International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 08 | Aug 2023
p-ISSN: 2395-0072
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Fuzzy Logic Modelling for Disease Severity Prediction in Cotton Crop using Weather Parameters Kiran Devi1, Vikash Siwach2, Manju Singh Tonk3, Anu4 1,2,3,4 Department of Mathematics & Statistics, COBS&H, CCS HAU, Hisar --------------------------------------------------------------------***---------------------------------------------------------------------Abstract - The relationship between weather parameters and disease severity has long been established. Disease severity
measurement in crops is a major challenge, as it plays a crucial role in yield estimation and determining the control factors necessary to enhance crop productivity. Mathematical models assist in disease severity assessment and management. Fuzzy logic models provide a flexible framework for dealing with imprecise and uncertain input, making them suited for modelling complicated relationships in real-world systems. Cotton leaf curl disease (CLCuD) is a viral disease that affects cotton plants and it is a significant threat to cotton production in many regions in India. In this study, a fuzzy logic system (FLS) based model has been proposed for prediction of disease severity based on potential weather variables such as temperature, relative humidity, sunshine and rainfall etc. which are indicators of disease severity. This model has been using comprehensive weather dataset and corresponding disease severity levels for model formulation, training and validation. Different performance indicators, including accuracy, precision, recall, and F1-score, were used to evaluate the model's predictive ability. Furthermore, sensitivity analysis was performed to identify the most influential weather parameters in disease severity prediction. The experimental results demonstrated that the developed FLS achieved significant accuracy in predicting disease severity based on weather parameters. The model exhibited the ability to capture complex non-linear relationships between weather conditions and disease severity, providing valuable insights for disease management.
Key words: Fuzzy logic system, Cotton leaf curl disease, Disease severity, Weather parameters, Sensitivity analysis etc.
1. INTRODUCTION A mathematical paradigm for thinking in the presence of uncertainty and ambiguity is fuzzy logic [1]. It's particularly helpful when dealing with complex and imprecise data that can't be expressed easily using standard binary logic. Fuzzy logic, rather than using firm true or false values, provides a mechanism to handle and manipulate this uncertain data. Control systems, pattern recognition, decision-making systems, expert systems, and automotive systems are all applications of fuzzy logic models. They're employed in a variety of applications, including industrial process control, robotics, image processing, voice recognition, and automobile control systems [2]. Fuzzy logic is a versatile and robust method for dealing with uncertainty and imprecision in a variety of fields. Agriculture has a crucial part in the Indian economy because it directly or indirectly supports more than half of the country's population [3]. The agricultural sector in India is noted for its size and production, which can be linked to the country's unique agro-climatic conditions. Despite its economic importance, farmers encounter numerous hurdles in increasing crop yields. Numerous efforts have been made to identify the primary causes causing poor crop productivity. Pest and disease modelling using diverse methodologies is one key approach to reducing the influence on agricultural yields [4]. Using cutting-edge techniques, agricultural forecasting approaches, including pest and disease forecasting, have been developed recently [5], [6]. In addition, internet-based forecasting tools and decision support systems have been introduced to successfully predict and manage crop diseases [7], [8]. Furthermore, advances in dispersal modelling and projected meteorology have aided in disease warnings and allowed farmers to adopt preventive actions [9], [10], [11]. These collaborative activities aim to assist Indian farmers in overcoming barriers and increasing crop yield in a sustainable manner. India is the world's second-largest producer of cotton, after China. Cotton has been cultivated in India for centuries, and it plays a significant role in the country's agricultural sector and economy [12]. The country accounts for about 25% of the global cotton production. The production of cotton in India has been steadily increasing over the years. In the 2020-2021 season, India produced approximately 30 million bales (each weighing 170 kilograms) of cotton. India cultivates various cotton varieties, including both hybrid and genetically modified (GM) cotton. Cotton cultivation in India faces several challenges, including pests and diseases, water scarcity, fluctuations in market prices, and the availability of quality seeds [13]. Pest management, irrigation facilities, and access to credit for farmers are areas of continuous focus for improving cotton production.
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