The effects of treatment of chlorine dioxide （C1Oz） gas on postharvest physiology and preservation quality of green bell peppers were studied. Green bell peppers were collected in bags and treated with 0, 5, 10, 20,...The effects of treatment of chlorine dioxide （C1Oz） gas on postharvest physiology and preservation quality of green bell peppers were studied. Green bell peppers were collected in bags and treated with 0, 5, 10, 20, and 50 mg L^-1 ClO2 gas at 10±0.5℃ for over 40 d, and the changes in postharvest physiology and preservation quality of the peppers were evaluated during the storage. The inhibition of rot of the peppers was observed for all the tested ClO2 gas treatments. The rot rates of the treated samples were 50% lesser than those of the control after day 40 of storage. The highest inhibitory effect was obtained after 50 mg L^-1 ClO2 gas treatment, where the peppers did not decay until day 30 and showed only one-fourth of the rot rate of the control at day 40 of storage. The respiratory activity of the peppers was significantly （P〈0.05） inhibited by 20 and 50 mg L^-1 ClO2 treatments, whereas no significant effects on respiratory activity were observed with 5 and 10 mg L^-1 ClO2 treatments （P〉0.05）. Except for 50 mg L^-1 ClO2, malondialdenyde （MDA） contents in the peppers treated with 5, 10, or 20 mg L^-1 ClO2 were not significantly （P〉0.05） different from those in the control. Degradation of chlorophyll in the peppers was delayed by 5 mg L-1ClO2, but promoted by 10, 20, or 50 mg L^-1 ClO2. The vitamin C content, titratable acidity, and total soluble solids of the peppers treated by all the tested ClO2 gas did not significantly change during the storage. The results suggested that ClO2 gas treatment effectively delayed the postharvest physiological transformation of green peppers, inhibited decay and respiration, maintained some nutritional and sensory quality, and retarded MDA accumulation.展开更多
Increasing salinity of the groundwater is one of major challenges faced by agricultural sector in West Bank/Palestine. This study was carried out in the Lower Jordan Valley （LJV） under greenhouse field condition, wh...Increasing salinity of the groundwater is one of major challenges faced by agricultural sector in West Bank/Palestine. This study was carried out in the Lower Jordan Valley （LJV） under greenhouse field condition, where an area of 0.12 ha was irrigated with 3.5 dS/m magnetic treated water during the growing season 2012/2013. The results of this pilot project show that there are significant increases in the yield of red and yellow bell pepper of about 20% and 18% on fresh weight basis, respectively. Water use efficiency increased by 15% and an increase in shelf time of 7 d were also recorded. The chlorophyll content raised significantly in the leaves of treated plants compared to the controlled one by 2.5 mg/g. Bell pepper irrigated with magnetic water produces 37% more four champers than that of the controlled one. On the other hand, there were no clear significant effects on the height of the plant, number of fruits, distance between nods, size of fruits, number and thickness of walls and sugar contents. Applying visible/near infrared （VIS/N｜R） spectroscopy test shows that it is possible to distinguish between treated and controlled bell pepper fruits. Multivariate data analysis （MVDA） method was used to test the classification of chemical elements in the fruit and it was found that treated and controlled fruit samples are divided into two groups according to their water treatment. An increase in all nutrient concentrations was found in fruits irrigated with magnetic treated water compared with the controlled one. Further testing is needed especially by involving other variables such as decreasing the volume of irrigated water and fertilizers.展开更多
Bile acid binding potential of foods and food fractions has been related to lowering the risk of heart disease and that of cancer. Steam cooking has been observed to significantly improve bile acid binding of green/le...Bile acid binding potential of foods and food fractions has been related to lowering the risk of heart disease and that of cancer. Steam cooking has been observed to significantly improve bile acid binding of green/leafy vegetables. It was hypothesized that other cooking methods could further improve the bile acid binding of various vegetables. Sautée cooking resulted in in vitro bile acid binding measured on a dry matter basis relative to cholestyramine of 14% for mustard greens