Open Journal Systems

Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.

A new approach to measure spatial variability of soil parameters and field technique to test-value specific fertilizer

Information on the distribution of soil properties is important to know the status of nutrients in the soils based on which fertilizer nutrients are recommended. Given the variability of nutrients in the soils, making a site-specific fertilizer recommendation seems to be a compelling work. To determine the spatial variability of soil nutrients and to make judicious and precise fertilizer recommendations, new measures are designed with this study. These measures are tested against the soil samples (n = 43) for total nitrogen (N), organic matter (OM), phosphorus (P2O5), and potassium (K2O) in the study area. The descriptive statistical analysis indicated an average of low nitrogen and organic matter, while phosphorus was found to be very high and the level of potassium was high. The spread of nutrients across the data sets, however, included low, medium, high, and very high levels of ratings. The Deviation Square Index was developed and applied for the variability measurement and found that the largest variation was with phosphorus distribution, followed by potassium, nitrogen, and organic matter. The coefficient of variation (CV%) analysis also exhibited similar trends in nutrient distributions. Nitrogen was the main determinant explaining the variations in rice yield, while phosphorus and potash were negatively related to the yield. An index of fertilizer nutrient recommendation called Test-Value Specific Dose (TVSD) was developed and used to calculate the nutrient recommendation for each sampled location. This new method gave easy and more accurate doses of fertilizer over the blanket recommendation to fit the variations across the soil samples.

Applications of artificial intelligence in precision agriculture to ameliorate production and distribution

Garimella Bhaskar Narasimha Rao

Article ID: 2374
Vol 4, Issue 2, 2023

DOI: https://doi.org/10.54517/ama.v4i2.2374
VIEWS - 3328 (Abstract)

Download PDF

Abstract

Automated intelligence platforms, i.e., machine learning, big data, and Internet of Things (IoT), provide new deployment opportunities within the agricultural marketing paradigm. This study attempts to derive a framework of predictive models to ameliorate crop yield and assists in understanding various features that affect crop yield. On the one hand, it investigates the impact of allied technologies, including networks with memory and generative models, and on the other, it quantitatively analyzes different agri-factors, including the management of plant growth, its quality, crop disease, inorganic fertilizer and pesticide deployment, weed management, irrigation, and field-level phenotyping. Further, the study analyzes the utilization of smart farming and the monitoring of highly dependent variables across the spectrum of precision agriculture. The conclusion is to manifest the importance of networks with memory and generative models and emphasize the vital role of artificial intelligence in transforming farm methods into a novel methodology of smart information communication technology (ICT) in fidelity agriculture. Apart from increased productivity, this study seeks to contribute to the ongoing efforts to reduce the incidence of malnutrition associated with limited access and lower production of food grains.

Keywords

artificial intelligence; machine learning; networks with memory; generative models; smart farming; agri marketing

References

1. Talaviya T, Shah D, Patel N, et al. Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture 2020; 4: 58–73. doi: 10.1016/j.aiia.2020.04.002

2. Elmoulat M, Debauche O, Mahmoudi S, et al. Edge computing and artificial intelligence for landslides monitoring. Procedia Computer Science 2020; 177: 480–487. doi: 10.1016/j.procs.2020.10.066

3. Ampatzidis Y, Partel V, Costa L. Agroview: Cloud-based application to process, analyze and visualize UAV-collected data for precision agriculture applications utilizing artificial intelligence. Computers and Electronics in Agriculture 2020; 174: 105457. doi: 10.1016/j.compag.2020.105457

4. Su WH. Crop plant signaling for real-time plant identification in smart farm: A systematic review and new concept in artificial intelligence for automated weed control. Artificial Intelligence in Agriculture 2020, 4: 262–271. doi: 10.1016/j.aiia.2020.11.001

5. Heiden B, Alieksieiev V, Volk M, et al. Framing artificial intelligence (AI) additive manufacturing (AM). Procedia Computer Science 2021; 186: 387–394. doi: 10.1016/j.procs.2021.04.161

6. Talukdar S, Pal S, Singha P. Proposing artificial intelligence based livelihood vulnerability index in river islands. Journal of Cleaner Production 2021; 284: 124707. doi: 10.1016/j.jclepro.2020.124707

7. Singh H, Kumar Y. Hybrid artificial chemical reaction optimization algorithm for cluster analysis. Procedia Computer Science 2020; 167: 531–540. doi: 10.1016/j.procs.2020.03.312

8. Aghelpour P, Bahrami-Pichaghchi H, Kisi O. Comparison of three different bio-inspired algorithms to improve ability of neuro fuzzy approach in prediction of agricultural drought, based on three different indexes. Computers and Electronics in Agriculture 2020; 170: 105279. doi: 10.1016/j.compag.2020.105279

9. Riahi Y, Saikouk T, Gunasekaran A, et al. Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions. Expert Systems with Applications 2021; 173: 114702. doi: 10.1016/j.eswa.2021.114702

10. Yu H, Wen X, Li B, et al. Uncertainty analysis of artificial intelligence modeling daily reference evapotranspiration in the northwest end of China. Computers and Electronics in Agriculture 2020; 176: 105653. doi: 10.1016/j.compag.2020.105653

11. Sukhbaatar S, Szlam A, Weston J, Fergus R. End-to-end memory networks. In: Cortes C, Lawrence N, Lee D, et al. (editors). Advances in Neural Information Processing Systems 28, Proceedings of the Annual Conference on Neural Information Processing Systems; 7–12 December 2015; Montreal, Quebec, Canada. pp. 2440–2448.

