• Abstract

    As agriculture continues to evolve, there is an increasing demand for efficient as well as sustainable practices to ensure food security and optimize resource utilization. Agricultural image processing is revolutionized by the integration of machine learning techniques with conventional approaches and semantic segmentation has become a critical area of study attention. Performance in tasks like crop cover analysis, type identification and pest/disease detection has been improved by this synthesis. Using segmentation principles as a guide, this paper thoroughly examines current developments in conventional and machine learning-based semantic segmentation techniques for agricultural imagery. The conversation focuses on machine learning's effective features that can be used in concert with the original image data. There are acknowledged challenges with agricultural image segmentation, such as the requirement for improved generalization and resilience. The review tackles the lack of labelled samples by showcasing novel approaches such as dataset augmentation and multimodal information integration that improve machine learning techniques. This culminates in a valuable guide for using image semantically segmented in agricultural informatization.

  • References

    1. Anand T, Sinha S, Mandal M, Chamola V & Yu FR (2021) Agrisegnet: Deep Aerial Semantic Segmentation Framework For Iot-Assisted Precision Agriculture. IEEE Sensors Journal, 21(16), 17581-17590. DOI: 10.1109/JSEN.2021.3071290
    2. Arun RA, Umamaheswari S & Jain AV (2020) Reduced U-Net Architecture For Classifying Crop And Weed Using Pixel-Wise Segmentation. In 2020 IEEE International Conference For Innovation In Technology (INOCON) IEEE, 1-6. DOI: 10.1109/INOCON50539.2020.9298209
    3. Bais-Moleman AL, Schulp CJ & Verburg PH (2019) Assessing The Environmental Impacts Of Production-And Consumption-Side Measures. In Sustainable Agriculture Intensification In The European Union. Geoderma, 338, 555-567. DOI: 10.1016/J.Geoderma.2018.11.042
    4. Baker T, Whitehead B, Musker R & Keizer J (2019) Global Agricultural Concept Space: Lightweight Semantics For Pragmatic Interoperability. Npj Science Of Food 3(1), 16. DOI: 10.1038/S41538-019-0048-6
    5. Chávez‐Dulanto PN, Thiry AA, Glorio‐Paulet P, Vögler O & Carvalho FP (2021) Increasing The Impact Of Science And Technology To Provide More People With Healthier And Safer Food. Food And Energy Security, 10(1), E259. DOI:10.1002/Fes3.259
    6. Cheng J, Wang Q & Yu J (2022) Life Cycle Assessment Of Concentrated Apple Juice Production In China: Mitigation Options To Reduce The Environmental Burden. Sustainable Production And Consumption, 32, 15-26. DOI: 10.1016/J.Spc.2022.04.006
    7. Du Z, Yang J, Huang W & Ou C (2018) Training Segnet For Cropland Classification Of High Resolution Remote Sensing Images. In AGILE Conference.
    8. Gonçalves DN, De Moares Weber VA, Pistori JGB, Da Costa Gomes R, De Araujo AV, Pereira MF, Gonçalves WN & Pistori H (2021) Carcass Image Segmentation Using CNN-Based Methods. Information Processing In Agriculture, 8(4), 560-572. Https://Doi.Org/10.1016/J.Inpa.2020.11.004
    9. Islam M, Dinh A, Wahid K & Bhowmik P (2017) Detection Of Potato Diseases Using Image Segmentation And Multiclass Support Vector Machine. In 2017 IEEE 30th Canadian Conference On Electrical And Computer Engineering (CCECE) IEEE, 1-4. DOI: 10.1109/CCECE.2017.7946594
    10. Li D, Song Z, Quan C, Xu X & Liu C (2021) Recent Advances In Image Fusion Technology In Agriculture. Computers And Electronics In Agriculture, 191, 106491. DOI: 10.1016/J.Compag.2021.106491
    11. Li L, Zhang S & Wang B (2021) Plant Disease Detection And Classification By Deep Learning—A Review. IEEE Access, 9, 56683-56698. DOI: 10.1109/ACCESS.2021.3069646