and kale, 9% for broccoli, 8% for collard greens, 6% for cabbage, and 5% for green bell pepper. These results point to the significantly different (P ≤ 0.05) health promoting potential of mustard greens = kale > broccoli > collard greens > cabbage > green bell pepper. Sautéing significantly improved in vitro bile acid binding of mustard greens, kale, broccoli, cabbage and green bell pepper compared with steaming, boiling or raw (uncooked). Collard greens exhibited significantly higher bile acid binding by steaming compared with sautéing, boiling or raw. Data suggest that the cooking method with most heath promoting potential for mustard greens, kale, broccoli, cabbage and green bell pepper should be sautéing. Steaming should be used for collard greens as the cooking method. These green/leafy vegetables, when consumed regularly after sautéing, would promote a healthy lifestyle and have the potential to lower the risk of premature degenerative diseases.展开更多
The uniformity of appearance attributes of bell peppers is significant for consumers and food industries.To automate the sorting process of bell peppers and improve the packaging quality of this crop by detecting and ...The uniformity of appearance attributes of bell peppers is significant for consumers and food industries.To automate the sorting process of bell peppers and improve the packaging quality of this crop by detecting and separating the not likable low-color bell peppers,developing an appropriate sorting system would be of high importance and influence.According to standards and export needs,the bell pepper should be graded based on maturity levels and size to five classes.This research has been aimed to develop a machine vision-based system equipped with an intelligent modelling approach for in-line sorting bell peppers into desirable and undesirable samples,with the ability to predict the maturity level and the size of the desirable bell peppers.Multilayer perceptron(MLP)artificial neural networks(ANNs)as the nonlinear modelswere designed for that purpose.TheMLP modelswere trained and evaluated through five-fold cross-validation method.The optimum MLP classifier was compared with a linear discriminant analysis(LDA)model.The results showed that the MLP outperforms the LDA model.The processing time to classify each captured image was estimated as 0.2 s/sample,which is fast enough for in-line application.Accordingly,the optimum MLP model was integrated with a machine vision-based sorting machine,and the developed system was evaluated in the in-line phase.The performance parameters,including accuracy,precision,sensitivity,and specificity,were 93.2%,86.4%,84%,and 95.7%,respectively.The total sorting rate of the bell pepper was also measured as approximately 3000 samples/h.展开更多
Conventional weed management approaches are inefficient and non-suitable for integration with smart agricultural machinery.Automatic identification and classification of weeds can play a vital role in weed management ...Conventional weed management approaches are inefficient and non-suitable for integration with smart agricultural machinery.Automatic identification and classification of weeds can play a vital role in weed management contributing to better crop yields.Intelligent and smart spot-spraying system's efficiency relies on the accuracy of the computer vision based detectors for autonomous weed control.In the present study,feasibility of deep learning based techniques(Alexnet,GoogLeNet,InceptionV3,Xception)were evaluated in weed identification from RGB images of bell pepper field.The models were trained with different values of epochs(10,20,30),batch sizes(16,32),and hyperparameters were tuned to get optimal performance.The overall accuracy of the selected models varied from 94.5 to 97.7%.Among the models,InceptionV3 exhibited superior performance at 30-epoch and 16-batch size with a 97.7%accuracy,98.5%precision,and 97.8%recall.For this Inception3 model,the type 1 error was obtained as 1.4%and type II error was 0.9%.The effectiveness of the deep learning model presents a clear path towards integrating them with image-based herbicide applicators for precise weed management.展开更多
