Mohammad Effendy Ya’acob^1,*, Noor Fadzlinda Othman², Wan Amirah Wan Mansor³,
and Mohammad Amirul Faiz Zulkiply³

 

¹ Department of Process & Food Engineering, Hybrid Agrivoltaic System Showcase (HAVs), Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
² Department of Agricultural Technology, Faculty of Agricultural, Universiti Putra Malaysia,43400 Serdang, Selangor, Malaysia; Email: fadzlin013@gmail.com (N.F.O.)
³ Department of Biology, Faculty of Science, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia; Email: wanamirah52@gmail.com (W.A.W.M.); afzulaty9393@gmail.com (M.A.F.Z.)

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Abstract
Solar Photovoltaic (PV) largely depends on the sun irradiation or insolation level (in W/m2) on its surface for electricity conversion process. Cloud movement especially thick cloud near to earth surface creates a non-permanent shading on solar PV farms top surface which significantly reduce the electricity yield. Improper weed management in large scale solarfarms would create a permanent shading to the PV surface especially with creeping plants. Thus, this work implies freely available application namely Pl@ntNet and Canopeo to analyse the impacts of weed surface cover in Solarfarms. Images of specified weed growing above the solar PV surface are captured and identified using Pl@ntNet application to determine the type of weed. Weed identification is the first stage of efficient weed management to aid in a fundamental understanding of the life cycle and biology of the weeds for proper control measures. The same images are used in Canopeo application to determine the surface coverage by means of Quadrat sampling. This information will be invaluable to solarfarm operators showing the significant energy reduction when the solar PV surface are covered by weeds.

Keywords
Pl@ntNet Apps, canopeo apps, PV surface coverage, weed management, agrivoltaic system

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