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The choice of a sales channel for fresh vegetables is an important decision a farmer can make. Typically, the farmers rely on their personal experience in directing the produce to a sales channel. This study examines how sales forecasting of fresh vegetables along multiple channels enables marginal and small-scale farmers to maximize their revenue by proportionately allocating the produce considering their short shelf life.

Machine learning models, namely long short-term memory (LSTM), convolution neural network (CNN) and traditional methods such as autoregressive integrated moving average (ARIMA) and weighted moving average (WMA) are developed and tested for demand forecasting of vegetables through three different channels, namely direct (Jaivasree), regulated (World market) and cooperative (Horticorp).

The results show that machine learning methods (LSTM/CNN) provide better forecasts for regulated (World market) and cooperative (Horticorp) channels, while traditional moving average yields a better result for direct (Jaivasree) channel where the sales volume is less as compared to the remaining two channels.

The price of vegetables is not considered as the government sets the base price for the vegetables.

The existing literature lacks models and approaches to predict the sales of fresh vegetables for marginal and small-scale farmers of developing economies like India. In this research, the authors forecast the sales of commonly used fresh vegetables for small-scale farmers of Kerala in India based on a set of 130 weekly time series data obtained from the Kerala Horticorp.

In the purpose of the section, the cracks that are in the construction domain may be common and usually fixed with the human inspection which is at the visible range, but for the cracks which may exist at the distant place for the human eye in the same building but can be captured with the camera. If the crack size is quite big can be visible but few cracks will be present due to the flaws in the construction of walls which needs authentic information and confirmation about it for the successful completion of the wall cracks, as these cracks in the wall will result in the structure collapse.

In the modern era of digital image processing, it has captured the importance in all the domain of engineering and all the fields irrespective of the division of the engineering, hence, in this research study an attempt is made to deal with the wall cracks which are found or searched during the building inspection process, in the present context in association with the unique U-net architecture is used with convolutional neural network method.

In the construction domain, the cracks may be common and usually fixed with the human inspection which is at the visible range, but for the cracks which may exist at the distant place for the human eye in the same building but can be captured with the camera. If the crack size is quite big can be visible but few cracks will be present due to the flaws in the construction of walls which needs authentic information and confirmation about it for the successful completion of the wall cracks, as these cracks in the wall will result in the structure collapse. Hence, for the modeling of the proposed system, it is considered with the image database from the Mendeley portal for the analysis. With the experimental analysis, it is noted and observed that the proposed system was able to detect the wall cracks, search the flat surface by the result of no cracks found and it is successful in dealing with the two phases of operation, namely, classification and segmentation with the deep learning technique. In contrast to other conventional methodologies, the proposed methodology produces excellent performance results.

The originality of the paper is to find the portion of the cracks on the walls using deep learning architecture.