The present work investigated the efficiency of leaf reflectance indices in the identification of Capsicum annuum L. var. annuum resistant to anthracnose in the fruit. Twenty-five F<sub>5:6</sub> families ...The present work investigated the efficiency of leaf reflectance indices in the identification of Capsicum annuum L. var. annuum resistant to anthracnose in the fruit. Twenty-five F<sub>5:6</sub> families originating from contrasting parents were assessed;the parents were accession UENF 2285 (susceptible to anthracnose) and accession UENF 1381, a hot pepper resistant to anthracnose in the fruit. The experiment was carried out in an experimental field in Campos dos Goytacazes, Rio de Janeiro, Brazil, between May and October of 2021. The treatments were arranged in a randomized block design, with three replications and five plants per plot. Fifteen LRIs were estimated using a CI-710 portable mini leaf spectrometer. The assessments covered all plant growth after flowering, and a total of six assessments were performed at 15-days intervals, beginning at 35 and ending 120 days after flowering (DAFs). Analysis of variance in a split-plot scheme was performed, as were tests of mean groupings and principal components analysis (PCA). The best period for evaluating leaf reflectance indices in C. annuum var. annuum is 120 days after flowering. The leaf reflectance indices PRI, CNDVI and Ctr2 stood out as effective in distinguishing between resistant and susceptible genotypes.展开更多
文摘The effects of treatment of chlorine dioxide （C1Oz） gas on postharvest physiology and preservation quality of green bell peppers were studied. Green bell peppers were collected in bags and treated with 0, 5, 10, 20, and 50 mg L^-1 ClO2 gas at 10±0.5℃ for over 40 d, and the changes in postharvest physiology and preservation quality of the peppers were evaluated during the storage. The inhibition of rot of the peppers was observed for all the tested ClO2 gas treatments. The rot rates of the treated samples were 50% lesser than those of the control after day 40 of storage. The highest inhibitory effect was obtained after 50 mg L^-1 ClO2 gas treatment, where the peppers did not decay until day 30 and showed only one-fourth of the rot rate of the control at day 40 of storage. The respiratory activity of the peppers was significantly （P〈0.05） inhibited by 20 and 50 mg L^-1 ClO2 treatments, whereas no significant effects on respiratory activity were observed with 5 and 10 mg L^-1 ClO2 treatments （P〉0.05）. Except for 50 mg L^-1 ClO2, malondialdenyde （MDA） contents in the peppers treated with 5, 10, or 20 mg L^-1 ClO2 were not significantly （P〉0.05） different from those in the control. Degradation of chlorophyll in the peppers was delayed by 5 mg L-1ClO2, but promoted by 10, 20, or 50 mg L^-1 ClO2. The vitamin C content, titratable acidity, and total soluble solids of the peppers treated by all the tested ClO2 gas did not significantly change during the storage. The results suggested that ClO2 gas treatment effectively delayed the postharvest physiological transformation of green peppers, inhibited decay and respiration, maintained some nutritional and sensory quality, and retarded MDA accumulation.
文摘Increasing salinity of the groundwater is one of major challenges faced by agricultural sector in West Bank/Palestine. This study was carried out in the Lower Jordan Valley （LJV） under greenhouse field condition, where an area of 0.12 ha was irrigated with 3.5 dS/m magnetic treated water during the growing season 2012/2013. The results of this pilot project show that there are significant increases in the yield of red and yellow bell pepper of about 20% and 18% on fresh weight basis, respectively. Water use efficiency increased by 15% and an increase in shelf time of 7 d were also recorded. The chlorophyll content raised significantly in the leaves of treated plants compared to the controlled one by 2.5 mg/g. Bell pepper irrigated with magnetic water produces 37% more four champers than that of the controlled one. On the other hand, there were no clear significant effects on the height of the plant, number of fruits, distance between nods, size of fruits, number and thickness of walls and sugar contents. Applying visible/near infrared （VIS/N｜R） spectroscopy test shows that it is possible to distinguish between treated and controlled bell pepper fruits. Multivariate data analysis （MVDA） method was used to test the classification of chemical elements in the fruit and it was found that treated and controlled fruit samples are divided into two groups according to their water treatment. An increase in all nutrient concentrations was found in fruits irrigated with magnetic treated water compared with the controlled one. Further testing is needed especially by involving other variables such as decreasing the volume of irrigated water and fertilizers.
