期刊
COMPUTERS & ELECTRICAL ENGINEERING
卷 109, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compeleceng.2023.108739
关键词
ANN; CBR; Feature extraction; Fuzzy logic; Median filtering; Neuro-fuzzy; Smart farming
Agriculture is a crucial occupation that supports the world's population, and the loss in sugarcane production in India can be attributed to various factors. Farmers rely on local knowledge to identify plant diseases, but a more accurate and efficient solution is needed. This article proposes a smart farming system that utilizes machine learning and image processing to accurately diagnose sugarcane diseases and provide timely treatment.
Agriculture is one of the oldest occupations in the world and continues to exist today. In some form or another, the world's population depends on agriculture for its needs. The major loss in sugarcane production in India is due to pests, plant disease, malnutrition, and nutrient deficiency in plants. To identify these diseases, farmers go to local farmers, experts, agricultural people, and fellow neighbors to identify the problem caused. In some cases, their information may be adequate, but in others it is not. These people cannot solve all the problems caused by their crops can be solved by these people; there is a need to accurately predict the correct disease and provide the proper treatment at the right time. This can only be done by applying machine learning-based Internet of Things solutions in real time. This article proposes a method for a smart farming system to address the needs of farmers producing sugarcane in India by applying intelligent solutions that use image processing and soft computing. Four sugarcane diseases are investigated, such as Eyespot, Leaf Scald, Yellow Leaf, and Pokkah Boeng, and three characteristics such as color, shape, and texture. Images were used for training data in Artificial Neural Network (ANN), Neuro-Fuzzy, and Case-Based Reasoning (CBR) algorithms, and the performance of the feature extraction technique was evaluated in terms of sensitivity, specificity, F1 score, and accuracy.
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