12. Weston P, Hong R, Kaboré C, Kull CA. Farmer-managed natural regeneration enhances rural livelihoods in dryland West Africa. Environmental Management 2015; 55(6): 1402–1417. doi: 10.1007/s00267-015-0469-1

13. Soni N, Sharma EK, Singh N, et al. Artificial intelligence in business: From research and innovation to market deployment. Procedia Computer Science 2020; 167: 2200–2210. doi: 10.1016/j.procs.2020.03.272

14. Ahmadi F, Mehdizadeh S, Mohammadi B, et al. Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation. Agricultural Water Management 2021; 244: 106622. doi: 10.1016/j.agwat.2020.106622

15. Seyedzadeh A, Maroufpoor S, Maroufpoor E, et al. Artificial intelligence approach to estimate discharge of drip tape irrigation based on temperature and pressure. Agricultural Water Management 2020; 228: 105905. doi: 10.1016/j.agwat.2019.105905

16. Li X, Liu J, Liu D, et al. Measurement and analysis of regional agricultural water and soil resource composite system harmony with an improved random forest model based on a dragonfly algorithm. Journal of Cleaner Production 2021, 305: 127217. doi: 10.1016/j.jclepro.2021.127217

17. Debauche O, Mahmoudi S, Mahmoudi SA, et al. Edge computing and artificial intelligence for real-time poultry monitoring. Procedia Computer Science 2020; 175: 534–541. doi: 10.1016/j.procs.2020.07.076

18. Bhagat SK, Tung TM, Yaseen ZM. Development of artificial intelligence for modeling wastewater heavy metal removal: State of the art, application assessment and possible future research. Journal of Cleaner Production 2020; 250: 119473. doi: 10.1016/j.jclepro.2019.119473

19. Liu T, Sun Y, Wang C, et al. Unmanned aerial vehicle and artificial intelligence revolutionizing efficient and precision sustainable forest management. Journal of Cleaner Production 2021; 311: 127546. doi: 10.1016/j.jclepro.2021.127546

20. Kosovic IN, Mastelic T, Ivankovic D. Using Artificial Intelligence on environmental data from Internet of Things for estimating solar radiation: Comprehensive analysis. Journal of Cleaner Production 2020; 266: 121489. doi: 10.1016/j.jclepro.2020.121489

21. Maruthi SP, Panigrahi T, Jagannath RPK. Distributed version of hybrid swarm intelligence-Nelder Mead algorithm for DOA estimation in WSN. Expert Systems with Applications 2020; 144: 113112. doi: 10.1016/j.eswa.2019.113112

22. Anastasi S, Madonna M, Monica L. Implications of embedded artificial intelligence—Machine learning on safety of machinery. Procedia Computer Science 2021; 180: 338–343. doi: 10.1016/j.procs.2021.01.171

23. Varela N, Silva J, Pineda OB, et al. Prediction of the corn grains yield through artificial intelligence. Procedia Computer Science 2020; 170: 1017–1022. doi: 10.1016/j.procs.2020.03.080

24. Pathan M, Patel N, Yagnik H, et al. Artificial cognition for applications in smart agriculture: A comprehensive review. Artificial Intelligence in Agriculture 2020; 4: 81–95. doi: 10.1016/j.aiia.2020.06.001

25. Lu Y, Young S. A survey of public datasets for computer vision tasks in precision agriculture. Computers and Electronics in Agriculture 2020; 178: 105760. doi: 10.1016/j.compag.2020.105760

26. Zougagh N, Charkaoui A, Echchatbi A. Artificial intelligence hybrid models for improving forecasting accuracy. Procedia Computer Science 2021; 184: 817–822. doi: 10.1016/j.procs.2021.04.013

Refbacks

There are currently no refbacks.

This site is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Editor-in-Chief

Prof. Zhengjun Qiu

Zhejiang University, China

Honorary Editor-in-Chief

Cheng Sun

Academician of World Academy of Productivity Science; Executive Chairman, World Confederation of Productivity Science China Chapter, China

Processing Speed (2023)

- - - ＜7 days: submission to initial review decision;
  - - 56 days: received to online

News & Announcements

2024-09-11

Smart agriculture is becoming a reality!

Modern agricultural technology is evolving rapidly, with scientists collaborating with leading agricultural enterprises to develop intelligent management practices. These practices utilize advanced systems that provide tailored fertilization and treatment options for large-scale land management.

2024-05-01

Notice on the prohibition of using AI techniques to generate papers!

This journal values human initiative and intelligence, and the employment of AI technologies to write papers that replace the human mind is expressly prohibited. When there is a suspicious submission that uses AI tools to quickly piece together and generate research results, the editorial board of the journal will reject the article, and all journals under the publisher's umbrella will prohibit all authors from submitting their articles.

Readers and authors are asked to exercise caution and strictly adhere to the journal's policy regarding the usage of Artificial Intelligence Generated Content (AIGC) tools.

2024-01-14

We are recruiting Editorial Team Members

Advances in Modern Agriculture (AMA) is looking for members of the Editorial Board.

2023-12-28

Notice on the change of publication frequency

The publication frequency of Advances in Modern Agriculture is Quarterly since 2024, that means the journal will publish 4 issues per year from 2024 onwards.

More Announcements...

Journal Center

Asia Pacific Academy of Science Pte. Ltd. (APACSCI) specializes in international journal publishing. APACSCI adopts the open access publishing model and provides an important communication bridge for academic groups whose interest fields include engineering, technology, medicine, computer, mathematics, agriculture and forestry, and environment.

more

Article Tools

Indexing metadata

How to cite item

Review policy