Yield Measurement System for Seed Corn: Improving Dynamic Weight Accuracy and Harvest Area Determination

A prototype weight-based yield mapping system for seed corn production was developed at the University of Tennessee (UTK) and field tested in Iowa. The first chapter of the following study focuses on assessing the accuracy of this yield mapping system which employs a novel yield prediction and analysis software called Yield Analyzer. Yield Analyzer was designed using a rule-based system for producing yield maps with minimal user input by automatically determining acceptable ranges for known dependent variables that contribute to dynamic weight measurement errors. The second chapter of this thesis covers the development of a non-intrusive, machine vision technique to measure true width of crop entering a header during harvesting. The development of this technology would further contribute to the overall yield prediction accuracy by providing necessary information for calculating real-time changes in the area component of yield. Using a rule-based system for yield data processing, Yield Analyzer produces two levels of site-specific yield measurements. At the first level of data acquisition, cart weight measurements compared to certified scale weights at an average absolute difference of 6.07 %. At the second level of data acquisition, weight, length, and yield measurements had a higher degree of variance. For determination of effective header width, two vision-based classification methods were tested from real-time harvesting video data. The first method used color features for crop detection performed \u3e 90 % accuracy at 0.50 - 0.75 standard deviations from mean color feature descriptors. A linear support vector machine classifier trained with image SURF descriptors performed at \u3e 95 % classification accuracy when images from the entire video dataset were used for training

Applications of Image Processing for Grading Agriculture products

Image processing in the context of Computer vision, is one of the renowned topic of computer science and engineering, which has played a vital role in automation. It has eased in revealing unknown fact in medical science, remote sensing, and many other domains. Digital image processing along with classification and neural network algorithms has enabled grading of various things. One of prominent area of its application is classification of agriculture products and especially grading of seed or cereals and its cultivars. Grading and sorting system allows maintaining the consistency, uniformity and depletion of time. This paper highlights various methods used for grading various agriculture products. DOI: 10.17762/ijritcc2321-8169.15036

Identification of Canola Seeds through Computer Vision Image Processing

The objectives of this research are to present the automatic organization of agricultural seeds with the explosion of digital information through compute image vision processing. In this research paper CVIP (computer vision image processing) tool has been applied on different varieties and categorized of canola seeds. We had the 4 different varieties of canola seeds which were named as Gobhi Sarson (A), Barassica comp (B), Sathri (C) and Rocket Herbof (D). Each variety had 10 images and total 10*4 =40 images of canola seeds. We took the train data results of all kinds of canola seeds. After that the train data results were compared for pattern classification and apply the nearest neighbor and k-nearest neighbor algorithms for final classification. We achieved in nearest neighbor 85% and 76% average and k-nearest neighbor 90% and 73% average as a final pattern classification results. These are the best percentage for classification and provide more accuracy. The formers can select best healthy seed variety with the help of the results of this research. Keywords:  Features, Pattern classification, nearest neighbor, k-nearest neighbo

Identification of Canola Seeds using Nearest Neighbor and K-Nearest Neighbor Algorithms

Agriculture plays an important role on Pakistan economy. Canola is the major crop of Pakistan. There are different varieties of canola crop. It fulfills the requirement of oil. It is the difficult task to identify best canola seeds for sowing due to different varieties of canola seeds. In this paper we are try to introduce different machine learning approaches for classification of different canola seeds which provide opportunity to people or farmer to identify different canola seeds. Canola seeds verities implementing by the computer vision image processing tool. We have the 4 different varieties which names as Gobhi Sarson (A), Barassica comp (B), Sathri (C) and Rocket Herbof (D) canola seeds and take the images of canola seeds from these different varieties. Each variety has 10 images and we have total 10*4 =40 images of canola seeds. we take the train and test data results of all kinds of canola seeds. then train and test data results will be compare for pattern classification and apply the nearest neighbor and k-nearest neighbor algorithms for final classification in computer image processing tool. We achieved in nearest neighbor 85% and 76% average and k-nearest neighbor 90% and 73% average as a final pattern classification results. These are the best percentage for classification and provide more accuracy. These are important for farmer and other people for identify the different canola seeds. Keywords:  Features, Pattern classification, nearest neighbor, k-nearest neighbo

