Lettuce classification using convolutional neural network
Determining the varieties of lettuce through image processing and pattern recognition is a part of precision farming. Automatic classification is becoming vital for precision farming practice as it is rapidly sprouting field with the emergence of many applications in agriculture. It is a hassling process to differentiate and identify the lettuce varieties through human capabilities as it is time-consuming and also prone to errors in the identification process. Hence, there is a need to perform this task assisted by a machine capability which makes it faster with even greater accuracy. The objective of this research work is to design lettuce varieties recognition using Convolutional Neural Network (CNN) in MATLAB with an accuracy of at least 90%. CNN was employed to classify seven types of most commonly found lettuce. The CNN model was trained with 7000 leaves and tested with 1800 leaves for the classification of 7 varieties of lettuce. The overall classification accuracy is 97.8%; meanwhile, individual classification accuracies for the selected lettuce varieties, i.e. Butterhead, Celtuce Love, Italian, Red Coral, Lactuca Sativa Lettuce, Red Oakleaf and Salad Grand Rapid are 97%, 99.3%, 98.7%, 96%, 100%, 99.3%, and 94%, respectively. The results from this study have proven the high effectiveness of using a machine learning technique, i.e. CNN, to identify a particular variety of lettuce.