Plant diseases adversely affect the quantity and quality of food production, contributing to food insecurity. Prompt identification, diagnosis, and intervention can significantly minimize economic and ecological losses. By reducing the use of agrochemicals through timely disease detection, the environmental impact can be mitigated. Traditional manual methods for recognizing plant diseases are prevalent but are often limited, time-consuming, costly, and ineffective. Convolutional Neural Network (CNN) architectures have demonstrated excellent capabilities in detecting plant diseases and other complex images, but they lack spatial or rotational invariance and require extensive data in various forms to be effective. This is typically achieved by applying data augmentation, as the datasets in the field of agriculture are often limited. Capsule Networks address CNN's limitations, but their encoder network is inefficient at feature extraction, hence does not perform well on complex images. This study seeks to modify and improve CapsNet by combining a Color Difference Histogram (CDH) with a Capsule Network that includes extra two convolutional, three max pooling layers, three batch normalization layers, and reduced the primary capsule channels in the original CapsNet to 16 from 32 for efficient plant disease detection in apples, bananas, grapes, corn, mangoes, pepper, potatoes, rice, tomato, and on the CIFAR-10 dataset. This approach improved the original CapsNet in terms of validation accuracies by 5.83%, 14.82%, 5.9%, 4.42%, 20.87%, 40.12%, 4.41%, 0.76%, 9.49%, and 13.97% on apple, banana, grape, corn, mango, pepper, potato, rice, tomato, and CIFAR-10 datasets respectively. The CDH-CapsNet achieved better results in terms of accuracy, sensitivity, F1-Score, precision, specificity, Receiver Operating Characteristic (ROC), Precision-Recall (PR) values, parameter count, and disk size, surpassing the original CapsNet and CapsNet models presented in available research. The original CapsNet and CDH-CapsNet exhibited strong performance on datasets such as the Rice dataset, possibly because of high-quality images and low intra-class variance. The findings suggest that this efficient and computationally less demanding supportive tool can significantly enhance plant disease classification by offering a lightweight, scalable solution that can be adapted for field use in resource-constrained settings, contributing to efforts aligned with the SDG 2 goal. However, environmental factors such as inconsistent lighting and complex backgrounds encountered in practical  
scenarios may affect the model's effectiveness.  Subsequent studies will aim to overcome these issues and broaden the model's applicability.