CN115615987B - Fruit harvesting and classifying method integrating appearance quality and internal quality detection - Google Patents
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Abstract
The invention discloses a fruit harvesting and classifying method integrating appearance quality and internal quality detection, which considers that the conditions of different time sections, harvesting equipment, orchard management and the like of fruits are different in actual conditions when harvesting and classifying, and can cause variation conditions of indexes in a decision scheme of harvesting and classifying, so that different decision schemes of fruit harvesting and classifying are optimally subjected to fuzzy decision recognition by aiming at the related indexes of the appearance quality and the internal quality, and the evaluation conditions corresponding to the decision schemes of fruit harvesting and classifying and the evaluation results for determining the fruit harvesting and classifying are given to guide fruit harvesting and classifying work.
Description
Technical Field
The invention belongs to the technical field of fruit postharvest treatment, and particularly relates to a method for harvesting and classifying fruits integrating appearance quality and internal quality detection based on multi-objective multi-level system multi-dimensional fuzzy decision.
Background
The harvesting, grading, packaging and transportation of fruits and vegetables are the work to be done before entering into formal storage or marketing, and the commercial treatment after harvesting and harvesting mainly has two effects on fruits. On one hand, the quality and the storability of fruits and vegetables are affected, such as proper-period harvesting, grading and selecting after harvesting, reasonable packaging, pest and disease control after harvesting, timely precooling after harvesting, reasonable stacking and the like, can have a certain influence on the storage and transportation of fruits and vegetables, and on the other hand, the commodity property of fruits and vegetables is affected. In the commodity circulation field, the uniformity of size and specification is required. The appearance is beautiful, and the package is exquisite and proper. Ensure shelf time. This requires grading and packaging of the fruit according to certain criteria after harvesting. Some fruits have beautiful appearance and prolonged shelf life, and are also required to be cleaned and waxed to improve commodity properties of the fruits. Harvesting is the last link of fruit and vegetable production and also the beginning of post-harvest treatment. The harvesting maturity of fruits and vegetables has close relation with the yield, quality and storage characteristics. The judgment of the maturity in production is generally comprehensively considered according to factors such as different varieties, biological characteristics, growth conditions, climate conditions, cultivation management and the like. At the same time, the suitable harvest time is determined from the aspects of regulating market supply, storage, transportation and processing requirements, labor arrangement and the like.
Fruit quality mainly includes external quality and internal quality. External qualities include fruit size, shape, color, odor, etc. The internal quality includes hardness, soluble solids, sugar degree, acidity, vitamins and other components, and the internal browning, freeze injury, insect damage, hollowness and other indexes of the fruit are invisible. The nondestructive detection of fruit quality refers to a detection process for analyzing and acquiring fruit quality by utilizing physical characteristics of fruits such as sound, electricity, light, magnetism and the like and comprehensive detection technologies such as spectral imaging, dielectric characteristics, nuclear magnetic resonance and the like on the premise of not damaging a detection object. In recent years, nondestructive detection technology of fruit quality is mature and perfect, a detection instrument is developed towards portability and intellectualization, detection indexes are changed from single indexes to multiple indexes for simultaneous detection, and accuracy, reliability and timeliness of detection results are gradually improved, so that the nondestructive detection instrument plays a greater promotion role in fruit industry industrialization.
Disclosure of Invention
The invention aims at carrying out optimal fuzzy decision recognition on different decision schemes of fruit harvest classification aiming at the related indexes of the appearance quality and the internal quality of the set, thereby providing the evaluation grade corresponding to the decision scheme of fruit harvest classification to guide fruit harvest classification work.
