# Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review

arXiv: https://arxiv.org/abs/2009.04365 · HTML: https://mlsanity.com/paper/2009.04365/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection](https://mlsanity.com/paper/2106.07770/) (2021)
- [Embrace Limited and Imperfect Training Datasets: Opportunities and Challenges in Plant Disease Recognition Using Deep Learning](https://mlsanity.com/paper/2305.11533/) (2023)
- [LWMSCNN-SE: A Lightweight Multi-Scale Network for Efficient Maize Disease Classification on Edge Devices](https://mlsanity.com/paper/2601.07957/) (2026)
- [Design and Implementation of FourCropNet: A CNN-Based System for Efficient Multi-Crop Disease Detection and Management](https://mlsanity.com/paper/2503.08348/) (2025)
- [A Resource-Efficient Hybrid CNN-LSTM network for image-based bean leaf disease classification](https://mlsanity.com/paper/2604.13835/) (2026)
- [AgriKD: Cross-Architecture Knowledge Distillation for Efficient Leaf Disease Classification](https://mlsanity.com/paper/2605.01355/) (2026)

## Abstract (arXiv)

Plant diseases are considered one of the main factors influencing food production and minimize losses in production, and it is essential that crop diseases have fast detection and recognition. The recent expansion of deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. In this context, this work presents a systematic review of the literature that aims to identify the state of the art of the use of convolutional neural networks(CNN) in the process of identification and classification of plant diseases, delimiting trends, and indicating gaps. In this sense, we present 121 papers selected in the last ten years with different approaches to treat aspects related to disease detection, characteristics of the data set, the crops and pathogens investigated. From the results of the systematic review, it is possible to understand the innovative trends regarding the use of CNNs in the identification of plant diseases and to identify the gaps that need the attention of the research community.