Agricultural systems engineering (greenhouse, fish farming, mushroom production)
P. Shamsi Roodbarsar; S. R. Mousavi Seyedi; D. Kalantari; K. Ghasemi
Abstract
IntroductionIt is predicted that the world population will grow to 9.3 billion by 2050 and the urban population will increase by 73%, growing from 3.6 billion to 6.3 billion. This huge population requires abundant food production. A plant factory with artificial light (PFAL) is a closed growing system ...
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IntroductionIt is predicted that the world population will grow to 9.3 billion by 2050 and the urban population will increase by 73%, growing from 3.6 billion to 6.3 billion. This huge population requires abundant food production. A plant factory with artificial light (PFAL) is a closed growing system that is insulated against heat and air. The plants grow on shelves under horizontal artificial lighting. The main goal of PFAL is commercial plant production, but mini PFALs do not have commercial goals and are used to produce plants in small domestic sizes. Plants that are less than 30 cm tall, and grow well in relatively low light conditions and at high planting densities, are suitable for the plant factory. Therefore, plants such as rice, wheat, and potatoes are not suitable for cultivation in a plant factory.The main purpose of this research is to study the proper light quality for growing radish plants. All light treatments had a significant effect on biomass, sugar, and photosynthetic pigments of radish. The results showed that the highest amount of chlorophyll a was 0.964 mg g-1 fresh leaf weight and the lowest amount was 0.318 mg g-1 fresh leaf weight. For chlorophyll b, the highest value was 0.666 mg g-1 wet weight and the lowest value was 0.229 mg g-1 wet weight. The highest and lowest carotenoid contents were 74.75 mg g-1 and 30.6 mg g-1 wet weight, respectively. The highest sugar content was 0.717 μg g-1 dry weight and the lowest was 0.02 μg g-1 dry weight. The highest fresh and dry weights of the plant were 0.27 g and 0.014 g, respectively, while the lowest values recorded were 0.155 g and 0.007 g, respectively. In this study, plant length was also examined, but no significant difference was observed between different light treatments. Based on these findings, it can be concluded that the light composition (R2, G0, B1) was the most suitable light regime for use in the designed system.Materials and MethodsThe plant studied in this investigation was radish. The place of growth was a vertically built system consisting of four floors, each divided into two sections. A controller was required in each section to regulate parameters such as light time, temperature, and moisture. The controllers were designed using Fritzing software and built with parts and sensors like DHT 11, Arduino UNO based on ATMEGA328P, Relay module Arduino, data logging shield, and driver module RC. A programming platform like Arduino was used to write codes for controlling the remaining parameters. This study tested seven different light treatments, plus sunlight as a control, to investigate their effects on radish growth. The light treatments were developed by adjusting the number of three different lights: red, green, and blue. LEDs were installed after designing and constructing the m-PFAL system. Based on previous research conducted in this field, all LED lights were positioned above the shelves to ensure that the plants received an appropriate amount of light in a vertical orientation. Additionally, light reflectors were installed beside the plants to provide proper lighting for the lower leaves. The experimental design involved a completely randomized design with eight treatments and three replications, and all data analysis was conducted through SAS software. The average comparison was performed using the Duncan method at a probability of 1% and 5%.Results and DiscussionThe results indicate that the light regime (R2, G0, B1) resulted in the highest amount of chlorophyll "a", which was significantly different from both the control and other treatments. The treatment with the lowest amount of chlorophyll "a" was (R1, G0, B0), which did not differ significantly from the control or (R1, G1, B1). The treatment with the highest amount of chlorophyll "b" was (R2, G0, B1), which differed significantly from the control but not from (R2, G1, B0) or (R1, G0, B2). Using a mixed light treatment of blue and red resulted in higher amounts of photosynthesis pigments, especially when the red light was more prevalent. The treatment with the highest wet weight was (R2, G0, B1), which did not differ significantly from natural light. The treatment with the lowest wet weight was the just red light treatment, which was much lower than the other treatments. The dry weight of the radish was 4-6 percent of its wet weight, and the treatment with the highest dry weight was (R2, G0, B1), which did not differ significantly from (R0, G1, B2) or (R1, G0, B0). The treatment with the highest amount of sugar was (R2, G0, B1), which was significantly higher than other optical regimes used and natural light. Because the production of carbohydrates and sugar is directly related to photosynthesis, it can be concluded that the state of photosynthesis was most proper in the (R2, G0, B1) treatment.ConclusionThis study investigated the optimal light quality for the healthy and rapid growth of radish plants in a plant factory. LED lights can be an excellent alternative to natural light when there are limitations, such as in greenhouses or multi-floor plantings. The results show that the best light mixture was red and blue lights, with more red light than blue light, while the worst light regime was just red color, which had a negative effect on all parameters.