In order to achieve the above object, the present invention includes the following:
Let the fruit harvest classification decision set be Γ= { η j |1.ltoreq.j.ltoreq.j }, where η j is the J-th fruit harvest classification decision scheme integrating appearance quality and internal quality detection, J is the number of decision schemes, and is defined by C 1 (size), C 2 (shape), C 3 (colour), C 4 (odour), C 4 (hardness), C 5 (soluble solids), C 6 (sugar degree), C 7 (acidity), C 8 (internal browning), C 9 (freeze injury), C 10 (insect pest), C 11 (hollow) and the like to construct an index set c= { C 1,...,C11 } of the decision set Γ, then dividing C 1,...,C4 into appearance quality B 1, Dividing C 5,...,C11 into internal qualities B 2, thereby constructing a gatepost set b= { B 1,B2 }, from the appearance quality B 1 and the internal quality B 2; Because the actual conditions are different when harvesting and classifying are carried out according to the conditions of different time sections, harvesting equipment, orchard management and the like of fruits, the variation condition exists in the index in the decision scheme of the harvesting and classifying, so that different decision schemes of the fruit harvesting and classifying can be divided into H=4 different evaluation grades as decision evaluation sets D in the use process according to the actual demands of sales, storage, processing and the like, and the decision sets D can be expressed as:
d= { D 1 (preferred use), D 2 (recommended use), D 3 (barely used), D 4 (not recommended) } (1)
The data characteristic value matrix of the fruit harvest classifying index corresponding to the category k decision j index I can be expressed as (kxi,j)I×J, wherein 1 is less than or equal to 2,1 is less than or equal to 11, kxi,j is the characteristic value of the category k decision j index I, kri,j is the normalized value of the characteristic value kxi,j of the category k decision j index I, ksi,h is the target value of the standard decision mode of the category k evaluation grade h, wherein 1 is less than or equal to 4, kuh,j is the relative membership degree of the characteristic value of the evaluation grade h to which the decision j belongs and kwi,j Decision of target weight of j index i for class k and/>The method can establish a system comprising Lagrangian constant/>, according to a multi-level system multi-dimensional fuzzy decision theory, for obtaining optimal fuzzy decision recognition under different scenes corresponding to fruit harvesting classification decision schemes integrating appearance quality and internal quality detectionAnd a minimization objective function of xi/>
For a pair ofThe first and second derivatives are obtained with respect to kuh,j,kwi,j:
When (when) When in use, it is obvious thatIt can be seen that kuh,j,kwi,j is present to makeIf there is a minimum, the objective function/>, is minimizedThere is a solution.
kuh,j Available from formula (3) as 0Is represented by the expression:
From (7) and Availability/>Is represented by the expression:
Then the theoretical value expression of kuh,j can be obtained from formulas (7) and (8):
The expression kwi,j for ζ is obtainable from equation (4) being 0:
From (9) and Availability/>Is represented by the expression:
Then the theoretical value expression of kwi,j can be obtained from formulas (9) and (10):
Theoretical value expressions of kuh,j and kwi,j can be substituted into the iterative process shown in fig. 1 to give actual values ku'h,j and kw'i,j corresponding to kuh,j and kwi,j, and to give fruit harvest classification decision schemes for preferential, recommended and marginal use according to the maximum membership principle to give an evaluation result of fruit harvest classification:
Step 1: kxi,j、kri,j and ksi,h can be given out through training set data corresponding to different indexes in the fruit harvesting and classifying process and historical data of a harvesting and classifying decision scheme in the initializing process, if the historical data of the harvesting and classifying decision scheme is not available, all indexes including appearance quality 2C1 and internal quality 2C2 of the harvesting and classifying decision scheme are set, the target weight kwi,j (t) of the index I of the category k decision j is 1/I, and the iteration times t is set to be 0;
step 2: the iteration times t are replaced by t+1, kxi,j、kri,j、ksi,h and kwi,j (t-1) are substituted into a theoretical value expression of kuh,j so as to output kuh,j (t);
Step 3: substituting kxi,j、kri,j、ksi,h and kuh,j (t) into the theoretical value expression of kwi,j to output kwi,j (t);
Step 4: if both conditions | [ kuh,j(t)-kuh,j(t-1)]/kuh,j(t)|≤δu and | [ kwi,j(t)-kwi,j(t-1)]/kwi,j(t)|≤δw ] are satisfied, wherein δ u and δ w are iterative process change thresholds of kuh,j and kwi,j, respectively, kuh,j (t) and kwi,j (t) are output as actual values ku'h,j and kw'i,j, thereby constructing (ku'h,j)H×J and kw'i,j)I×J, otherwise step 2 is entered;
Step 5: according to the decision evaluation set D= { D 1,D2,D3,D4 }, a multi-dimensional fuzzy decision matrix of the category k (ku'h,j)H×J can obtain a fruit harvesting classification decision set E h＝{eh＝j|ku'h,j≥εh corresponding to the evaluation level h, wherein E h is an element in the fruit harvesting classification decision set E h, epsilon h is a decision threshold of the evaluation level h, and then the corresponding fruit harvesting classification decision can be selected to be used according to the order of priority to barely using of the evaluation level h;
Step 6: if the fruit harvest classification decision given in step 5 is j ', a weight vector of appearance quality is constructed from the target weights kwi,j′ of the corresponding category k decision j ' index i (1wi,j′)1×4 and the weight vector of internal quality (2wi,j′)1×7, and the normalized values of the eigenvalues kxi,j′ of the category k decision j ' index i are counted to establish (1ri,j′)1×4 and (2ri,j′)1×7, the evaluation result for determining fruit harvest classification can be expressed as:
Wherein, Using the M (+, ·) calculation mode, ρ is the quality weight.
