Application of Fuzzy Logic in Plant Disease Management

The timely detection of the infection in plants and its severity is a major concern for the farmers. Although various techniques have been employed to identify and estimate the severity of infection, they generally use a fixed threshold to segment the infected areas from the leaf image. Such methods define the participation of a pixel, as part of the infected area, in the form of a classical or crisp set. Use of fuzzy logic in feature extraction, grading the disease post identification, and estimating the disease severity are seen as rapidly growing techniques. Using fuzzy logic, the infected area is calculated by considering the degree of contribution provided by neighboring pixels to the current pixel. The severity estimation is performed on the basis of the infected area and the number of lesions in the leaf image. Depending on the amount of infection, severity has been classified into early, middle, later, and advanced stage. The proposed technique will help the farmers to identify the disease class at an early stage.

Agricultural Health and Safety Measures by Fuzzy ahp and Prediction by Fuzzy Expert System

Farming is an ancient traditional business, but still it is not a profitable business sector due to risk factor attached to it. It is a high-risk business. Although profit is lucrative, loss rate is also high. Occupational safety is a big issue of discussion for agricultural workers. The methods of working in field in extreme climate (heat, rain) totally depends on environmental factors. Due to rain and droughts, the loss of profit impacts on economic condition and market. Extreme weather condition, heavy workload during their working procedure gives them early old age, bone and muscle problems. So to attain better efficiency of performance and to improve productivity of the worldwide farmers in the agricultural sector it is essential to minimize risk factors. Agricultural workers need sufficient precaution and safety measures at the time of field and machine work to minimize risk factors. Still risk is major discussion topic in agricultural business. So, an effort is taken to prioritize safety majors by fuzzy ahp, and prediction are done by fuzzy logic modelling.

Application of Fuzzy Expert System for Prediction of Farmer Muscle Strength

In this chapter, 168 anthropometric dimensions and the back-leg-chest (BLC) strength as the muscle strength of 113 male farmers and 31 female farmers of Odisha are statistically analyzed. Factor analysis is done to identify the most significant anthropometric dimensions. Then correlation coefficient and regression analysis are done considering the anthropometric dimensions and BLC strength. Further, an attempt is made by using ANFIS tool to predict the BLC strength of both male and female farmers. It is found that ANFIS could better predict the muscle strength of farmers.

A Predictive Fuzzy Expert System for Crop Disease Diagnostic and Decision Support

Soybean accounts for 38% of the total oilseed production in India, and around 50% of the total oilseed production in Kharif season. This crop has shown tremendous growth over the last four decades with an average national yield of 1264 kg/hectare. Currently, soybean is severely attacked by more than 10 major diseases. Yield losses due to different diseases ranges from 20 to 100%. Timely detection of soybean crop disease would help farmers save their money, effort, and crop from being destroyed. This chapter presents a case study on the development of a decision support system for prediction of soybean crop disease severity. The outcome of this system will aid farmers to decide the extent of disease treatment to be employed. Such predictions make use of human involvement, and thus are a source of ambiguities. To deal with such ambiguities in decision making, this decision support system uses fuzzy inference method based on triangular fuzzy sets.

Enhanced Fuzzy Assessment Methodology to Find Overlapping in Membership Function Using K Ratio to Find the Yield of Rice

Fuzzy expert systems are designed based on fuzzy logic and deal with fuzzy sets. Many fuzzy expert systems have been developed for diagnosis. Fuzzy expert systems are developed using fuzzification interface, enhanced fuzzy assessment methodology, and defuzzification interface. Fuzzification helps to convert crisp values into fuzzy values. By applying the enhanced fuzzy assessment methodology for rice, the yield parameters of rice can be diagnosed with number of tillers per hill, number of grains per panicle, and 1000 grain weight. Pest and disease incidence becomes simple for scientists. Enhanced fuzzy assessment methodology for rice uses triangular membership function with Mamdani's inference and K Ratio. Defuzzification interface is adopted to convert the fuzzy values into crisp values. Performance of the system can be evaluated using the accuracy level. Accuracy is the proportion of the total number of predictions that are correct. The proposed algorithm was implemented using MATLAB fuzzy logic tool box to construct fuzzy expert system for rice.

Adaptive Neuro-Fuzzy Inference System in Agriculture

This chapter emphasizes the use of adaptive fuzzy inference system (ANFIS) in agriculture. An overview of the basic concepts of ANFIS is provided at the beginning, where the underlying architecture of ANFIS is also discussed. The introduction is followed by the second section which highlights the diverse applications of ANFIS in agriculture during recent times. The third section describes how Matlab software can be utilized to build the ANFIS model. The fourth section describes the case study of the application of ANFIS for crop yield prediction. The conclusion follows this case study.

Diagnostic Analytics on Agriculture with Fuzzy Classification

Agriculture is the main domain and need of India. The country is second place in the world in agriculture. Cropping is the main part of agriculture. Various crops like millets, fruits, vegetables, oil seeds are produced and exported to other countries every year. So, various innovative technologies are used to improve the productivity of crops in agriculture. Rainfall is most important for growing crops. The water level for the crops based on rainfall has some uncertainty. Fuzzy regression analysis is one of the methods based on regression analysis that is used to handle fuzzy parameters and crisp data and vice versa. Linear fuzzy regression is one of the methods of fuzzy regression analysis to handle fuzzy parameters. This chapter explores fuzzy classification, which is based on fuzzy regression analysis, and it is compared with other classification algorithms on the agriculture data.

A Fuzzy-Based Sustainable Solution for Smart Farming

Agriculture is an important sector in many developing countries, but the traditional methods are not sufficient to produce a good amount of crop. Moreover, the natural calamities are also destroying a large portion of the crop. Hence, this chapter proposes a prototype model, AgriHelp, to address an agricultural issue using fuzzy logic. The model takes two parameters as input: when and where the farmer wants to sow the crop. Using this information along with available dataset, AgriHelp extracts the expected min-max temperature, rainfall and soil type in the region in the specified season and suggests the best-suited crop to the farmer. The model can further be extended by incorporating more features.

Subjective and Objective Assessment for Variation of Plant Nitrogen Content to Air Pollutants Using Machine Intelligence

In olden days, the plants used to tolerate and minimize the effect of air pollution caused by the then established industries and some automobiles. But in today's scenario, the rate at which plants and industries are rising doesn't match the count of trees. The plant survival and metabolism are based upon the nitrogen and chlorophyll available. There are several expensive methods to determine the chlorophyll and nitrogen content of the leaf like SPAD meter; the researchers have proposed a simple, inexpensive method that precisely determines the chlorophyll and nitrogen vales with a simple input RGB image. This chapter investigates the variation of content of plants in polluted environments and pollution-free environments.

Applications of Fuzzy Expert Systems in Farming

Agriculture, cattle breeding, and poultry farming constitute the backbone of the Indian economy. Today, India is ranked first worldwide in terms of milk production, second in terms of farm output, and third in terms of poultry output (eggs production). Over the year, agriculture, poultry farming, and cattle breeding have contributed towards India's GDP but is narrowly declining with the country's economic growth due to lack of initiatives. Fuzzy expert systems are used for various activities with an objective to get better results and good yield. Expert systems combine the experimental and experiential knowledge with the intuitive reasoning skills of a multitude of specialists to aid farmers in making the best decisions to improve the quality and increase the production. Weather and climatic changes play important roles. Thus, any changes in them affect the quantity and quality of production. Therefore, weather prediction plays an important role and helps the farmers to take right decisions and precautions to safeguard the production.