A Fuzzy Inference System for Maize Plant Yield Prediction
Olajide Blessing Olajide1, Odeniyi Olufemi Ayodeji2, Olabiyi Olatunji Coker3, Adewale Joseph Adekunle4, Yakubani Yakubu5
1Olajide Blessing Olajide*, Federal University Wukari, Nigeria.
2Odeniyi Olufemi Ayodeji, Osun State College of Technology, Esa Oke, Nigeria.
3Olabiyi Olatunji Coker, Federal University Wukari, Nigeria.
4Adewale Joseph Adekunle, Osun State College of Technology, Esa Oke, Nigeria.
5Yakubani Yakubu, Federal University Wukari, Nigeria.
Manuscript received on September 10, 2021. | Revised Manuscript received on September 13, 2021. | Manuscript published on September 30, 2021. | PP: 90-96 | Volume-10 Issue-11, September 2021. | Retrieval Number: 100.1/ijitee.K949309101121 | DOI: 10.35940/ijitee.K9493.09101121
Open Access | Ethics and  Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Ascertaining infections in maize plants is through observation of the crop plant for visual indications which a farmer is able to relate to specific diseases. The perception of the farmer is prone to human error which may sometimes link some symptoms to the wrong disease and could impact the application of suitable preventive and curable routines to combat the identified diseases. Hence, accurate identification of crop plant disease is of high importance to a farmer to aid response to diseases. The objective of this article is to apply fuzzy set and interpolation technique to develop an expert system to carry out field-based identification and yield forecast for the maize plant. For this study, some associated factors were recognized for maize plant diseases and confirmed by a professional Botanists. For this study, a number of associated factors were identified for maize plant diseases and validated by experienced Botanists. Further to this, triangular membership functions was used to develop the fuzzy inference system model following the preprocessing of identified factors and related output. 32 inferred rules were formulated using IF-THEN statements which adopted the values of the factors as antecedent and the yield of maize plant as the consequent part of each rule for classification of the yield of maize plant. The Fuzzy model was simulated for each of the identified five factors. The simulation results showed that the risk factors identified; black moldy growth on kernels and ears, blights on leaves, rotten cobs, infected husks and black kernels and seed decay have noticeable influence on the maize plant yield if timely remedy is not administered. The study established that the utilisation of fuzzy technique is helpful to appraise the yield of maize such that the lesser the manifestation of identified associated features then the higher the yield of the maize plant.
Keywords: Disease, fuzzy logic, maize, yield.
Scope of the Article: fuzzy logic