are lower for the majority of the range of cut-off values. This approach contributes to the future construction of weather insurance indexes for the region where a decrease in the base risk would be expected, thus, resulting in a reduction in insurance costs. Full article

(This article belongs to the Special Issue Data-Driven Agricultural Innovation Technology for Digital Agriculture)

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12 pages, 2224 KiB

Open AccessArticle

Optimization of Design Parameters Using SQP for an Agricultural Pipe Extraction Device

by Su-Min Lee, Sang-Hong Lee, Hyun-Woo Han, Jooseon Oh and Sung-Bo Shim

Appl. Sci. 2023, 13(5), 3167; https://doi.org/10.3390/app13053167 - 1 Mar 2023

Viewed by 1201

Abstract

Removal of agricultural pipes used in crop support and greenhouse agriculture is manpower-intensive. However, most agricultural workers are elderly. Therefore, auxiliary devices should be used to allow pipe removal with as little force as possible. In this study, the design parameters of an extraction device were optimized within constraints to minimize the force required to remove agricultural pipes. The optimization parameters are the length of each link and the initial link angle of the device. The driving force, according to the design parameters, was calculated by applying the theory of kinematics. An optimal design plan was derived using an optimization algorithm to minimize the force driving the device within the desired constraint. As a result of the optimization, it was confirmed that the force required to remove the pipe was reduced by 87.1% compared with the initial design value and was designed to suit the user’s convenience. Full article

(This article belongs to the Special Issue Data-Driven Agricultural Innovation Technology for Digital Agriculture)

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18 pages, 3722 KiB

Open AccessArticle

Forecasting Agricultural Financial Weather Risk Using PCA and SSA in an Index Insurance Model in Low-Income Economies

by Adriana L. Abrego-Perez, Natalia Pacheco-Carvajal and Maria C. Diaz-Jimenez

Appl. Sci. 2023, 13(4), 2425; https://doi.org/10.3390/app13042425 - 13 Feb 2023

Cited by 2 | Viewed by 2251

Abstract

This article presents a novel methodology to assess the financial risk to crops in highly weather-volatile regions. We use data-driven methodologies that use singular value decomposition techniques in a low-income economy. The risk measure is first derived by applying data-driven frameworks, a Principal Component Analysis (PCA), and Singular Spectrum Analysis (SSA) to productive coffee crops in Colombia (163 weather stations) during 2010–2019. The objective is to understand the future implications that index insurance tools will have on strategic economic crops in the country. The first stage includes the identification of the PCA components at the country level. The risk measure, payouts-in-exceedance ratio, or POER, is derived from an analysis of the most volatile-weather-producing regions. It is obtained from a linear index insurance model applied to the extracted singular-decomposed tendencies through SSA on first-component data. The financial risk measure due to weather volatilities serves to predict the future implications of the payouts-in-exceedance in both seasons—wet and dry. The results show that the first PCA component contributes to forty percent of the total variance. The seasonal forecast analysis for the next 24 months shows increasing additional payouts (PO), especially during the wet season. This is caused by the increasing average precipitation tendency component with POERs of 18 and 60 percent in the first and second years. The findings provide important insights into designing agricultural hedging insurance instruments in low-income economies that are reliant on the export of strategic crops, as is the case of Colombian coffee. Full article

(This article belongs to the Special Issue Data-Driven Agricultural Innovation Technology for Digital Agriculture)

