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Proceedings ArticleDOI

Automatic vegetable differentiator

01 Sep 2017-
TL;DR: The main objective of the project was to differentiate between potatoes and onions and classify them on the basis of sizes — small, medium and large and used a color sensor which can detect the color of the respective vegetable and separate them accordingly.
Abstract: The main objective of the project was to differentiate between potatoes and onions and classify them on the basis of sizes — small, medium and large. Thus, for the first task i.e., to differentiate between potatoes and onions, we used a color sensor which can detect the color of the respective vegetable and separate them accordingly. For the second task i.e., to classify them according to the size we used five IR sensors. If any two IR sensors detect the vegetable, then it signifies that it is a small produce and it'll be separated using a flap at a certain angle. Similarly, if three sensors detect then the produce is medium sized and it'll be separated at an angle unlike to that of the small one. Finally, if all the five sensors detect then the produce will be classified as a large produce and will be separated at a different angle other than that of the other two. This procedure applies for both vegetables with potatoes on one side and onions on the other direction. This project shall have an application in both farming and the upliftment of the agricultural sector and the life of farmers.
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Proceedings ArticleDOI
23 Jul 2022
TL;DR: In this paper , the authors used a random forest algorithm to identify onion's external disease and its cause and sort the healthy onions from the diseased onions using 200 images of red and white onion.
Abstract: Onion is one of the most important crops grown in the world and is generally used as vegetables, spices, and medicines. The inspection of onion quality relies on the visual part of the crop, most known diseases are based upon two things: the leaves and the roots. When either of the two is damaged, it is automatically a bad one. When harvest time comes, manual sorting is already implemented at the field. The study aims to identify the onion's external disease and its cause and sort the healthy onions from the diseased onions. Dataset preparation was a collection of 200 images of red and white onions. The images were augmented to increase the diversity of data for machine learning training and testing. The trained model using random forest algorithm were implemented in raspberry pi 4 which act as the main controller of the system. The system was tested for 30 trials and yield an accuracy of 96.67% in identifying the onion disease and sorting based on its quality.
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Posted Content
TL;DR: In this paper, the authors discuss the trends and patterns in agricultural growth at the national and sub-national levels in India and discuss the use of modern varieties, irrigation and fertilisers were important factors that ensured higher growth in crop production.
Abstract: The present study discusses the trends and patterns in agricultural growth at the national and sub-national levels in India. Data on important variables like area, production, input use and value of output were compiled for the period 1967-68 to 2007-08 from various published sources. The analysis of data reveals that the cropping pattern in India has undergone significant changes over time. There is a marked shift from the cultivation of food grains to commercial crops. Similarly, the performance of pulses in terms of area and output was not impressive during the study period. The use of technological inventions in the cultivation of other crops was also not so conspicuous in pulses. Nevertheless, the increase in crop yield has been a major factor for accelerating production in the country since the late 1960s. The use of modern varieties, irrigation and fertilisers were important factors that ensured higher growth in crop production. However, technological and institutional support for a few crops like rice and wheat brought significant changes in crop area and output composition in some regions. The results of crop output growth model indicate that the enhanced capital formation, better irrigation facilities, normal rainfall and improved fertiliser consumption helped to improve crop output in the country.

41 citations

Posted Content
TL;DR: In this article, the authors discuss the trends and patterns in agricultural growth at the national and sub-national levels in India and discuss the important variables like area, production, input use and value of output.
Abstract: The present study discusses the trends and patterns in agricultural growth at the national and sub-national levels in India. Data on important variables like area, production, input use and value of output were compiled for the period 1967-68 to 2007-08 from various published sources. The analysis of data reveals that the cropping pattern in India has undergone significant changes over time. There is a marked shift from the cultivation of food grains to commercial crops. Among food grains, the area under coarse cereals declined by 13.3 per cent between 1970-71 and 2007-08. Similarly, the performance of pulses in terms of area and output was not impressive during the study period. The use of technological inventions in the cultivation of other crops was also not so conspicuous in pulses. Nevertheless, the increase in crop yield has been a major factor for accelerating production in the country since the late 1960s. The use of modern varieties, irrigation and fertilisers were important factors that ensured higher growth in crop production. However, technological and institutional support for a few crops like rice and wheat brought significant changes in crop area and output composition in some regions. The results of crop output growth model indicate that the enhanced capital formation, better irrigation facilities, normal rainfall and improved fertiliser consumption helped to improve crop output in the country.

26 citations