Drawings
Fig. 1: fruit harvesting and classifying iterative process diagram integrating appearance quality and internal quality detection
Fig. 2: normalized value chart for appearance quality test of Newhol navel orange fruit
Fig. 3: normalized value chart for internal quality test of Newhol navel orange fruit
Fig. 4: neohol navel orange harvesting classification evaluation result graph
Detailed Description
The following will describe in detail the technical solution provided by the present invention in connection with a specific embodiment of fruit harvesting classification integrating appearance quality and internal quality detection by navel orange of new-holl variety, specifically including the following:
Let the Neohol navel orange harvesting classification decision set be Γ= { eta j |1.ltoreq.j.ltoreq.J }, wherein eta j is the J-th Neohol navel orange harvesting classification decision scheme integrating appearance quality and internal quality detection, the number of the past decision schemes is provided with J=10, and the number is provided with C 1 (size), C 2 (shape), C 3 (colour), C 4 (odour), C 4 (hardness), C 5 (soluble solids), C 6 (sugar degree), C 7 (acidity), C 8 (internal browning), C 9 (freeze injury), C 10 (insect pest), C 11 (hollow) and the like to construct an index set c= { C 1,...,C11 } of the decision set Γ, then dividing C 1,...,C4 into appearance quality B 1, Dividing C 5,...,C11 into internal qualities B 2, thereby constructing a gatepost set b= { B 1,B2 }, from the appearance quality B 1 and the internal quality B 2; Different decision schemes for harvesting and classifying the neoil navel orange according to actual demands such as sales, storage and processing can be divided into H=4 different evaluation grades in the use process to be used as a decision evaluation set D, and the decision evaluation set D can be expressed as:
d= { D 1 (preferred use), D 2 (recommended use), D 3 (barely used), D 4 (not recommended) }
The data characteristic value matrix of the Newhall navel orange harvesting classification index corresponding to the category k decision j index I can be expressed as (kxi,j)I×J), wherein 1 is less than or equal to 2,1 is less than or equal to 11, kxi,j is the characteristic value of the category k decision j index I, kri,j is the normalized value of the characteristic value kxi,j of the category k decision j index I, ksi,h is the target value of the standard decision mode of the category k evaluation grade h, wherein 1 is less than or equal to 4, kuh,j is the relative membership degree of the characteristic value of the evaluation grade h to which the decision j belongs and kwi,j Decision of target weight of j index i for class k and/>The optimal fuzzy decision recognition under different scenes corresponding to the Newhall navel orange harvesting classification decision scheme integrating appearance quality and internal quality detection can be established to comprise Lagrangian constant/>, according to a multi-level system multi-dimensional fuzzy decision theoryAnd a minimization objective function of xi/>
For a pair ofThe first and second derivatives are obtained with respect to kuh,j,kwi,j:
When (when) When in use, it is obvious thatIt can be seen that kuh,j,kwi,j is present to makeIf there is a minimum, the objective function/>, is minimizedThere is a solution.
