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The aim of this empirical field research was to show potential differences due to the precise application of herbicides in the quality and size of the carrot root yield and the amount of working liquid used compared to those for control samples. Empirical verification of the effectiveness of the developed technology, confirmed by statistical analyses of the obtained results, allows for a comparative assessment of this method with the conventional method of herbicide application used in this study. Due to the methodology used, it can be assumed that for carrots, the years were a random factor, and the experiments carried out were a series. It can be assumed that in the analysis of a series of experiments, for each of the examined features there are no differences between the groups and there is no interaction of the groups with the years. The yields of the groups weeded manually, sprayed in a conventional way, and weeded with precise spraying did not differ in a statistically significant way. In the precise application, a 20–30% lower herbicide consumption was observed, which has an impact on the protection of the environment and improves the economic effect of carrot production.

Modelling and predicting agricultural production processes have high cognitive and practical values. Plant growth, the threat of pathogens and pests, and the structure of agricultural machinery treatments are mostly non-linear, measurable processes that can be described mathematically. In this paper, a multiple regression analysis was carried out in the first step to check the non-linearity of the data and yielded a coefficient of determination of R2 = 0.9741 and the coefficient of determination corrected for degrees of freedom was R2adj = 0.9739. An artificial neural network model, called CH-NET, is then presented to predict the yield loss of carrot roots by leaving root mass in the field during harvest at the mechanical heading stage. The proposed network model has an architecture consisting of an input layer, three hidden layers with 12 neurons each, and an output layer with one neuron. Twelve input criteria were defined for the analysis and testing of the network, eight of which related to carrot root parameters and four to the heading machine. The training, testing, and validation database of the CH-NET network consisted of the results of field trials and tests of the operation of the patented (P.242097) root heading machine. The proposed CH-NET neural network model achieved global error (GE) values of 0.0931 t·ha−1 for predicting carrot root yield losses for all twelve criteria adopted. However, when the number of criteria is reduced to eight, the error increased to 0.0991 t·ha−1. That is, the prediction was realised with an accuracy of 90.69%. The developed CH-NET model allows the prediction of economic losses associated with root mass left in the field or contamination of the raw material with undercut leaves. The simulations carried out showed that minimum root losses (0.263 t·ha−1) occur at an average root head projection height of 38 mm and a heading height of 20 mm from the ridge surface. [ABSTRACT FROM AUTHOR]

Evaluating onions for size, shape, damage, colour and discolouration is the first and most important step in classifying them for raw material quality, processing and the horticultural and agri-food sectors. Current methods of geometric evaluation and grading of onions involve mechanical and extremely invasive sorting, which causes additional damage, reduces the quality of the raw material and is also labour and time-consuming. As a result, non-invasive evaluation and classification methods that are both fast and accurate are being sought. One such method is digital image analysis, which, when combined with instrumentation and deep neural networks, can fully automate the process. The main aim of this study was the development of a model for the automatic evaluation and classification of onions using a deep convolutional neural network (CNN) model. A fixed-architecture network was built, for which a computational algorithm was developed in Python 3.9 and published at https://github.com/piotrrybacki/onion-CNN.git (accessed on 4 October 2024). The Hyduro F1 onion variety, a hybrid all-purpose variety of the Rijnsburger type, was used to build, teach and test the model. The developed algorithm classified the onion images qualitatively with an accuracy of 91.85%. This classification was based on the geometric parameters of the onion, i.e. diameter, height, transversal and longitudinal circumference, and the estimated area of damage or discolouration of the skin. The root mean square error (MSE) in RGB space varied between 87.99 and 91.24, and the maximum image classification time was 28.98 ms/image. The developed algorithm has a very high utility, as it automates the classification process, reducing its time and labour intensity. [ABSTRACT FROM AUTHOR]

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Copyright of Ecological Engineering / Inżynieria Ekologiczna is the property of Polish Society of Ecological Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)