Machine learning based mass prediction and discrimination of chickpea (Cicer arietinum L.) cultivars

Chickpea is an important edible legume that can be grown in rain fed conditions. Image analysis and machine learning could be used for rapid and non-destructive determination of seed physical attributes and such techniques yield objective, accurate and reliable results. In this study, size, shape, and area attributes of 26 different chickpea cultivars were determined by image processing method, and color properties were determined by chromametric method, and machine learning algorithms (Multi layer Perceptron-MLP, Random Forest-RF, Support Vector Regression-SVR, and k-Nearest NeighborkNN, were used for mass prediction of chickpea seeds. Ilgaz and Cakir cultivars had the highest size and shape values, while Izmir and Sezenbey cultivars had the highest color attributes. Compactness (in horizontal orientation) had a positive correlation with the equivalent diameter (in vertical orientation) and elongation (in vertical orientation) (r = 0.99 for both parameters). Besides, a* had a high correlation with b* (r = 0.97). According to Euclidean distances, Akca-Inci and Damla-Isik cultivars were identified as the closest cultivars in terms of physical attributes. In PCA analysis, PC1 and PC2 explained 73.17% of the total variation. The PC1 included length, geometric mean diameter, volume and surface area, and the PC2 included roundness (in horizontal orientation), thickness, elongation (in horizontal orientation) and sphericity. RF and ML had successful results with the values of 0.8054 and 0.8043 for train-test split, and 0.8231 and 0.8142 for k-fold cross validation, respectively. Present findings revealed that texture image processing and machine learning could be used as an effective and inexpensive discrimination tool for chickpea seeds.