Identification of Plant Nutrient Deficiencies Using Convolutional Neural Networks

A novel image analysis method for identifying nutrient deficiencies in plant based on its leaf is proposed. First, the proposed method divides an input leaf image into small blocks. Second, each block of leaf pixels is fed to a set of convolutional neural networks (CNNs). Each CNN is specifically trained for a nutrient deficiency and is utilized to decide if a block is presenting any symptom of the corresponding nutrient deficiency. Next, the responses from all CNNs are integrated to produce a single response for the block using a winner-take-all strategy. Finally, the responses from all blocks are integrated into one using a multi-layer perceptron to produce a final response for the whole leaf. Validation of the proposed method was performed on a set of black gram (Vigna mungo) plants grown under nutrient controlled environments. Five types of deficiencies, i.e., Ca, Fe, K, Mg, and N deficiencies, and a group of plants with complete nutrients were studied. A dataset consisting of 3,000 leaf images was collected and used for experimentation. Experimental results indicate the superiority of the proposed method over trained humans in nutrient deficiency identifications Plants are heavily reliant on nutrients present in the soil. In some cases, such as with nitrogen or phosphorus, the plant can send these nutrients from old tissue to new tissue in the event of a deficiency in the environment. This experiment sought to analyze the effects of certain nutrient deficiencies over a four week span of growth. It was discovered that nitrogen deficiency, phosphorus deficiency, and complete nutrient deficiency all led to significant differences in growth capacity over the span of the experiment. Standard chlorophyll content also showed significant differences when comparing the nitrogen-deficient and phosphorus deficient treatments with the complete nutrient treatment. These results indicated that mobility of nutrients cannot completely offset a deficiency of said nutrient in the environment it can only help the plant attempt to survive the deficiency

Keywords - nutrient deficiency leaf, image analysis, machine learning, CNN, ANN, DenseNet121