Application of fuzzy logic for evaluating the influence of panchagavya on tomato yield in grow bags: Fuzzy logic for tomato yeild

Sathya, A.; Jayalalitha, S.; Sabitha, R.; Swetha, R.; Brintha, T.

doi:10.1109/icpcsi.2017.8392086

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…In future, this work will be extended and implemented for different othe sensitive crops, vegetables and fruits to reduce human intervention in continuously monitoring of the crop and soil conditions. On/off conditions-based motorized system controller by fuzzy logic making use of the soil sensor and moisture sensor Sathya et al (2017) In this, fuzzy model is used for tomato crop which considered the effect of nutrient when grow in bags. Suruthi et al (2018) In this, ANFIS based model has utilized the environmental conditions such as temperature, humidity, soil moisture and weather condition to study the time for irrigation.…”

Section: Discussionmentioning

confidence: 99%

“…For the fulfilment of agricultural needs, the electric motor and tube well are taken as predictors to determine the level of water and time (Tayyaba et al, 2015). The yield of tomato crop has been described taking the effect of nutrient into consideration (Sathya et al, 2017). An adaptive neuro-fuzzy inference system (ANFIS)-based model has utilized the environmental conditions such as temperature, humidity, soil moisture and weather condition to study the time for irrigation (Suruthi et al, 2018).…”

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confidence: 99%

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Design of decision model for sensitive crop irrigation system

et al. 2022

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Agriculture Industry is highly dependent on environmental and weather conditions.Many times, crops are spoiled because of sudden changes in weather. Therefore, we need a decision model to take care the water requirement of sensitive crops of agriculture industry. The proposed work presents a novel and proficient hybrid model for sensitive crop irrigation system (SCIS). For implementation of the model, brassica crop is taken. The duration and amount of water to be supplied is based upon the weather prediction and soil condition information. The decision model is developed using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) for brassica crops. In this model, if the input data values are available in range, then ANFIS model would be preferred and if the data sets are available for training, testing and validation then ANN model would be the best choice. The soil moisture, soil status in terms of temperature and leaf wetness are the input and flow control of sprinklers is the out for SCIS. The predicted outputs are analysed to assert the suitability of the proposed approach in the brassica crops. The proposed SCIS achieved an accuracy of 91% and 99% for ANFIS and ANN models respectively.

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Design of decision model for sensitive crop irrigation system

et al. 2022

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“…This paper intents to propose an alternative to the full-time professional companion to crop monitoring process through an early identification model based on Image sampling for Xanthomonas campestris disease in green bean leaves. To achieve that goal, through the performance in terms of the mean [11], and optimized with heuristic (genetic) [12] and exact (Quasi-Newton) [13] algorithms, are evaluated. Classification variables are the ones defined in the RGB scales to assess each pixel in different layers [14,15].…”

Section: Introductionmentioning

confidence: 99%

Optimization techniques on fuzzy inference systems to detect Xanthomonas campestris disease

Velandia

Calderón

Lara

2021

IJECE

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This paper shows the outcomes for four optimization models based on fuzzy inference systems, intervened using Quasi-Newton and genetic algorithms, to early assess bean plants’ leaves for Xanthomonas campestris disease. The assessment on the status of the plant (sane or ill) is defined through the intensity of the color in the RGB scale for the data-sets and images to analyze the implementation of the models. The best model performance is 99.68% when compared with the training data and a 94% effectiveness rate on the detection of Xanthomonas campestris in a bean leave image. Therefore, these results would allow farmers to take early measures to reduce the impact of the disease on the look and performance of green bean crops.

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Fuzzy Interface Automatic Brassica Horticulture Hoop House

Aggarwal

Thakur

2019

2019 6th International Conference on Signal Processing and Integrated Networks (SPIN)

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Application of fuzzy logic for evaluating the influence of panchagavya on tomato yield in grow bags: Fuzzy logic for tomato yeild

Cited by 3 publications

References 4 publications

Design of decision model for sensitive crop irrigation system

Design of decision model for sensitive crop irrigation system

Optimization techniques on fuzzy inference systems to detect Xanthomonas campestris disease

Fuzzy Interface Automatic Brassica Horticulture Hoop House

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