Precision Farming
M. Hashemi Jozani; H. Bagherpour; J. Hamzei
Abstract
Fractional vegetation cover (FVC) and normalized difference vegetation index (NDVI) are the most important indicators of greenness and have a strong correlation with green biomass. The objective of this study was to evaluate a hand-held GreesnSeeker (GS) active remote sensing instrument to estimate NDVI ...
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Fractional vegetation cover (FVC) and normalized difference vegetation index (NDVI) are the most important indicators of greenness and have a strong correlation with green biomass. The objective of this study was to evaluate a hand-held GreesnSeeker (GS) active remote sensing instrument to estimate NDVI and FVC in the spinach plant. In this study, the color indices of the G-B index and Excess Green (ExG) were used as color vegetation indices to discriminate leaves from soil background. During 28 to 44 days after emergence (DAG), the results showed good correlations between chlorophyll yield and NDVI (R = 0.61 to 0.91), and the correlation between NDVI of GS and biomass was significant. In addition, in this growth stage, the results showed a good coefficient of correlation between NDVI of GS and FVC (R = 0.67 to 0.82). In assessing the nitrogen rate on the NDVI of GS, the results showed significant differences only at the short period of growth stage (28 to 36 DAG). The results revealed that GreenSeeker performed well for estimation both chlorophyll and biomass yield of spinach crop and it could be used as a suitable instrument for estimation of leaf area index in the middle of the plant growth period.
Design and Construction
H. Biabi; S. Abdanan Mehdizadeh; M. Nadafzadeh; M. Salehi Salmi
Abstract
Introduction Leaf color is usually used as a guide for assessments of nutrient status and plant health. Most of the existing methods that examined relationships between chlorophyll status and carotenoid of leaf color were developed for particular species. Different methods have been developed to measure ...
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Introduction Leaf color is usually used as a guide for assessments of nutrient status and plant health. Most of the existing methods that examined relationships between chlorophyll status and carotenoid of leaf color were developed for particular species. Different methods have been developed to measure chlorophyll status and carotenoid. However, the high cost and difficulty to use have restricted their application, whereas the handheld chlorophyll meters such as the SPAD has become popular in the last decade for non-destructive measurement of chlorophyll content. SPAD meter readings have found to be related to the plant’s nutrition status, seed protein content, types of nodulation, and photosynthetic rates of leaves. Digital color (RGB) image analysis, another nondestructive technique is becoming increasingly popular with its potential in phenotyping various parameters of plant health status. The development of low-cost digital cameras that use charged-couple device (CCD) arrays to capture images offers an advantage of low-cost real-time monitoring process over optical sensor based SPAD meter. Gupta et al. (2012) estimated chlorophyll content, using simple leaf digital analysis procedure in parallel to a SPAD chlorophyll content meter. The chlorophyll content as determined by the SPAD meter was significantly correlated to the RGB values of leaf image analysis (RMSE = 3.97). The aim of this research is developing a new inexpensive, hand-held and easy-to-use technique for detection of chlorophyll and carotenoid content in plants based on leaf color. This method provides rapid analysis and data storage at minimal cost and does not require any technical or laboratory skills. Materials and Methods Sample collection In this research, 15 leaves were randomly selected from six types of plants (Shoeblackplant, Vitex, Spiderwort, Sacred fig, Vine and Lotus). Afterwards, the chlorophyll content of the leaf was measured in 3 different ways: 1) using a SPAD instrument; 2) using machine vision system (non-destructive method), and 3) laboratory test using a spectrophotometer. Chlorophyll and carotenoid content The chlorophyll content of the leaf was measured and recorded using SPAD chlorophyll meter (Hansatech, model CL-01, Japan) and spectrometer as explained by Dey et al. (2016). Furthermore, to measure the carotenoid content method described by Gitelson et al. (2006) was utilized. Image processing For estimation of chlorophyll using the image processing algorithm, sample images were taken using CCD (CASIO, model Exilim EX-ZR700, Japan) and transferred to the computer. The camera was mounted perpendicular to the horizontal plane at a fixed distance of 25 cm from the samples. In a consequence histogram of leaf, images were equalized and the average of each color channels from RGB, Lab, HSV, and I1I2I3 were extracted using Matlab 2016. Decision tree regression (DTR) algorithm To develop a regression model to predict chlorophyll and carotenoid content, two decision tree were constructed. The average of each color channels from RGB, Lab, HSV, and I1I2I3 become the predictor variables or feature vector and the real known chlorophyll and carotenoid content become the target variable or the target vector of each regression tree. To develop the regression models, dataset (90 observations) was split into training (60 observations) and test (30 observations) data. Results and Discussion According to the obtained results, a high correlation of 0.92 for chlorophyll and 0.85 for carotenoid was achieved, respectively, between the image processing method and the values measured by the spectrometer. Therefore, it can be said that the proposed image processing method has a desirable and acceptable performance for prediction of both chlorophyll content and carotenoid. The review points out a need