Design features and bruise evaluation of an apple harvest and in-filed presorting machine

In-field presorting of apples, in combination with the harvest aid function, would have advantages of cost savings in postharvest handling and storage, reduced postharvest pest and disease problems, and better inventory management, while also enhancing harvest productivity. A new apple harvest and in-field presorting prototype was developed to help apple growers achieve these potential benefits. The prototype sorts and grades fruit based on color and size, using a machine vision-based sorting system with an innovative fruit singulating and rotating design (SRD), and it handles the graded fruit in the bins using newly designed automatic bin fillers. Bruise damage by impact is a critical factor in the development of the apple harvest and in-field presorting prototype. This article reports on the major design features of the prototype and experimental evaluation of the prototype for potential bruise damage. Experiments were conducted on ‘Gala’ and ‘Fuji’ apples to evaluate bruise damage potential under both empty and partially filled bin conditions. An impact recording device (IRD) was used to measure the impact magnitude in terms of peak acceleration (G) at all critical points of the machine, including harvest conveyors, main conveyor, flat conveyor, SRD, cup conveyor, bin filler, and bins. It was found that bruise damage mainly occurred during bin filling. The number of impacts recorded for the partially filled bin was reduced by 60%, compared to that for the empty bin, indicating that the impact between apples and the wooden bin’s floor was a major cause of bruising. The maximum G value for the partially filled bin was measured at 34.5, while the measured G values were less than 20 from start to the point just before the bin filler, indicating no bruise damage. Bruise evaluation showed that no more than 9% of the test apples would be downgraded from ‘Extra Fancy’ grade for the partially filled bin condition. Higher G values for the empty bin condition suggested the need for further improvement to the discharge of apples from the bin filler to the bin to further reduce bruise damage

Virtual Grader for Apple Qualityassessment using Fruit Size and Illumiation Features

The present paper reports on the development of an intelligent virtual grader for assessing apple quality using machine vision. The heart of the proposed virtual grader was executed in the form of K-Nearest Neighbor (K-NN) classifier designed on the architecture of Euclidean distance metric. KNN classifier is executed for this particular application due to its robustness to the noisy environment. The present study revealed that fruit surface illumination is one of the major deterministic parameters affecting accuracy substantially while assessing apple quality based on fruit size. The performance of the proposed virtual grader was examined experimentally under different conditions of fruit surface illumination. An industrial grade camera connected to an image grabber was used to implement the proposed industrial-grade virtual grader using machine vision. Results of this study are quite promising with an achievement of 99% efficiency at 100% repeatability when fruit surface is exposed to an optimal value of 310 lux. However, such an attempt has not been made earlier

Customized sorting and packaging machine

India is a country which has a cornerstone of agriculture. And as it comes to fruit/vegetable sorting and packaging in India, human labor has been a vital part. With manual hand picking, it is a very laborious task to classify the quality of fruits/vegetables and simultaneously pack them. One leading-edge technology for the fulfilment of this purpose is ‘Image Processing’ technology which is extremely fast and cost-efficient. Our whole idea revolves around the fact that each fruit will be inspected, sort and simultaneously packed. For the same, a low cost automated mechatronic system has designed consisting of a solitary mechanical arrangement, which is controlled and synchronized through electronic components. Fruits/vegetables are sorted as high-quality and low-quality on the basis of physical appearance and weight. For this, a suitable algorithm is designed using the Open CV library. And the sorting is done using Arduino Uno and Raspberry pi. Hence the aim is to develop a sorting and packaging facility that can be established at the very root level itself which will be economically compact and accurate and will give more justice to farmers

Boosting minimalist classifiers for blemish detection in potatoes

This paper introduces novel methods for detecting blemishes in potatoes using machine vision. After segmentation of the potato from the background, a pixel-wise classifier is trained to detect blemishes using features extracted from the image. A very large set of candidate features, based on statistical information relating to the colour and texture of the region surrounding a given pixel, is first extracted. Then an adaptive boosting algorithm (AdaBoost) is used to automatically select the best features for discriminating between blemishes and nonblemishes. With this approach, different features can be selected for different potato varieties, while also handling the natural variation in fresh produce due to different seasons, lighting conditions, etc. The results show that the method is able to build “minimalist” classifiers that optimise detection performance at low computational cost. In experiments, minimalist blemish detectors were trained for both white and red potato varieties, achieving 89.6% and 89.5% accuracy respectively

Economic evaluation of apple harvest and in-filed sorting technology

The U.S. apple industry, which generated more than $2.7 billion in revenue at the farm gate in 2013, is facing critical challenges with decreased availability of labor and increased labor and production costs. To address these challenges, a self-propelled apple harvest and in-field sorting machine is being developed in our laboratory. This article reports on the economic evaluation of this prototype machine by considering machine cost (annual ownership and operating costs), harvest productivity increase (including that due to decreased occupational injuries), and cost savings in postharvest storage and packing resulting from in-field sorting of fresh market quality apples from processing apples for both fresh apple growers and processing apple growers. The economic evaluation was conducted based on the assumptions that the machine increases harvest productivity by 43 from$13,500 to $78,400 when the machine price is between$100,000 and $160,000. For processing apple orchards with processing apple incidences of 80$23,900 to $81,700. Overall, the benefits gained from in-field sorting outweigh those from the harvest productivity increase, and integration of the harvest-assist and in-field sorting functions is more beneficial to apple growers. This technology will help the U.S. apple industry improve labor productivity and reduce production costs, and thus it looks promising for commercialization

