A methodology of weed-crop classification based on autonomous models
  choosing and ensemble
  Neural networks play an important role in crop-weed classification have high
accuracy more than 95%. Manually choosing models and fine-tuning are laborious,
yet it is indispensable in most traditional practices and researches. Moreover,
classic training metric are not thoroughly compatible with farming tasks, that
a model still have a noticeable chance of miss classifying crop to weed while
it reach higher accuracy even more than 99%. In this paper we demonstrate a
methodology of weed-crop classification based on autonomous models choosing and
ensemble that could make models choosing and tunning automatically, and improve
the prediction with high accuracy(>99% for both data set) in specific class
with low risk in incorrect predicting.