    12. Li Y, Cao Z, Lu H, Xiao Y, Zhu Y & Cremers AB (2016) In-Field Cotton Detection Via Region-Based Semantic Image Segmentation. Computers And Electronics In Agriculture, 127, 475-486. DOI: 10.1016/J.Compag.2016.07.006
    13. Liu Q, Zhang Y, Chen J, Sun C, Huang M, Che M, Li C & Lin S (2023) An Improved Deeplab V3+ Network Based Coconut CT Image Segmentation Method. Frontiers In Plant Science, 14. DOI:10.3389%2Ffpls.2023.1139666
    14. Pérez-Pons ME, Parra-Domínguez J, Chamoso P, Plaza, M & Alonso R (2020) Efficiency, Profitability And Productivity: Technological Applications In The Agricultural Sector. ADCAIJ: Advances In Distributed Computing And Artificial Intelligence Journal, 9(4), 1-114. DOI:10.14201/ADCAIJ202094
    15. Riehle D, Reiser D & Griepentrog HW (2020) Robust Index-Based Semantic Plant/Background Segmentation For RGB-Images. Computers And Electronics In Agriculture, 169, 105-201. DOI: 10.1016/J.Compag.2019.105201
    16. Shaikh TA, Rasool T & Lone FR (2022) Towards Leveraging The Role Of Machine Learning And Artificial Intelligence In Precision Agriculture And Smart Farming. Computers And Electronics In Agriculture, 198, 107-119. DOI: 10.1016/J.Compag.2022.107119
    17. Sheng H, Chen X, Su J, Rajagopal R & Ng A (2020) Effective Data Fusion With Generalized Vegetation Index: Evidence From Land Cover Segmentation In Agriculture. In Proceedings Of The IEEE/CVF Conference On Computer Vision And Pattern Recognition Workshops, 60-61.
    18. Singh J & Kaur H (2019) Plant Disease Detection Based On Region-Based Segmentation And KNN Classifier. In Proceedings Of The International Conference On ISMAC In Computational Vision And Bio-Engineering 2018 (ISMAC-CVB) Springer International Publishing, 1667-1675. Https://Doi.Org/10.1007/978-3-030-00665-5_154
    19. Sodjinou SG, Mohammadi V, Mahama ATS & Gouton P (2022) A Deep Semantic Segmentation-Based Algorithm To Segment Crops And Weeds In Agronomic Color Images. Information Processing In Agriculture, 9(3), 355-364. DOI: 10.1016/J.Inpa.2021.08.003
    20. Surya T, Palanivel A & Bhuvaneswari R (2021) Super-Pixel Segmentation Based Skin Texture Pattern Recognition. In 2021 5th International Conference On Electronics, Communication And Aerospace Technology (ICECA) IEEE, 790-798. DOI:10.1109/ICECA52323.2021.9675925
    21. Tian Z, Wang JW, Li J & Han B (2021) Designing Future Crops: Challenges And Strategies For Sustainable Agriculture. The Plant Journal, 105(5), 1165-1178. DOI: 10.1111/Tpj.15107
    22. Turker M & Rahimzadeganasl A (2021) Agricultural Field Detection From Satellite Imagery Using The Combined Otsu’s Thresholding Algorithm And Marker-Controlled Watershed-Based Transform. Journal Of The Indian Society Of Remote Sensing, 49, 1035-1050. DOI: 10.1007/S12524-020-01276-4
    23. Xia M, Tian N, Zhang Y, Xu Y & Zhang X (2020) Dilated Multi-Scale Cascade Forest For Satellite Image Classification. International Journal Of Remote Sensing, 41(20), 7779-7800. Https://Doi.Org/10.1080/01431161.2020.1763511
    24. Yang A, Zhang K, Duan L, Wang J, Bai X & Yang J (2019) Estimation Method For SPAD Value Of Rice Leaves After Full Heading Based On RGB And HSV Color Space. Actaagriculturae Jiangxi, 31(8), 106-112. DOI: 10.19386/J.Cnki.Jxnyxb.2019.08.18
    25. Zemmour E, Kurtser P & Edan Y (2019) Automatic Parameter Tuning For Adaptive Thresholding In Fruit Detection. Sensors 19(9), 21-30. DOI: 10.3390/S19092130
    26. Zhang Z & Zhu L (2023) A Review On Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, And Applications. Drones, 7(6), 398. DOI:10.3390/Drones7060398

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How to cite

Kumar, S., Jayabalan, B., Acharjya, K., & Dheer, M. (2024). Current developments in machine learning for agriculture: Semantic division approaches and critical evaluation of farm image analysis. Multidisciplinary Reviews, 6, 2023ss062. https://doi.org/10.31893/multirev.2023ss062
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