نوع مقاله : مقاله پژوهشی
نویسنده
گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی ومنابع طبیعی تهران، دانشگاه تهران، کرج، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
Soil is one of the most important sources of production in agriculture. Therefore, with the determination of soil and its important characteristics, proper management and sustainable use of agricultural lands can be achieved. The current study aimed to predict the soil texture using a machine vision system and deep convolutional neural network (DCNN) algorithm. The proposed CNN model was composed of two blocks, including convolutional layers, max pooling layers, a dropout layer, batch normalization layers, fully connected layers, and a support vector machine classifier. This model was trained and tested on the images of different soil samples (11 types of soil texture and a total of 790 soil sample images). The data is prepared by a machine vision system and a smartphone camera (Galaxy A8). Using the confusion matrix, important statistical parameters such as accuracy, precision, specificity, sensitivity, and area under the curve were obtained at 99.65%, 98.75%, 99.8%, 98.75, and 99.27%, respectively. The suggested model successfully and correctly classified the soil sample images with 98.1% accuracy. The obtained results indicated that this study's implemented deep learning model can be a proper alternative to costly and time-consuming laboratory methods for determining soil texture.
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