Computationally light deep learning framework to recognize cotton leaf diseases

Abstract

Cotton is an important commodity because of its use in various industries across the globe. It is grown in many countries and is imported/exported as a cash crop due to its large utility. However, cotton yield is adversely affected by the existence of pests, viruses and pathogenic bacteria, etc. For the last one decade or so, several image processing/deep learning-based automatic plant leaf disease recognition methods have been developed but, unfortunately, they rarely address the cotton leaf diseases. The proposed work presents a simple yet efficient deep learning-based framework to recognize cotton leaf diseases. The proposed model is capable of achieving the near ideal accuracy with early convergence to save computational cost of training. Further, due to the unavailability of publicly available datasets for this crop, a dataset is also collected comprising of three diseases namely curl virus, bacterial blight and fusarium wilt in addition to the healthy leaf Images. These images were collected from the Internet and fields of Southern Punjab region in Pakistan where the cotton crop is grown on thousands of acres every year and is exported to the Europe and the US either as a raw material or in the form of knitted industrial/domestic products. Experimental results have shown that almost all variants of our proposed deep learning framework have shown remarkably good recognition accuracy and precision. However, proposed EfficientNet-B0 model achieves 99.95% accuracy in only 152 seconds with best generalization and fast inference.

A Lightweight Meta-Ensemble Approach for Plant Disease Detection Suitable for IoT-Based Environments

“…This dataset [27] consists of 1518 images of four different classes of cotton leaf disease images captured under realworld conditions and also from the internet. The images of in Table 1.…”

Detection of cotton leaf disease with machine learning model

“…Various studies were conducted in 2015 to categorize cotton leaf illnesses utilizing various pattern recognition techniques for classification and identification of cotton leaf diseases. The horticultural area in which the production level of the cotton crop is identified by using different technological strategies [12]. For the detection and categorization of three cotton leaf diseases-Alternaria, Myrothecium, and Bacterial blight-Rothe PR and Kshirsagar RV presented a pattern recognition method.…”

Evaluation of CNN Models in Identifying Plant Diseases on a Mobile Device

“…Several sources of datasets were used in this research, these are The PlantVillage dataset that collected by Hughes and Salathé [13], with extended cassava leaf disease that used in competition held by Makerere University AI Lab [14], Plant Pathology dataset that was the dataset of Plant Pathology 2020 Challenge [15], a robusta coffee leaf images called RoCole dataset [16], then an apple scab dataset collected by Institute of Hulticulture [17], bean disease dataset from public domain, PlantDoc dataset [18], corn leaf disease dataset [19], cotton leaf disease dataset [20], DiaMOS plant dataset which contains four pear leaf disease [21], some disease species from Digipathos dataset [22] that have more than 20 images, rice leaf diseases dataset that gathered from a farming community [23], citrus leaf diseases dataset [24], cassava leaf diseases dataset [25], bean diseases dataset [26], and some manual searching for two classes, rice gray leaf spot and rice healthy. Total images were 104,282 images consisting of 21 plant species in 79 different classes with 19 healthy plants and 60 combinations of diseased plants.…”