文摘Bile acid binding potential of foods and food fractions has been related to lowering the risk of heart disease and that of cancer. Steam cooking has been observed to significantly improve bile acid binding of green/leafy vegetables. It was hypothesized that other cooking methods could further improve the bile acid binding of various vegetables. Sautée cooking resulted in in vitro bile acid binding measured on a dry matter basis relative to cholestyramine of 14% for mustard greens and kale, 9% for broccoli, 8% for collard greens, 6% for cabbage, and 5% for green bell pepper. These results point to the significantly different (P ≤ 0.05) health promoting potential of mustard greens = kale > broccoli > collard greens > cabbage > green bell pepper. Sautéing significantly improved in vitro bile acid binding of mustard greens, kale, broccoli, cabbage and green bell pepper compared with steaming, boiling or raw (uncooked). Collard greens exhibited significantly higher bile acid binding by steaming compared with sautéing, boiling or raw. Data suggest that the cooking method with most heath promoting potential for mustard greens, kale, broccoli, cabbage and green bell pepper should be sautéing. Steaming should be used for collard greens as the cooking method. These green/leafy vegetables, when consumed regularly after sautéing, would promote a healthy lifestyle and have the potential to lower the risk of premature degenerative diseases.
文摘The uniformity of appearance attributes of bell peppers is significant for consumers and food industries.To automate the sorting process of bell peppers and improve the packaging quality of this crop by detecting and separating the not likable low-color bell peppers,developing an appropriate sorting system would be of high importance and influence.According to standards and export needs,the bell pepper should be graded based on maturity levels and size to five classes.This research has been aimed to develop a machine vision-based system equipped with an intelligent modelling approach for in-line sorting bell peppers into desirable and undesirable samples,with the ability to predict the maturity level and the size of the desirable bell peppers.Multilayer perceptron(MLP)artificial neural networks(ANNs)as the nonlinear modelswere designed for that purpose.TheMLP modelswere trained and evaluated through five-fold cross-validation method.The optimum MLP classifier was compared with a linear discriminant analysis(LDA)model.The results showed that the MLP outperforms the LDA model.The processing time to classify each captured image was estimated as 0.2 s/sample,which is fast enough for in-line application.Accordingly,the optimum MLP model was integrated with a machine vision-based sorting machine,and the developed system was evaluated in the in-line phase.The performance parameters,including accuracy,precision,sensitivity,and specificity,were 93.2%,86.4%,84%,and 95.7%,respectively.The total sorting rate of the bell pepper was also measured as approximately 3000 samples/h.
文摘Conventional weed management approaches are inefficient and non-suitable for integration with smart agricultural machinery.Automatic identification and classification of weeds can play a vital role in weed management contributing to better crop yields.Intelligent and smart spot-spraying system's efficiency relies on the accuracy of the computer vision based detectors for autonomous weed control.In the present study,feasibility of deep learning based techniques(Alexnet,GoogLeNet,InceptionV3,Xception)were evaluated in weed identification from RGB images of bell pepper field.The models were trained with different values of epochs(10,20,30),batch sizes(16,32),and hyperparameters were tuned to get optimal performance.The overall accuracy of the selected models varied from 94.5 to 97.7%.Among the models,InceptionV3 exhibited superior performance at 30-epoch and 16-batch size with a 97.7%accuracy,98.5%precision,and 97.8%recall.For this Inception3 model,the type 1 error was obtained as 1.4%and type II error was 0.9%.The effectiveness of the deep learning model presents a clear path towards integrating them with image-based herbicide applicators for precise weed management.
文摘The present work investigated the efficiency of leaf reflectance indices in the identification of Capsicum annuum L. var. annuum resistant to anthracnose in the fruit. Twenty-five F<sub>5:6</sub> families originating from contrasting parents were assessed;the parents were accession UENF 2285 (susceptible to anthracnose) and accession UENF 1381, a hot pepper resistant to anthracnose in the fruit. The experiment was carried out in an experimental field in Campos dos Goytacazes, Rio de Janeiro, Brazil, between May and October of 2021. The treatments were arranged in a randomized block design, with three replications and five plants per plot. Fifteen LRIs were estimated using a CI-710 portable mini leaf spectrometer. The assessments covered all plant growth after flowering, and a total of six assessments were performed at 15-days intervals, beginning at 35 and ending 120 days after flowering (DAFs). Analysis of variance in a split-plot scheme was performed, as were tests of mean groupings and principal components analysis (PCA). The best period for evaluating leaf reflectance indices in C. annuum var. annuum is 120 days after flowering. The leaf reflectance indices PRI, CNDVI and Ctr2 stood out as effective in distinguishing between resistant and susceptible genotypes.
