Abstract
Tomato crops are considered the most important agricultural products worldwide. However, the quality of tomatoes depends mainly on the nutrient levels. Visual inspection is made by farmers to anticipate the nutrient deficiency of the plants. Recently, precision agriculture has explored opportunities to automate nutrient level monitoring. Previous work has demonstrated that a convolutional neural network (CNN) is able to estimate low nutrients in tomato plants using images of their leaves. However, the performance of the CNN was not adequate. Thus, this work proposes a novel CNNbased classifier, namely CNN+AHN, for estimating low nutrients in tomato crops using an image of the tomato leaves. The CNN+AHN incorporates a set of convolutional layers as the feature extraction part, and a supervised learning method called artificial hydrocarbon network (AHN) as the dense layer. Different combinations of the architecture of CNN+AHN were examined. Experimental results showed that our best CNN+AHN classifier is able to estimate low nutrients in tomato plants with an accuracy of 95:57% and F1-score of 95:75%, outperforming the literature.
Highlights
Nowadays, one of the most important food sources is the agricultural production field, and more than 30% of the human food consumption is lost in some phases of the supply chain
The results show that our best convolutional neural network (CNN)+artificial hydrocarbon network (AHN) model is able to estimate low nutrients in tomato plants with an accuracy of 95.57%
We take advantage of deep learning to analyze the leaves of the tomato crops for detecting nutrients deficiency. Another successfully vision-based application of deep learning [13] is where with a simple CNN predicting the nutrient deficiency in tomato crops using an image of their leaves, the results show that this CNN-based work achieved 86.59% of accuracy metric using the same dataset used in this work [13]
Summary
One of the most important food sources is the agricultural production field, and more than 30% of the human food consumption is lost in some phases of the supply chain. There are multiple open problems in agricultural production in some of the phases involved in the supply chain process and plant care processes. Based on data from Food and Agriculture Organization, an estimated 1.3 billion tons of food is lost or wasted every year in the world [2]. Having efficient agricultural practices allows obtaining an optimum use of the crop, a reduction of environmental pollution, and reduction of waste [3]. At present, these practices allow the farmer to supply the necessary amount of nutrients to the plants, at the time they need them
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