for fast and precise leaf chlorophyll measurement technique. With this in mind, Dey et al. (2016) used image processing techniques to measure chlorophyll content. For the purpose of analysis of the proposed model, the model outcome was compared with the LEAF+ chlorophyll meter reading. Regression analysis proofed that there was a strong correlation between the proposed image processing technique and chlorophyll meter reading. Thus, it appears that the proposed image processing technique of leaf chlorophyll measurement will be a good alternative for measuring leaf chlorophyll rapidly and with ease. Conclusion In this research, collections of images from six divers plants (Shoeblackplant, Vitex, Spiderwort, Sacred fig, Vine and Lotus) were analyzed to predict chlorophyll and carotenoid content at different color spaces (RGB, Lab, HSV, and I1I2I3). Based on the results, it was shown that there were high correlations of 0.92 for chlorophyll content as well as 0.85 for carotenoid between the image processing method and the values measured by the spectrometer. Therefore, in general, it can be concluded that the proposed image processing method has a desirable and acceptable performance for prediction of chlorophyll content as well carotenoid.
B. Sepehr; H. Mohamadi-Monavar
Abstract
Introduction One of the most important factors in agricultural production is nitrogen which has a great impact on plant growing, yield performance and plant quality production. The optimum amount of nitrogen fertilizer is varied from fields to fields. There are some time consuming and costly ways to ...
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Introduction One of the most important factors in agricultural production is nitrogen which has a great impact on plant growing, yield performance and plant quality production. The optimum amount of nitrogen fertilizer is varied from fields to fields. There are some time consuming and costly ways to measure the nitrogen content of plants or soil, which are inappropriate for extended field or for a long growing season. Fast and remote optical sensors calculate greenness of plant using reflectance or absorbance of light from green leaves. Measuring chlorophyll with SPAD managed the nitrogen requirement for maize, Poinsettia and Nagoya Red. Whereas SPAD was not a suitable choice for chlorophyll measurement at the end of growing period. Therefore, GreenSeeker was applied as a non-contact to record the NDVI of tomato’s and cucumber’s leaves. The purpose of this research was the evaluation of GreenSeeker ability to estimate nitrogen requirement and then the plant health. Materials and Methods The study was performed on Matin and Nahid cultivars of tomato and cucumber, respectively. The pots were 291 and filled with 3 kg sieved soil. The bottom layer of each pot was filled with stone for better drainage. Before planting, the soil was analyzed in order to define the ingredients. All pots put in the greenhouse with polycarbonate structure in two floors. Measurements were repeated every week with SPAD and GreensSeeker and fertigation was started 50 days after planting (DAP). In order to provide other nutrient elements, all pots got Humic-acid at 37DAP and the effect was measured in 43rd DAP. Fertigation was continued until 71st DAP and first, second and third treatments were supplemented with extra fertilizer to reach the amount of fertilizer to fifth treatment. To calculate Total Nitrogen (TN), the concentrations of nitrate-N and nitrite-N are determined and added to the total Kjeldahl nitrogen. Chlorophyll meter (SPAD) and GreenSeeker optical sensor have become available for site-specific and need-based N management in greenhouse. The GS was located at 60 cm above the plant and measured the average NDVI. This sensor has red and NIR diodes which reflect and absorb the spectra in 660±15nm and 770±15nm regions, respectively. The SPAD values were recorded by inserting the middle portion of the index leaf in the slit of SPAD meter. As well as, chlorophyll meter can confirm the GreenSeeker output (NDVI). GreenSeeker is a suitable optical sensor because it is not affected by light and temperature variation or wind intensity. Statistical analyses were performed on the pooled data of both tomato and cucumber using Statistical Product and Service Solutions (SPSS). Regression equations were fitted between fertilizer and the readings recorded with different gadgets at different growth stages. Results and Discussion Chlorophyll content and NDVI of tomato and cucumber increased during the growing stages except in 71st DAP for cucumber. The percentage of total nitrogen of 1st, 2nd and 3rd treatments were further than two others because of supplementary fertilizer. According to the Kjeldahl result of cucumber, the 3rd treatment had the lowest nitrogen accumulation in fruits. In addition, chlorophyll and NDVI of cucumber almost showed the increasing correlation by fertilizer enhancement while the opposite behavior was seen for tomato. That would be related to different fertilizer needs of them. The linear regression of fertilizer and reading NDVI of 2nd to 5th treatments were ascending. The number of increasing leaves was calculated in all pots every weeks as another studied element. Each pot had new grown leaves every weeks that was more or sometimes less than last weeks. However, accurate correlation coefficient was reported with NDVI in all treatments, whereas chlorophyll did not show a direct relation. Conclusion The result of the study confirmed the useful GreanSeeker as an accurate and fast technology for prediction of NDVI. Among different fertilizer treatments of cucumber, 3rd one showed the acceptable results. Since tomatoes did not reach to fertility stage, it would not possible to extract the best nitrogen fertilizer treatments. It is obvious that evaluation of pots in complete growth stages reach us to codify manual fertilization.
