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prediction of chlorophyll content of tomato plant by artificial neural networks and adaptive nero-fuzzy inference system
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نویسنده
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rasooli sharabiani vali ,kisalaei asma ,taghinezhad ebrahim
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منبع
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مكانيزاسيون كشاورزي - 1400 - دوره : 6 - شماره : 3 - صفحه:59 -65
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چکیده
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Approximately three-quarters of harvested tomatoes are freshly used. good quality is an important factor in distributing of fresh tomato. chlorophyll is the green chemicals to provide required food of plants and ensure plant growth and productivity. the main function of chlorophyll is to absorb blue and red lights and perform photosynthesis. in recent years, the tendency to use of prediction methods such as soft computing and artificial intelligence for growth of plans has increased. the main aim of this study was to investigate the relationship between height and chlorophyll content in the leaves of tomato plants using modeling and predicting techniques and compare the accuracy of these methods. in this study, some cultivated plants of tomato were randomly selected for height and spad measurements. the results showed the relationship between chlorophyll content and height of plants was very low (r2 = 0.276). however using the modelling of ann and anfis improved the prediction power up to (r2=0.982 and 0.913), respectively.
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کلیدواژه
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anfis ,chlorophyll content ,modeling ,neural networks ,tomato
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آدرس
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university of mohaghegh ardabili, faculty of agriculture and natural resources, department of biosystems engineering, iran, university of mohaghegh ardabili, faculty of agriculture and natural resources, department of biosystems engineering, iran, university of mohaghegh ardabili, moghan college of agricultural and natural resources, department of agricultural technology engineering, iran
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پست الکترونیکی
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e.taghinezhad@uma.ac.ir
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prediction of chlorophyll content of tomato plant by artificial neural networks and adaptive nero-fuzzy inference system
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Authors
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rasooli sharabiani vali ,kisalaei asma ,taghinezhad ebrahim
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Abstract
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approximately three-quarters of harvested tomatoes are freshly used. good quality is an important factor in distributing of fresh tomato. chlorophyll is the green chemicals to provide required food of plants and ensure plant growth and productivity. the main function of chlorophyll is to absorb blue and red lights and perform photosynthesis. in recent years, the tendency to use of prediction methods such as soft computing and artificial intelligence for growth of plans has increased. the main aim of this study was to investigate the relationship between height and chlorophyll content in the leaves of tomato plants using modeling and predicting techniques and compare the accuracy of these methods. in this study, some cultivated plants of tomato were randomly selected for height and spad measurements. the results showed the relationship between chlorophyll content and height of plants was very low (r2 = 0.276). however using the modelling of ann and anfis improved the prediction power up to (r2=0.982 and 0.913), respectively.
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Keywords
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anfis ,chlorophyll content ,modeling ,neural networks ,tomato
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