Machine Learning Methods for Determining Optimal Irrigation Timing for Corn

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Abstract

The global forecast for increasing food production on irrigated lands poses the task of optimizing irrigation. Saving water resources is especially important in arid areas, where it is very important to clearly understand what to water, when and in what quantity. The article proposes a method for optimizing the irrigation process of agricultural crops using a control system based on visible and hyperspectral images. We proposed an algorithm and developed a system for obtaining a map of corn irrigation in the low-delay mode. The system can be installed on a circular sprinkler and consists of 8 IP cameras connected to a video recorder connected to a laptop and a hyperspectral camera synchronized with one of the IP cameras. The algorithm for establishing irrigation rates consists of three stages. The stage of establishing the average stage of plant growth (a site of 6–8 plants), the stage of determining the amount of water in plants on this site and the stage of establishing plant irrigation rates directly. In the first case, we used a modified DenseNet121 convolutional neural network with a compression and excitation (SE) block, trained on visible images from an IP camera and allowing to identify the growth stage according to the Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) scale with an accuracy of up to 92%. In the second case, we used hyperspectral images, which, together with the data on the development stage, determine the amount of water in plants. Hyperspectral images were converted into a 2D-model using wavelet transforms and then classified using the 2D-CapsNet capsule neural network. The accuracy of detecting a lack or excess of water in plants was 94%. At the third stage, the data obtained from the two previous stages and a number of characteristics related to the current state of the atmosphere and the field were combined into a separate classifier based on a neural network – a multilayer perceptron, which marked the areas of the field with increased and decreased irrigation rates. The resulting map was then used to irrigate the field. This reduced the amount of water used by 7.4%. At the same time, the efficiency of irrigation water use, linked to the yield of agricultural crops per unit of water used, increased due to an increase in yield by 8.4%.

About the authors

Sergey T. Gataullin

MIREA – Russian Technological University

Author for correspondence.
Email: gataullin@mirea.ru
ORCID iD: 0000-0002-0446-0552
Scopus Author ID: 57205436562
ResearcherId: AAX- 8389-2021

Cand. Sci. (Econ.); leading researcher, Institute of Advanced Technologies and Industrial Programming

Russian Federation, Moscow

Alexey V. Osipov

MIREA – Russian Technological University

Email: osipov_av@mirea.ru
ORCID iD: 0000-0002-1261-8559
Scopus Author ID: 57224632462
ResearcherId: AAB-5151-2022

Cand. Sci. (Phys.-Math.); associate professor, Institute of Advanced Technologies and Industrial Programming;

Russian Federation, Moscow

Ekaterina S. Pleshakova

MIREA – Russian Technological University

Email: pleshakova@mirea.ru
ORCID iD: 0000-0002-8806-1478
SPIN-code: 5152-8969
Scopus Author ID: 56471764200
ResearcherId: ABG-2302-2021

Cand. Sci. (Eng.); associate professor, Institute of Advanced Technologies and Industrial Programming

Russian Federation, Moscow

Alexander V. Yudin

MIREA – Russian Technological University

Email: yudin_a@mirea.ru
ORCID iD: 0000-0002-6802-8603
Scopus Author ID: 56018042000
ResearcherId: A-1665-2014

Dr. Sci. (Econ.); Head of the Department, Institute of Advanced Technologies and Industrial Programming

Russian Federation, Moscow

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Supplementary files

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1. JATS XML
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2. Fig. 1. a – Hyperspectral cube image of a field plot; b – corn canopy spectrum (OCS)

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3. Fig. 2. Features of the formation of a dataset of hyperspectral images in an irrigated field

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4. Fig. 3. The second (a) and first (b) derivatives with respect to wavelength, respectively, of the corn vegetation spectrum the corn vegetation spectrum (c)

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5. Fig. 4. Wavelet transform of the graphs of dependencies of the second (a) and first (b) derivatives with respect to wavelength, respectively, from the spectrum of the corn vegetation cover of the spectrum of the corn vegetation cover (c)

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6. Fig. 5. Architecture of CNN DenseNet121+ SE

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7. Fig. 6. Block representation of the 2D-CapsNet network model for 2D-image classification using wavelet transform of hyperspectral data

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8. Fig. 7. Scheme of the proposed approach to identifying the state of corn crops based on the 2D-CapsNet method

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9. Fig. 8. Hyperspectral plots of maize plants: a – with different water contents in plants at the BBCH-43 growth stage; b – at different growth stages with water content FMCc = 2.7–3%

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10. Fig. 9. Second (a) and first (b) derivatives with respect to wavelength, respectively, of the spectrum of the corn vegetation cover; the spectrum of the corn vegetation cover (c); 1–5 – the hyperspectrum is taken from sites of five different classes. The graph number corresponds to the class number

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11. Fig. 10. Wavelet transform of the dependence graphs of the spectrum of corn vegetation cover for five classes

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12. Fig. 11. Using t-SNE to visualize 2D-CapsNet capsule-level data: 1–5 are the five classes labeled by agricultural scientists according to the BBCH stages

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13. Fig. 12. Using t-SNE method to visualize 2D-CapsNet capsule-level data

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14. Fig. 13. Algorithm for creating an irrigation map: a – development map; b – map of the amount of water in corn plants; c – current NDWI; d – map of soil water availability; e – map of division into sectors for irrigation

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15. Fig. 14. Distribution of percentages of total area under the influence of dynamic prescription

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