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Agriculture robot by mechanical harvesting requires automatic detection and counting of fruits in tree canopy. Because of color similarity, shape irregularity, and background complex, fruit identification turns to be a very difficult task and not to mention to execute pick action. Therefore, green cucumber detection within complex background is a challenging task due to all the above-mentioned problems. In this paper, a technique based on texture analysis and color analysis is proposed for detecting cucumber in greenhouse. RGB image was converted to gray-scale image and HSI image to perform algorithm, respectively. Color analysis was carried out in the first stage to remove background, such as soil, branches, and sky, while keeping green fruit pixels presented cucumbers and leaves as many as possible. In parallel, MSER and HOG were applied to texture analysis in gray-scale image. We can obtain some candidate regions by MSER to obtain the candidate including cucumber. The support vector machine is the classifier used for the identification task. In order to further remove false positives, key points were detected by a SIFT algorithm. Then, the results of color analysis and texture analysis were merged to get candidate cucumber regions. In the last stage, the mathematical morphology operation was applied to get complete cucumber.

In this study, modeling of thin banana slices dried on 316 stainless steel shelves is carried out in an oven working with serial controlled and concentric blower-resistor couple. Changes occurred in banana slices (area and color) during drying process have been recorded by a camera. Additionally, weight has been measured with a load cell which is under the shelf and energy consumption has been measured with electricity consumption meter which is tied to energy input. The main aim of the study is to conduct the drying process of banana slices according to the data obtained from camera. Besides, obtained data have been tested with a powerful modeling technique like Artificial Neural Networks (ANN), and it has been seen that drying process could be modeled according to the data obtained from camera. Energy consumption data have been added in order to increase the performance of ANN and strengthen the modeling. Thus, an automatic drying system that can learn by itself using only a camera without any other sensors will be installed. This has been caused an increase in performance. However, it is obvious that it increases cost. According to the results of modeling process, 99% of “goodness of fit” has been obtained by using the change in banana slices and the number of pixels. It has been found that the developed model performed adequately in predicting the changes of the moisture content. Thus, it has been available to control the food drying process with a digital camera.