Plant Leaf Disease Classification

Crops of fruits and vegetables are very prone to various plant diseases, fungi, bacterial, and pest attacks. If these are not diagnosed earlier, they can spread quickly over the entire area and lead to reduced crop quality and yield and huge economic losses for the farmers and growers. As crop diseases appear at various developmental stages of plants, it becomes necessary to monitor them regularly.

Traditionally, crop disease identification is done manually through the inspection of the plants and leaves by the farmers, agronomists, or field specialists. This process can work, but the manual inspection of large agricultural land is very time-consuming and labour-intensive and prone to human errors. The early signs can also be hard to detect due to the similar pattern of the symptoms produced by multiple diseases.

In the following paper, a pretrained deep learning model for fruit and vegetable disease detection is presented, which allows for automating the process of crop disease identification based on plant and leaf images. The model uses the images of healthy and diseased leaves as the training dataset, allowing it to identify the patterns of various diseases.

Through the use of deep learning and computer vision to monitor agriculture, the model is able to detect diseases from crop images quickly. This would enable farmers, agricultural researchers, agronomists, and precision agriculture experts to monitor their crop status more efficiently and even identify any disease outbreaks much earlier than before.

This will give the growers an opportunity to examine the infected crops and manage their pests and diseases accordingly. The farmer no longer needs to conduct a broad inspection of their farm to find the infected areas.

The model could be used in precision agriculture, smart farming, crop monitoring, agricultural drone images, mobile apps, and an automated plant disease identification process. If used together with drone images or field images, the deep learning-based model for disease detection would become more scalable and could be used in larger agricultural lands.

This would be able to increase the efficiency of crop monitoring and make decisions on time to avoid losses.