Extracting and selecting features of leaf images for diagnosing nutrient deficiency diseases in tomatoes

Diagnosing nutrient deficiency diseases of tomatoes in soilless agriculture was studied. Image features that were not recognized by human eyes easily from nutrient deficient (N,K) tomato leaves were extracted and selected, so that diagnosing accuracy was improved. The color features of nutrient deficient tomato leaves were extracted by the percent color histogram method. The texture features of the leaves were extracted by the percent differential histogram method. The texture features of the frequency region were extracted by the Fourier Transform method. The texture features of the time-frequency region were extracted by the wavelet packet method. Application of genetic algorithm to select the above-mentioned features was studied in order to get the best information for diagnosing. The experimental results show that: the accuracy of this diagnosis system is higher, its accuracy reaches 95%～92%, and it can accurately diagnose diseases 6～10 d before their symptoms can be recognized by human eyes, and thus reduce damage to the agricultural products.