Deep Learning Prediction of Chlorophyll Content in Tomato Leaves

Precision agriculture has improved crops production around the world. Non-destructive evaluation of chlorophyll contents of plant leaves can be a useful solution, in the field of precision farming. In order to take the required measures, sometimes it is essential to precisely evaluate the chlorophyll content, without cutting the target leaves. In this work, a deep learning methodology is proposed to assess the quantity of chlorophyll in the leaves of the tomato plants, through image processing. This methodology can be extended to any other type of leaves. The proposed method consists of a convolutional denoising autoencoder, to reduce the ambient light noises. Then, using a deep autoencoder network, the valuable features of the plant leaf image are extracted and fed as input to another neural network that evaluates the chlorophyll content of the leaf, taking advantage of support vector regression. To validate the accuracy of the proposed method, measurements were performed using the SPAD chlorophyll meter. The validation results prove the desired accuracy and efficiency of the developed approach.