The objective of this work was to evaluate image spectral indices to detect nitrogen levels applied to bean plants. The experiment was in pots with 8 dm³ of soil, five treatments (0; 50; 100; 150 and 200 kg of N ha-1) with ten repetitions. The images were taken at 30; 40 and 50 days after emergence. Quadratic discriminant functions were developed, and the input vector was the average of the pixels from different combinations of four spectral indices. Three different sizes of image blocks were tested: 9 x 9, 20 x 20 and 40 x 40 pixels. The best results were obtained when using the 9 x 9 and 20 x 20 pixels blocks, with classification performance of 94; 96 e 96% better than a random classification for the blocks 9 x 9 pixels and 92; 94 and 94% for the blocks 20 x 20 pixels at 30; 40 e 50 days after emergency, respectively.
precision farming; digital image processing; statistical classification
