Data-Driven Artificial Intelligence Applications for Sustainable Precision Agriculture

Linaza, María Teresa; Posada, Jorge; Bund, Jürgen; Eisert, Peter; Quartulli, Marco; Döllner, Jürgen; Pagani, Alain; Olaizola, Igor G.; Barriguinha, André; Moysiadis, Theocharis; Lucat, Laurent

doi:10.3390/agronomy11061227

Cited by 123 publications

(54 citation statements)

References 21 publications

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“…The introduction of artificial intelligence in agriculture is associated with the problems of the systematic collection of data from multiple sources of storage and analysis. Approaches to solving these problems are outlined in [26,27] and represent pilot projects for the analysis of advanced control systems in various natural and climatic conditions. One of the key artificial intelligence techniques, such as machine learning, has significant potential for solving numerous problems.…”

Section: A Precision Agriculture Digitalization and Artificial Intell...mentioning

confidence: 99%

Artificial Neural Networks and Computer Vision’s-Based Phytoindication Systems for Variable Rate Irrigation Improving

et al. 2022

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The article proposes a methodology for optimizing the process of irrigation of crops using a phytoindication system based on computer vision methods. We have proposed an algorithm and developed a system for obtaining a map of irrigation for maize in low latency mode. The system can be installed on a center pivot irrigation and consists of 8 IP cameras connected to a DVR connected to a laptop. The algorithm consists of three stages. Image preprocessing stage -applying an integrated excess green and excess red difference (ExGR) index. The classification stage is the application of the method that we choose depending on the operating conditions of the system. At the final stage, a neural network trained using the Resilient Propagation method is used, which determines the rate of watering of plants in the current sector of the location of the sprinkler. The selected methods of pretreatment and classification made it possible to achieve an accuracy of plant identification up to 93%, growth stages -up to 92% (with unconsolidated maize sowing and good lighting). System performance up to 100 plants in one second, which exceeds the performance of similar systems. The neural network showed an accuracy of 92% on the training set and 87% on the test set. Dynamic analysis of spatial and temporal variability leads to an increase in productivity and efficiency of water use. In addition, given the ubiquitous distribution of agribusiness management systems, this approach is quite simple to implement in the farm's conditions.

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Section: A Precision Agriculture Digitalization and Artificial Intell...mentioning

confidence: 99%

Artificial Neural Networks and Computer Vision’s-Based Phytoindication Systems for Variable Rate Irrigation Improving

et al. 2022

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“…Combining information technology with modern agricultural operation and management systems is a new agricultural practice for positioning, timing, and quantification. The rapid development of precision agriculture in facility farming demands rapid and accurate detection technology for the precise control and management of desert facilities [1,2]. Precision agriculture is a new system in desert facility agriculture.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Near-Infrared Spectroscopy for Rapid Detection of Available Nitrogen in Vermiculite Substrates in Desert Facility Agriculture

Zhao

Xing

et al. 2022

Agriculture

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Fast and precise estimation of the available nitrogen content in vermiculite substrates promotes prescription fertilization in desert facility agriculture. This study explored near-infrared spectroscopy for rapid detection of the available nitrogen content in vermiculite substrates in desert facility agriculture. The spectra of vermiculite matrices with different available nitrogen contents were collected through a self-assembled near-infrared spectrometer. Partial least squares expression (PLSR) established the available nitrogen spectrum prediction model optimized using different pretreatments. After pretreatment, the prediction model of the available nitrogen spectrum was simplified by adopting three feature extraction methods. A comprehensive comparison of the results of each prediction model showed that the prediction model combining the first derivative with SG smoothing pretreatment was the best. The correlation coefficients of the corresponding calibration and prediction sets were 0.9972 and 0.9968, respectively. The root mean square errors of the calibration and prediction sets were 149.98 and 159.65 mg/kg, respectively, with 12.57 RPD. These results provide a feasible method for rapidly detecting the available nitrogen content of vermiculite substrates in desert facility agriculture.

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“…The increase in organic vegetable consumption and the search for more sustainable and environmentally friendly agriculture have triggered factors to give rise to new technologies to optimize vegetable cultivation and treatment processes for supplying the constant market demand ( Pachapur et al, 2020 ; Balkrishna, 2021 ; Lichtfouse, 2021 ; Linaza et al, 2021 ; Singh, 2021 ). These processes are mainly focused on the detection, irrigation, fertilization of vegetables and fruits during their growth and harvest phases ( Gonzalez-de Santos et al, 2020 ; Tang et al, 2020 ; Wu et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Trend Technologies for Robotic Fertilization Process in Row Crops

et al. 2022

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The development of new sensory and robotic technologies in recent years and the increase in the consumption of organic vegetables have allowed the generation of specific applications around precision agriculture seeking to satisfy market demand. This article analyzes the use and advantages of specific optical sensory systems for data acquisition and processing in precision agriculture for Robotic Fertilization process. The SUREVEG project evaluates the benefits of growing vegetables in rows, using different technological tools like sensors, embedded systems, and robots, for this purpose. A robotic platform has been developed consisting of Laser Sick AG LMS100 × 3, Multispectral, RGB sensors, and a robotic arm equipped with a fertilization system. Tests have been developed with the robotic platform in cabbage and red cabbage crops, information captured with the different sensors, allowed to reconstruct rows crops and extract information for fertilization with the robotic arm. The main advantages of each sensory have been analyzed with an quantitative comparison, based on information provided by each one; such as Normalized Difference Vegetation Index index, RGB Histograms, Point Cloud Clusters). Robot Operating System processes this information to generate trajectory planning with the robotic arm and apply the individual treatment in plants. Main results show that the vegetable characterization has been carried out with an efficiency of 93.1% using Point Cloud processing, while the vegetable detection has obtained an error of 4.6% through RGB images.

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Data-Driven Artificial Intelligence Applications for Sustainable Precision Agriculture

Cited by 123 publications

References 21 publications

Artificial Neural Networks and Computer Vision’s-Based Phytoindication Systems for Variable Rate Irrigation Improving

Artificial Neural Networks and Computer Vision’s-Based Phytoindication Systems for Variable Rate Irrigation Improving

Feasibility of Near-Infrared Spectroscopy for Rapid Detection of Available Nitrogen in Vermiculite Substrates in Desert Facility Agriculture

Trend Technologies for Robotic Fertilization Process in Row Crops

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