From the following componentsFirst derivative expression and/>, with respect to kuh,j The theoretical value expression of kuh,j can be obtained:
From the following components First derivative expression and/>, with respect to kwi,j The theoretical value expression of kuh,j can be obtained:
Theoretical value expressions kuh,j and kwi,j can be substituted into an iterative process as shown in fig. 1 to give actual values ku'h,j and kw'i,j corresponding to kuh,j and kwi,j, and a priority, recommended and marginal use newel navel orange harvesting classification decision scheme can be given according to the maximum membership rule, so that the evaluation result of newel navel orange harvesting classification is given:
Step 1: in the initialization process, the iteration times t are set to be 0, and kxi,j、kri,j、ksi,h and kwi,j (t=0) can be given out through training set data corresponding to different indexes and historical data of a harvesting classification decision scheme in the Newhol navel orange harvesting classification process;
step 2: the iteration times t are replaced by t+1, kxi,j、kri,j、ksi,h and kwi,j (t-1) are substituted into a theoretical value expression of kuh,j so as to output kuh,j (t);
Step 3: substituting kxi,j、kri,j、ksi,h and kuh,j (t) into the theoretical value expression of kwi,j to output kwi,j (t);
Step 4: if both conditions [ kuh,j(t)-kuh,j(t-1)]/kuh,j(t)|≤δu and [ kwi,j(t)-kwi,j(t-1)]/kwi,j(t)|≤δw ] are satisfied, wherein both iteration process change thresholds δ u and δ w of kuh,j and kwi,j are set to 1%, kuh,j (t) and kwi,j (t) are output as actual values ku'h,j and kw'i,j, thereby constructing (ku'h,j)H×J and kw'i,j)I×J, otherwise, step 2 is entered;
Step 5: according to the decision evaluation set D= { D 1,D2,D3,D4 }, a multi-dimensional fuzzy decision matrix of the category k (ku'h,j)H×J can obtain a fruit harvesting classification decision set E h＝{eh＝j|ku'h,j≥εh corresponding to the evaluation level h, wherein E h is an element in the fruit harvesting classification decision set E h, the decision threshold epsilon h of the evaluation level h is set to be 0.9, and then the corresponding fruit harvesting classification decision can be selected to be used according to the evaluation level h from priority to marginal use;
Step 6: if the fruit harvest classification decision given in step 5 is j ', a weight vector of appearance quality is constructed from the target weights kwi,j′ of the index i corresponding to the category k decision j ' (1wi,j′)1×4 and the weight vector of internal quality (2wi,j′)1×7, and the normalized values of the eigenvalues kxi,j′ of the index i of the category k decision j ' are counted to establish (1ri,j′)1×4 and (2ri,j′)1×7, the evaluation result of the fruit harvest classification is determined after setting the quality weight ρ=0.36 can be expressed as:
Wherein, The M (+,) computation mode was used.
In fig. 2 and 3, normalized values of appearance quality and internal quality of 30 new york navel orange fruits after being tested are shown, from which evaluation results of the new york navel orange harvest classification are shown in fig. 4, wherein the evaluation results of the new york navel orange harvest classification are comprehensively considered, such as appearance quality and internal quality, and the weight vectors of the appearance quality and internal quality are effectively constructed (1wi,j′)1×4 and the weight vector of the internal quality (2wi,j′)1×7 normalized values are constructed) according to the specific embodiment, such as C 1 (size), C 2 (shape), C 3 (color), C 4 (smell), C 4 (hardness), C 5 (soluble solid), C 6 (sugar degree), C 7 (acidity), C 8 (internal browning), C 9 (freeze injury), C 10 (insect attack), C 11 (hollow) and the corresponding target weight kwi,j′.
Claims (2)
1. A fruit harvesting and sorting method integrating appearance quality and internal quality detection, which is characterized by comprising the following steps:
a) Let the fruit harvest classification decision set be Γ= { η j |1.ltoreq.j.ltoreq.j }, where η j is the J-th fruit harvest classification decision scheme integrating appearance quality and internal quality detection, J is the number of decision schemes, and the index size C 1, Shape C 2, color C 3, odor C 4, hardness C 4, soluble solids C 5, Sugar degree C 6, acidity C 7, internal browning C 8, freezing injury C 9, insect pest C 10, The hollow C 11 constructs an index set C= { C 1,...,C11 } of the fruit harvest classification decision set Γ, a category set B= { B 1,B2 } is constructed by the appearance quality B 1 and the internal quality B 2, let the different decision schemes of fruit harvest classification be divided into h=4 different evaluation grades during use as decision evaluation set D, expressed as:
D＝{D1,D2,D3,D4}，
Wherein D 1 represents priority use, D 2 represents recommended use, D 3 represents marginal use, and D 4 represents no recommendation;
