Crop Health Detection Using Image Processing and Machine Learning for Better Yield Production

Abstract

Agriculture plays a vital role in every nation, as a healthy population relies on robust yields to ensure food security. With the continuous growth of the population, the excessive use of pesticides and fertilizers has become prevalent, which can negatively impact crop health. In this paper, we introduce a solution that utilizes images of crops, employing image processing and machine learning techniques to classify them as healthy or unhealthy. Various feature detection and extraction methods are available, but we specifically compare Oriented FAST and Rotated BRIEF and scale-invariant feature transform in this work. Both techniques can effectively extract features from the images, and we can use the matcher function from OpenCV to determine if the extracted features correspond to those of a trained image. If there is a match, it indicates an unhealthy crop, while a lack of matching suggests the crop is healthy. Additionally, machine learning classifiers can be employed to enhance training on these extracted features, leading to improved results and predictions.

Code and data availability

The paper's appendices explicitly provide a public GitHub repository containing the dataset of wheat crop images used for the SIFT/ORB crop health detection experiments.