Advanced Concepts for Intelligent Vision Systems: 20th International Conference, ACIVS 2020, Auckland, New Zealand, February 10–14, 2020, Proceedings

Deep Learning has solved complicated applications with increasing accuracies over time. The recent interest in this technology, especially in its potential application in agriculture, has powered the growth of efficient systems to solve real problems, such as non-destructive methods for plant anomalies recognition. Despite the advances in the area, there remains a lack of performance in real-field scenarios. To deal with those issues, our research proposes an efficient solution that provides farmers with a technology that facilitates proper management of crops. We present two efficient techniques based on deep learning for plant disease recognition. The first method introduces a practical solution based on a deep meta-architecture and a feature extractor to recognize plant diseases and their location in the image. The second method addresses the problem of class imbalance and false positives through the introduction of a refinement function called Filter Bank. We validate the performance of our methods on our tomato plant diseases and pest dataset. We collected our own data and designed the annotation process. Qualitative and quantitative results show that despite the complexity of real-field scenarios, plant diseases are successfully recognized. The insights drawn from our research helps to better understand the strengths and limitations of plant diseases recognition.