Let the data characteristic value matrix of the fruit harvest classifying index corresponding to the category k decision j index I be expressed as (kxi,j)I×J, wherein, 1 is less than or equal to k is less than or equal to 2,1 is less than or equal to I is less than or equal to 11, kxi,j is the characteristic value of the category k decision j index I, kri,j is the normalized value of the characteristic value kxi,j of the category k decision j index I, ksi,h is the target value of the standard decision mode of the category k evaluation grade h, wherein, 1 is less than or equal to h is less than or equal to 4, kuh,j is the relative membership degree of the characteristic value of the decision j belonging to the evaluation grade h and kwi,j Decision of target weight of j index i for class k and/>Establishing a system containing Lagrangian constant/>, according to a multi-level system multi-dimensional fuzzy decision theory, for optimal fuzzy decision recognition under different scenes corresponding to fruit harvesting classification decision schemes integrating appearance quality and internal quality detectionAnd a minimization objective function of xi/>
B) When (when)When the first derivative with respect to kuh,j,kwi,j is 0,/>With respect to the second derivative of kuh,j, kwi,j being greater than 0, kuh,j,kwi,j exists such that/>If there is a minimum, the objective function/>, is minimizedA solution;
c) From the following components The first derivative with respect to kuh,j is kuh,j with respect to/>Expression of/>To yield the theoretical value expression of kuh,j:
Wherein t and l are intermediate variables;
d) From the following components The first derivative with respect to kwi,j yields kwi,j expression with respect to ζ and/>To yield the theoretical value expression of kwi,j:
Wherein m and d are intermediate variables;
e) Substituting the theoretical value expressions kuh,j and kwi,j into an iterative process to give actual values ku'h,j and kw′i,j corresponding to kuh,j and kwi,j, and giving fruit harvest classification decision schemes which are preferentially used, recommended to be used and barely used according to the maximum membership principle so as to give evaluation results of fruit harvest classifications;
b) In, The first derivatives with respect to kuh,j,kwi,j are expressed as:
b) In, The second derivatives with respect to kuh,j,kwi,j are expressed as:
c) In kuh,j about Expression of/>The expressions of (2) are expressed as:
wherein m and c are intermediate variables;
d) In kwi,j, expression and expression for ζ The expressions of (2) are expressed as:
2. the fruit harvesting classification method integrating appearance quality and internal quality detection as claimed in claim 1, wherein: e) In the above, the iterative process is described as:
step 1: kxi,j、kri,j and ksi,h are given out through training set data corresponding to different indexes in the fruit harvesting and classifying process and historical data of a harvesting and classifying decision scheme in the initializing process, and if no historical data is used for setting the harvesting and classifying decision scheme to comprise all indexes of appearance quality B 1 and internal quality B 2, and the target weight kwi,j (t) of the index I of the gate k decision j is 1/I, the iteration times t is set to be 0;
step 2: the iteration times t are replaced by t+1, kxi,j、kri,j、ksi,h and kwi,j (t-1) are substituted into a theoretical value expression of kuh,j so as to output kuh,j (t);
Step 3: substituting kxi,j、kri,j、ksi,h and kuh,j (t) into the theoretical value expression of kwi,j to output kwi,j (t);
step 4: if both conditions | [ kuh,j(t)-kuh,j(t-1)]/kuh,j(t)|≤δu and | [ kwi,j(t)-kwi,j(t-1)]/kwi,j(t)|≤δw ] are satisfied, wherein δ u and δ w are iterative process change thresholds of kuh,j and kwi,j, respectively, kuh,j (t) and kwi,j (t) are output as actual values ku'h,j and kw′i,j, thereby constructing (ku'h,j)H×J and kw′i,j)I×J, otherwise step 2 is entered;
Step 5: according to the decision evaluation set D= { D 1,D2,D3,D4 }, obtaining a fruit harvesting classification decision set E h＝{eh＝j|ku'h,j≥εh corresponding to the evaluation level h by a multidimensional fuzzy decision matrix (ku'h,j)H×J), wherein E h is an element in the fruit harvesting classification decision set E h corresponding to the evaluation level h, epsilon h is a decision threshold of the evaluation level h, and then selecting to use the corresponding fruit harvesting classification decision according to the evaluation level h by a sequence from priority to marginal use;
Step 6: if the fruit harvest classification decision given in step 5 is j ', a weight vector of appearance quality is constructed from the target weights kwi,j′ of the corresponding category k decision j ' index i (1wi,j′)1×4 and the weight vector of internal quality (2wi,j′)1×7, and the normalized values of the eigenvalues kxi,j′ of the category k decision j ' index i are counted to establish (1ri,j′)1×4 and (2ri,j′)1×7, the evaluation result of the fruit harvest classification is expressed as:
A＝ρ·(1wi,j′)1×4*(1ri,j′)1×4+(1-ρ)·(2wi,j′)1×7*(2ri,j′)1×7;
wherein, M (+, ·) computation mode is adopted, ρ is the quality weight.
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