Use of leaf color images to identify nitrogen and potassium deficient tomatoes

Soilless culture has been popular in modern agriculture. However, plants in soilless culture often appear to be nutrient deficient. Therefore the intellective diagnostic system of plants disease of nutrients deficiency is important. In this paper, a novel idea based on computer vision is presented. Color and texture features of leaves are extracted by some methods such as percent intensity histogram, percent differential histogram, Fourier transform, and wavelet packet. Moreover, Genetic Algorithm (GA) has been used to select features to get the best information for diagnosing the disease. Experiments showed that the accuracy of this diagnostic system is above 82.5% and it can diagnose disease about 6–10 days before experts could determine.

► According to the nutrients-deficiency symptoms, color and texture features are extracted, and GA is used to optimize the features combination.
► The establishment of binary tree classification framework cuts down the difficulty greatly in pattern recognition.
► Experiments showed that the system is capable of diagnosing the disease about 6-10 days before symptoms can be recognized by experts.