A Lightweight Neural Network-Based Method for Identifying Early-Blight and Late-Blight Leaves of Potato
Source abstract
Abstract
Crop pests and diseases are one of the most critical disasters that limit agricultural production. In this paper, we trained a lightweight convolutional neural network model and built a Django framework-based potato disease leaf recognition system, which can recognize three types of potato leaf images including early blight, late blight, and healthy. A lightweight, neural network-based model for the identification of early potato leaf diseases significantly reduces the number of model parameters, whereas the accuracy of Top-1 identification is over 93%. We imported the trained model into the Django framework to build a website for a potato early leaf disease identification system, thus providing technical support for the implementation of a mobile-based potato leaf disease identification and early warning system.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Spectroscopy and Chemometric Analyses · Leaf Properties and Growth Measurement
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Inner Mongolia Agricultural University, Hohhot 010018, China
Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, Hohhot 010018, China
Funding
Natural Science Foundation of Inner Mongolia Autonomous Region of China · Research Program of science and technology at Universities of Inner Mongolia Autonomous Region of China · National Natural Science Foundation of China · Inner Mongolia Agricultural University High-level Talent Research Start-up Project
References (35)
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