Intelligent Assessment of Sun Flower Seeds Using Machine Learning Approaches

Pakistan is an agricultural country. Sun flower is the major crop of Pakistan which is being sowing in many areas of country. It fulfills the requirement of edible oil. In this paper we are trying to identify the best quality from different sun flowers seeds verities by using machine learning approaches. We take the images of four kinds of sunflower seeds which names Top sun(A), High Sun(B),US666(C) and Seji(D) for classification. We get eight different images of each kind of sunflower. In this paper sun flowers seeds varieties were categorized by using Computer vision image processing tool (CVIP). The experience and knowledge of inspectors are required to perfectly perform this assessment process. We use the RST-Invariant Features, Histogram Features, Texture Features, and Pattern Classification and also use the nearest neighbor and k-nearest neighbor algorithms for final classification. We achieved the final results of four kinds of sunflower using nearest neighbor on distance one and two 89% and 72% average and on k-nearest neighbor 89% and 73% average percentage. These are the best percentage results using these algorithms for classification. In this way we can easily classify the sunflower seeds and also these methods provide opportunity to farmer and other people for identify and select the different better and healthy sunflower seeds for better benefits. Keywords: RST-Invariant Features, Histogram Features, Texture Features, Classification Algorithm

Automatic Classification of Chickpea Varieties Using Computer Vision Techniques

There are about 90 different varieties of chickpeas around the world. In Iran, where this study takes place, there are five species that are the most popular (Adel, Arman, Azad, Bevanij and Hashem), with different properties and prices. However, distinguishing them manually is difficult because they have very similar morphological characteristics. In this research, two different computer vision methods for the classification of the variety of chickpeas are proposed and compared. The images were captured with an industrial camera in Kermanshah, Iran. The first method is based on color and texture features extraction, followed by a selection of the most effective features, and classification with a hybrid of artificial neural networks and particle swarm optimization (ANN-PSO). The second method is not based on an explicit extraction of features; instead, image patches (RGB pixel values) are directly used as input for a three-layered backpropagation ANN. The first method achieved a correct classification rate (CCR) of 97.0%, while the second approach achieved a CCR of 99.3%. These results prove that visual classification of fruit varieties in agriculture can be done in a very precise way using a suitable method. Although both techniques are feasible, the second method is generic and more easily applicable to other types of crops, since it is not based on a set of given features.This research was funded by the Spanish MICINN, as well as European Commission FEDER funds, under grant RTI2018-098156-B-C53. It has also been supported by the 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-JP

Identification of Rice Quality Through Pattern Classification Using Computer Vision Image Processing

Rice is the source of Pakistan agriculture industry and food. For agriculture, industry and the oldest sector in the world use rice for different purpose. There are many challenges in the particular sector such as their analysis. This analysis mostly often related to its texture, color, shape, grain etc. In this study, Vision system used to check the quality of rice using some texture features such as color, shape and characteristics. In this study Computer Vision Image Processing tool applied on three different types of rice. Using this tool we apply pattern classification using nearest neighbor and K-nearest neighbor algorithm. Using these algorithms we get results of three varieties of rice Bastmati, Jasmine and White rice. In both algorithms white rice result show best from Basmati rice and Jasmine rice. White rice result is 93.75 % which is best as quality wise. Other tool also available like as MATLAB, Mazda etc for future more best prediction. Keywords: RST-Invariant features, Histogram features, Texture features, Nearest Neighbor algorithm, K-nearest neighbor algorithm DOI: 10.7176/CEIS/11-2-01 Publication date: February 29th 202

Adaptive detection of volunteer potato plants in sugar beet fields

Volunteer potato is an increasing problem in crop rotations where winter temperatures are often not cold enough to kill tubers leftover from harvest. Poor control, as a result of high labor demands, causes diseases like Phytophthora infestans to spread to neighboring fields. Therefore, automatic detection and removal of volunteer plants is required. In this research, an adaptive Bayesian classification method has been developed for classification of volunteer potato plants within a sugar beet crop. With use of ground truth images, the classification accuracy of the plants was determined. In the non-adaptive scheme, the classification accuracy was 84.6 and 34.9% for the constant and changing natural light conditions, respectively. In the adaptive scheme, the classification accuracy increased to 89.8 and 67.7% for the constant and changing natural light conditions, respectively. Crop row information was successfully used to train the adaptive classifier, without having to choose training data in advanc