Diagnosis of common cauliflower diseases using image processing and deep learning
Cauliflower is a very healthy vegetable that is an important source of nutrients that is naturally rich in fiber and B vitamins. It provides antioxidants and phytonutrients that can protect against cancer. It also contains fiber for weight loss and digestion, choline, which is essential for learning and memory, and many other important nutrients. Factors such as lack of nutrients, weather conditions and diseases cause the growth of cauliflower to be accompanied by problems. Cauliflower contains various plant pathology diseases; but the traditional method for identifying diseased cabbages and separating them is tedious and time-consuming, so they can be identified in the shortest possible time using imaging techniques and artificial neural networks. The purpose of this research is to categorize cauliflower into 4 healthy groups, infected with powdery mildew, black rot and bacterial soft rot using LeNet image processing and deep learning techniques. First, a total of 655 color images including the mentioned 4 classes were prepared. 70% of the data was considered for model training. The results showed that the model was able to identify healthy cauliflowers, cauliflowers infected with black rot and powdery mildew with 100% accuracy. Cauliflower infected with bacterial soft rot could be identified with 99% accuracy.
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