Designing a fruit identification algorithm in orchard conditions to develop robots using video processing and majority voting based on hybrid artificial neural network

The first step in identifying fruits on trees is to develop garden robots for different purposes such as fruit harvesting and spatial specific spraying. Due to the natural conditions of the fruit orchards and the unevenness of the various objects throughout it, usage of the controlled conditions is very difficult. As a result, these operations should be performed in natural conditions, both in light and in the background. Due to the dependency of other garden robot operations on the fruit identification stage, this step must be performed precisely. Therefore, the purpose of this paper was to design an identification algorithm in orchard conditions using a combination of video processing and majority voting based on different hybrid artificial neural networks. The different steps of designing this algorithm were: (1) Recording video of different plum orchards at different light intensities; (2) converting the videos produced into its frames; (3) extracting different color properties from pixels; (4) selecting effective properties from color extraction properties using hybrid artificial neural network-harmony search (ANN-HS); and (5) classification using majority voting based on three classifiers of artificial neural network-bees algorithm (ANN-BA), artificial neural network-biogeography-based optimization (ANN-BBO), and artificial neural network-firefly algorithm (ANN-FA). Most effective features selected by the hybrid ANN-HS consisted of the third channel in hue saturation lightness (HSL) color space, the second channel in lightness chroma hue (LCH) color space, the first channel in L*a*b* color space, and the first channel in hue saturation intensity (HSI). The results showed that the accuracy of the majority voting method in the best execution and in 500 executions was 98.01% and 97.20%, respectively. Based on different performance evaluation criteria of the classifiers, it was found that the majority voting method had a higher performance.European Union (EU) under Erasmus+ project entitled “Fostering Internationalization in Agricultural Engineering in Iran and Russia” [FARmER] with grant number 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JPinfo:eu-repo/semantics/publishedVersio

Visual detection of blemishes in potatoes using minimalist boosted classifiers

This paper introduces novel methods for detecting blemishes in potatoes using machine vision. After segmentation of the potato from the background, a pixel-wise classifier is trained to detect blemishes using features extracted from the image. A very large set of candidate features, based on statistical information relating to the colour and texture of the region surrounding a given pixel, is first extracted. Then an adaptive boosting algorithm (AdaBoost) is used to automatically select the best features for discriminating between blemishes and non-blemishes. With this approach, different features can be selected for different potato varieties, while also handling the natural variation in fresh produce due to different seasons, lighting conditions, etc. The results show that the method is able to build ``minimalist'' classifiers that optimise detection performance at low computational cost. In experiments, blemish detectors were trained for both white and red potato varieties, achieving 89.6\% and 89.5\% accuracy, respectively

Grader: A review of different methods of grading for fruits and vegetables

Grading of agricultural produce especially the fruits and vegetables has become a perquisite of trading across borders.  In India mostly fruit growers grade the fruit manually.  Manual grading was carried out by trained operators who considered a number of grading factors and fruit were separated according to their physical quality.  Manually grading was costly and grading operation was affected due to shortage of labor in peak seasons.  Human operations may be inconsistent, less efficient and time consuming.  New trends in marketing as specified by World Trade Organization (WTO) demand high quality graded products.  Farmers are looking forward to having an appropriate agricultural produce-grading machine in order to alleviate the labor shortage, save time and improve graded product’s quality.  Grading of fruits is a very important operation as it fetches high price to the grower and improves packaging, handling and brings an overall improvement in marketing system.  The fruits are generally graded on basis of size and graded fruits are more welcome in export market.  Grading could reduce handling losses during transportation. Grading based on size consists of divergent roller type principle having inclination, expanding pitch type, inclined vibrating plate and counter rotating roller having inclination type graders.  Weight grading based on density and specific gravity of agricultural commodities.  The need to be responsive to market demand places a greater emphasis on quality assessment, resulting in the greater need for improved and more accurate grading and sorting practices.  Size variation in vegetables like potatoes, onions provided a base for grading them in different categories.  Every vegetable producing country had made their own standards of different grades keeping in view the market requirements.   Keywords: grading, handling, packaging, color sensor, specific gravity, Indi

Preliminary technology utilization assessment of the robotic fruit harvester

The results of an analysis whose purpose was to examine the history and progress of mechanical fruit harvesting, to determine the significance of a robotic fruit tree harvester and to assess the available market for such a product are summarized. Background information that can be used in determining the benefit of a proof of principle demonstration is provided. Such a demonstration could be a major step toward the transfer of this NASA technology

Application progress of machine vision technology in the field of modern agricultural equipment

With the rapid progress of image processing algorithms and computer equipment, the development of machine vision technology in the field of modern agricultural equipment is in the ascendant, and major application results have been obtained in many production links to improve the efficiency and automation of agricultural production. In the face of China, the world's largest agricultural market, agricultural machine vision equipment undoubtedly has tremendous development potential and market prospects. This paper introduces the research and application of machine vision technology in agricultural equipment in the fields of agricultural product sorting, production automation, pest control, picking machinery and navigation and positioning, analyzes and summarizes the current problems, and looks forward to the future development trend