The objective of this study is to develop a method for identifying and discriminating ten potato varieties by combining machine vision and artificial neural network methods. The potato varieties include Agria, Emrad. A total number of 72 characteristic parameters specifying color, textural and morphological features are found among these varieties. By using principal component analysis (PCA), 16 principal features are selected for identifying and discriminating potato varieties. The data obtained from image processing were classified using linear discriminant analysis and nonlinear artificial neural network method. The accuracy of discriminant analysis were 73.3%, 93.3%, 73.3%, 40%, 73.3%, 73.3%, 66.7%, 80%, 40% and 53.3%, respectively for the varieties used in this study. The classification accuracy was improved by 100% for all the varieties using neural network analysis and the Correct Classification Ratio (CCR) was 100% using this method.A c c e p t e d M a n u s c r i p t 2 It is revealed from the results that machine vision technique and neural network analysis could identify potato varieties with acceptable accuracy.

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Soil roughness is one of the most challenging issues in the agricultural domain and plays a crucial role in soil quality. The objective of this research was to develop a computerized method based on stereo vision technique to estimate the roughness formed on the agricultural soils. Additionally, soil till quality was investigated by analyzing the height of plow layers. An image dataset was provided in the real conditions of the field. For determining the soil surface roughness, the elevation of clods obtained from tillage operations was computed using a depth map. This map was obtained by extracting and matching corresponding keypoints as super pixels of images. Regression equations and coefficients of determination between the measured and estimated values indicate that the proposed method has a strong potential for the estimation of soil shallow roughness as an important physical parameter in tillage operations. In addition, peak fitting of tilled layers was applied to the height profile to evaluate the till quality. The results of this suggest that the peak fitting is an effective method of judging tillage quality in the fields.

Estimation of crop damage plays a vital role in the management of fields in the agriculture sector. An accurate measure of it provides key guidance to support agricultural decision-making systems. The objective of the study was to propose a novel technique for classifying damaged crops based on a state-of-the-art deep learning algorithm. To this end, a dataset of rapeseed field images was gathered from the field after birds’ attacks. The dataset consisted of three classes including undamaged, partially damaged, and fully damaged crops. Vgg16 and Res-Net50 as pre-trained deep convolutional neural networks were used to classify these classes. The overall classification accuracy reached 93.7% and 98.2% for the Vgg16 and the ResNet50 algorithms, respectively. The results indicated that a deep neural network has a high ability in distinguishing and categorizing different image-based datasets of rapeseed. The findings also revealed a great potential of deep learning-based models to classify other damaged crops.