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14 pages, 2886 KiB

Open AccessArticle

Tomato Maturity Estimation Using Deep Neural Network

by Taehyeong Kim, Dae-Hyun Lee, Kyoung-Chul Kim, Taeyong Choi and Jun Myoung Yu

Appl. Sci. 2023, 13(1), 412; https://doi.org/10.3390/app13010412 - 28 Dec 2022

Cited by 2 | Viewed by 1798

Abstract

In this study, we propose a tomato maturity estimation approach based on a deep neural network. Tomato images were obtained using an RGB camera installed on a monitoring robot and samples were cropped to generate a dataset with which to train the classification model. The classification model is trained using cross-entropy loss and mean–variance loss, which can implicitly provide label distribution knowledge. For continuous maturity estimation in the test stage, the output probability distribution of four maturity classes is calculated as an expected (normalized) value. Our results demonstrate that the F1 score was approximately 0.91 on average, with a range of 0.85–0.97. Furthermore, comparison with the hue value—which is correlated with tomato growth—showed no significant differences between estimated maturity and hue values, except in the pink stage. From the overall results, we found that our approach can not only classify the discrete maturation stages of tomatoes but can also continuously estimate their maturity. Furthermore, it is expected that with higher accuracy data labeling, more precise classification and higher accuracy may be achieved. Full article

(This article belongs to the Special Issue Data-Driven Agricultural Innovation Technology for Digital Agriculture)

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17 pages, 4156 KiB

Open AccessArticle

Promotion of Color Sorting in Industrial Systems Using a Deep Learning Algorithm

by Ivana Medojevic, Emil Veg, Aleksandra Joksimovic and Jelena Ilic

Appl. Sci. 2022, 12(24), 12817; https://doi.org/10.3390/app122412817 - 13 Dec 2022

Cited by 3 | Viewed by 2054

Abstract

Color sorting is a technological operation performed with the aim of classifying compliant and noncompliant agricultural products in large-capacity industrial systems for agricultural product processing. This paper investigates the application of the YOLOv3 algorithm on raspberry images as a method developed for the detection, localization, and classification of objects based on convolutional neural networks (CNNs). To our knowledge, this is the first time a YOLO algorithm or CNN has been used with original images from the color sorter to focus on agricultural products. Results of the F1 measure were in the 92–97% range. Images in full resolution, 1024 × 1024, produced an average detection time of 0.37 s. The impact of the hyperparameters that define the YOLOv3 model as well as the impact of the application of the chosen augmentative methods on the model are evaluated. The successful classification of stalks, which is particularly challenging due to their shape, small dimensions, and variations, was achieved. The presented model demonstrates the ability to classify noncompliant products into four classes, some of which are appropriate for reprocessing. The software, including a graphic interface that enables the real-time testing of machine learning algorithm, is developed and presented. Full article

(This article belongs to the Special Issue Data-Driven Agricultural Innovation Technology for Digital Agriculture)

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19 pages, 9714 KiB

Open AccessArticle

Spatial Evaluation of Machine Learning-Based Species Distribution Models for Prediction of Invasive Ant Species Distribution

by Wang-Hee Lee, Jae-Woo Song, Sun-Hee Yoon and Jae-Min Jung

Appl. Sci. 2022, 12(20), 10260; https://doi.org/10.3390/app122010260 - 12 Oct 2022

Cited by 9 | Viewed by 2357

Abstract

Recent advances in species distribution models (SDMs) associated with artificial intelligence (AI) and increased volumes of available data for model variables have allowed reliable evaluation of the potential distribution of any species. A reliable SDM requires suitable occurrence records and variables with optimal model structures. In this study, we developed three different machine learning-based SDMs [MaxEnt, random forest (RF), and multi-layer perceptron (MLP)] to predict the global potential distribution of two invasive ants under current and future climates. These SDMs showed that the potential distribution of Solenopsis invicta would be expanded by climatic change, whereas it would not significantly change for Anoplolepis gracilipes. The models were compared using model performance metrics, and the optimal model structure and spatial projection were selected. The MaxEnt exhibited high performance, while the MLP model exhibited low performance, with the largest variation by climate change. Random forest showed the smallest potential distribution area, but it was robust considering the number of occurrence records and changes in model variables. All the models showed reliable performance, but the difference in performance and projection size suggested that optimal model selection based on data availability, model variables, study objectives, or an ensemble approach was necessary to develop a comprehensive SDM to minimize modeling uncertainty. We expect that this study will help with the use of AI-based SDMs for the evaluation and risk assessment of invasive ant species. Full article

(This article belongs to the Special Issue Data-Driven Agricultural Innovation Technology for Digital Agriculture)

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