Data-Driven Agricultural Innovation Technology for Digital Agriculture

Food security issues are emerging worldwide due to rapid climate change and war [1], and accordingly, the demand for sustainable agriculture, such as improved agricultural productivity and high-efficiency agriculture is increasing [2]. Digital agriculture could be an effective solution to these problems [3]. In the past, agricultural practices were mostly carried out based on farmers’ experiences, but as data collection and communication became easier with the development of various advanced ICT technologies such as IoT and AI, the era of data-based digital agriculture has now arrived [4]. The value of data is paramount in digital agriculture, and the effectiveness of digital agriculture depends on how well data are processed and reproduced [5]. This is why we need to pay attention to data-based agricultural innovation technologies. Even now, many researchers worldwide are making various attempts to implement digital agriculture [6,7,8]. However, there is still room for new agricultural innovations to be developed.

This Special Issue focuses on various agricultural innovation technologies such as sensing, control, data measurement, spatial mapping, computer modeling, artificial intelligence, intelligent systems, and digital agricultural solutions for the digitization of the entire agricultural cycle. A total of six research papers in various fields regarding digital agriculture are introduced in this Special Issue. Lee et al. (contribution 1) proposed a tomato maturity estimation approach based on a deep neural network using tomato images using an RGB camera installed on a monitoring robot. They reported that the results of this study are not only able to classify individual ripening stages of tomatoes, but also estimate the maturity of tomatoes in real time. These research results on crop recognition can be used to implement agricultural robots and agricultural automation in various agricultural environments such as open fields and greenhouses. Hernández-Rojas et al. (contribution 2) proposed a data-driven methodology using artificial neural networks based on high-frequency satellite-based climate indices to explain flood risk and agricultural losses in the Antioquia region of Colombia. The authors reported that the proposed method can contribute to the construction of a weather insurance index in areas where base risk is expected to decrease in the future, resulting in insurance cost savings. Lee et al. (contribution 3) developed three different machine learning-based species distribution models (SDMs) to predict and evaluate the global potential distribution of two invasive ant species for current as well as future climates. This study presented results on the feasibility of using artificial intelligence (AI)-based SDM in the risk assessment of invasive ant species, and it was reported that all three proposed models showed reliable performance. Medojevic et al. (contribution 4) proposed a method of applying the YOLOv3 algorithm to raspberry images as a method developed for object detection, localization, and classification based on CNNs (Convolutional Neural Networks). The authors reported the successful classification of stems, which are particularly difficult to recognize due to their shape, small dimensions, and deformation. In particular, the application of this method is expected to significantly increase the accuracy of individual identification, increase the efficiency of agricultural product classification, increase the objectivity of decision making, and ensure the entire process facilitates greater economic benefits. Lee et al. (contribution 5) proposed a method to reduce the high level of labor required to remove existing agricultural pipes through the optimization of design variables of pipe extraction devices, which also significantly lowered the force required to remove pipes compared to conventional methods. The authors reported that this study improved the agricultural operation environment and improved the convenience of using agricultural equipment, enabling farmers to work more efficiently. Abrego-Perez et al. (contribution 6) presented a new data-driven methodology to assess the future impact of index insurance tools on strategic economic crops in highly variable weather regions in Colombia. This is expected to provide useful information for the design of insurance measures in Colombia’s agricultural economy, which is completely dependent on exports, such as coffee.

Although this Special Issue, entitled “Data-Driven Agricultural Innovation Technology for Digital Agriculture”, has closed, continued and in-depth research on data-based digital agriculture will significantly contribute to securing future food security for the world’s population and solving pressing agricultural problems.

List of Contributions

• Kim, T.; Lee, D.H.; Kim, K.C.; Choi, T.; Yu, J.M. Tomato Maturity Estimation Using Deep Neural Network. Appl. Sci. 2023, 13, 412. https://doi.org/10.3390/app13010412.
• Hernández-Rojas, L.F.; Abrego-Perez, A.L.; Lozano Martínez, F.E.; Valencia-Arboleda, C.F.; Diaz-Jimenez, M.C.; Pacheco-Carvajal, N.; García-Cárdenas, J.J. The Role of Data-Driven Methodologies in Weather Index Insurance. Appl. Sci. 2023, 13, 4785. https://doi.org/10.3390/app13084785.
• Lee, W.H.; Song, J.W.; Yoon, S.H.; Jung, J.M. Spatial Evaluation of Machine Learning-Based Species Distribution Models for Prediction of Invasive Ant Species Distribution. Appl. Sci. 2022, 12, 10260. https://doi.org/10.3390/app122010260.
• Medojevic, I.; Veg, E.; Joksimovic, A.; Ilic, J. Promotion of Color Sorting in Industrial Systems Using a Deep Learning Algorithm. Appl. Sci. 2022, 12, 12817. https://doi.org/10.3390/app122412817.
• Lee, S.M.; Lee, S.H.; Han, H.W.; Oh, J.; Shim, S.B. Optimization of Design Parameters Using SQP for an Agricultural Pipe Extraction Device. Appl. Sci. 2023, 13, 3167. https://doi.org/10.3390/app13053167.
• Abrego-Perez, A.L.; Pacheco-Carvajal, N.; Diaz-Jimenez, M.C. Forecasting Agricultural Financial Weather Risk Using PCA and SSA in an Index Insurance Model in Low-Income Economies. Appl. Sci. 2023, 13, 2425. https://doi.org/10.3390/app13042425.