Browsing by Author "Anaswara, S J."

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    Smart hydroponic system for indoor foliage plants
    (Department of Floriculture and Landscaping, College of Agriculture, Vellayani, 2025-01-24) Anaswara, S J.; Rafeekher, M
    The thesis work entitled “Smart hydroponic system for indoor foliage plants” was carried out at Department of Floriculture and Landscaping, College of Agriculture, Vellayani during 2021-2024. The study was conducted to standardize deep flow technique (DFT) and media culture technique in hydroponics, as well as to develop a smart hydroponic system for indoor foliage plants. The study comprised of three experiments. Syngonium podophyllum cv. White Butterfly was used for standardization of solution culture in DFT technique of hydroponics (experiment I) and standardization of media culture technique of hydroponics (experiment II). Two standard nutrient solutions were selected (s1- Hoagland solution and s2- Cooper’s solution) in four different nutrient doses (d1- 50%, d2- 100%, d3- 150% and d4- 200%) for DFT technique. The experiment was laid out in completely randomized design consisting of eight treatment combinations and a control (water) with ten replications. Hoagland solution + 150% nutrient dose (s1d3) exhibited superior performances across various growth parameters such as plant height, plant spread, number of leaves, leaf length, leaf breadth, petiole length and leaf area. Plant sample analysis also revealed the highest nitrogen, calcium, magnesium, iron, boron and chlorophyll content in s1d3 combination, while, s1d4 combination resulted in highest leaf area index, number of roots, plant fresh weight and dry weight characteristics. Longest roots were produced by s1d2 and s2d1 combinations. Four different soilless media (m1- metaljelly, m2- vermiculite, m3- expanded clayballs and m4- quartz sand) were selected for media culture technique. Four different nutrient solution application intervals were also given (a1- daily, a2- alternate day, a3- two days interval and a4- three days interval). The experiment was laid out in completely randomized design with sixteen treatment combinations with five replications. The best treatment from experiment I (Hoagland solution +150% nutrient dose application) was selected as nutrient solution in this experiment. The treatment combination m3a2 (expanded clay ball media + alternate day nutrient solution application) exhibited superior performances across growth parameters such as plant height, plant spread, leaf length, leaf breadth, petiole length, leaf area, number of roots, root length, fresh weight and dry weight. Plant sample analysis also resulted in highest nitrogen content, manganese content, zinc content, copper content and chlorophyll content for m3a2 combination. m2a1 combination resulted in production of highest number of leaves. Third experiment was to standardize hydroponic technique in different indoor foliage 350 plants using automated system. Other than Syngonium podophyllum cv. White Butterfly, aquascaping plants such as Alternanthera reineckii, Anubias gracillis, Cryptocoryne wendttii were used. There were two treatments (best treatment from experiment I and II) in ten replications and comparison within the plants was done using t-test. A smart hydroponic DFT system was developed using pH, Ec and water level indicator sensors. Smart hydroponic media culture was set up using moisture sensors and weight sensors. An arudino mega 2560 was used as micro controller board in both the systems. Levels for pH and Ec were set up as 5.5-6.5 and 1.00 respectively and water level as 75% in DFT system. Moisture levels in media culture was set up as 70%. Google spread sheet integration was done in both the systems for getting observations in every 15 minutes through Wi-Fi connection. Syngonium podophyllum cv. White Butterfly exhibited superior performances for parameters such as plant spread, number of leaves per plant, petiole length, leaf area, number of roots, root length, calcium content, iron content, copper content and boron content under smart hydroponic DFT technique. Alternanthera reineckii recorded the highest plant height, plant spread, number of leaves per plant, leaf length, leaf breadth, petiole length, leaf area, number of roots, root length, plant wet weight, plant dry weight, calcium content, manganese content, zinc content and chlorophyll content under smart hydroponic DFT technique. Anubias gracillis also recorded significantly highest plant spread, leaf length, leaf breadth, petiole length, leaf area, plant fresh weight, nitrogen content, calcium content, iron content, manganese content, zinc content and chlorophyll content under smart hydroponic DFT technique. Cryptocoryne wendttii exhibited superior performances for the growth parameters plant spread, number of leaves per plant, leaf length, leaf breadth, petiole length, leaf area, root length, plant fresh weight, plant dry weight, nitrogen content, phosphorous content, potassium content, calcium content, magnesium content, iron content, manganese content, zinc content, boron content and chlorophyll content under smart hydroponic media culture technique. A crop simulation model “SyCSM” was developed using the result from experiment I (leaf area index, petiole length and root length) and weather parameters during the period (maximum temperature (0C), minimum temperature (0C), relative humidity (%), sunshine hours (h) and solar radiation (mj/m2)) for predicting the plant parameters according to change in the environmental parameters in Syngonium podophyllum cv. White Butterfly. Unlike an independent situation, the applicability under the treatment combinations has been investigated using data generated from the experiment. The model performed better for all the treatment combinations. Model performance was assessed by D- index and normalized 351 objective function (NOF) analysis. D- index are 0.991-0.999 for leaf area index, 0.924-0.997 for petiole length and 0.661-0.921 for root length. A convolution neutral network (CNN) model was developed to classify leaf area index into different classes ranging 1-2, 2-3, 3-4 and 4-5. The model consisted of nine layers with prediction accuracy 70.47% for class 1-2, 65.83% for class 2-3, 67.90% for class 3-4 and 73.13% for class 4-5. Another attempt was made to calculate leaf area index from results driven from CNN model and it accurately predicted the actual leaf area index of the plant. Improvement and application of the developed smart hydroponic technique in other ornamental plants and extension of applicability of simulation model and leaf area classification to other varieties of syngonium in varying growth periods and situations may be done in future.