期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：11
DOI：10.14569/IJACSA.2020.0111180
出版社：Science and Information Society (SAI)
摘要：In this framework, the crop diseases have been identified using three types of methods, fuzzy-c as a clustering algorithm, runway scheduling trains like classification algorithm, and logistic regression as prediction algorithm. These techniques are meaningful solutions for losses in yields and the quantity of agriculture production. In this work, crop disease and corresponding fertilizers are predicted based on pattern scalability by the above algorithms. It proposes a Sensor Calibration and Feed Back Method (SCFM) with RWSA for better agriculture crop maintenance with automation and Fuzzy-c, Logistic regressions are helpful in studying the datasets of the crops for classifying the disease. This research tries to identify the leaf color, leaf size, disease of plant, and fertilizer for the illness of crops. In this context, RWSA-Agriculture gives the solution for current problems and improves the F1-Score. The data collected from local sensors and remote station is estimated with the dataset, these sensor based L.R., and Fuzzy-c controls disease prediction system in SCFM and RWSA. This technique accurately regulates the dispensing of water as well as chemicals; fertilizers for crop monitor and prevent the diseases of crops. This investigation gives performance metrics values i.e PSNR=44.18dB, SSIM = 0.9943, BPP =1.46, Tp=0.945 and CR = 5.25.