J. Habibi Asl; L. Behbahani; A. Azizi
Abstract
Introduction Many vegetables such as mint are highly seasonal in nature. They are available in plenty at a particular period of time in specific regions that many times result in market glut. Due to perishable nature, huge quantity of vegetables is spoiled within a short period. The post-harvest loss ...
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Introduction Many vegetables such as mint are highly seasonal in nature. They are available in plenty at a particular period of time in specific regions that many times result in market glut. Due to perishable nature, huge quantity of vegetables is spoiled within a short period. The post-harvest loss in vegetables has been estimated to be about 30-40% due to inadequate post-harvest handling, lack of infrastructure, processing, marketing and storage facilities. Therefore, the food processing sector can play a vital role in reducing the post-harvest losses and value addition of vegetables which will ensure better remuneration to the growers. Drying is a common technique for preservation of food and other products; including fruits and vegetables. The major advantage of drying food products is the reduction of moisture content to a safe level that allows extending the shelf life of dried products. The removal of water from foods provides microbiological stability and reduces deteriorate chemical reactions. Also, the process allows a substantial reduction in terms of mass, volume, packaging requirement, storage and transportation costs with more convenience. Sun drying is a well known traditional method of drying agricultural products immediately after harvest. However, it is plagued with in-built problems, since the product is unprotected from rain, storm, windborne dirt, dust, and infestation by insects, rodents, and other animals. It may result in physical and structural changes in the product such as shrinkage, case hardening, loss of volatiles and nutrient components and lower water reabsorption during rehydration. Therefore, the quality of sun dried product is degraded and sometimes become not suitable for human consumption. For these reasons, to utilize renewable energy sources, reduce vegetable losses and increase farmers income, the current project has been conducted in the Agricultural Engineering Department of Khuzestan Agricultural Research Center during the years 2011-2013. Materials and Methods In this research an indirect cabinet solar dryer with three trays and grooved collector was constructed. To improve air convection, a chimney was mounted above the dryer. The dryer performance was evaluated by drying mint leaves in three levels of mass density of 2, 3, and 4 kg m-2 at two drying manners of natural and forced convection and compared with drying mint leaves in shade as the traditional method. Results and Discussion The results showed that total drying time required in different solar drier treatments was 3.5 to 15 h, while it was about 5 days in traditional method. Drying time in upper trays was more as the air flow decreased due to increase in mass density. Mean required drying time in forced convection was 29.7% less than that of natural convection. Maximum essences with 0.80% and 0.76% were belonged to "natural convection and 3kg m-2 mass density" and "forced convection and 4 kg m-2 mass density" treatments respectively, while minimum one with 0.30% was for "forced convection and 2 kg m-2 mass density" treatment. Also, the highest and lowest chlorophyll content with 8.51 and 4.18 mg ml-1 were measured in "natural convection and 3 kg m-2 mass density" and "forced convection and 4 kg m-2 mass density" treatments respectively. According to obtained results, 3 and 4 kg m-2 mass density can be suggested for natural and forced convection solar drying of mint leaves in Khuzestan condition respectively. Conclusion In order to reduce vegetable losses and increase Khuzestan vegetable producers income, indirect cabinet solar dryer for drying mint leaves in winter season, could be an appropriate option. For natural and forced convection drying methods, mass density of 3 and 4 kg m-2 is recommended respectively.
