Accurate Image Recognition of Plant Diseases Based on Multiple Classifiers Integration

<p class="Normal1">Medicinal plants have been found and utilized in traditional medical practices from ancient times. Many medicinal plants play a vital role in curating many life threatening diseases. Very few of the medicinal herbs are commercially cultivated. Many plant diseases are there which destroys these medicinal plants. Early detection of plant diseases can prevent the huge loss of these medicinal plants. Here, we presented a hybrid model that makes use of SVM along with the traditional convolutional neural network (CNN) for predicting <em>Basil</em> plants leaves diseases. We transformed the conventional CNN model by adding a classification layer Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) after feature extraction and this approach tends to perform better than traditional CNN as we make the dataset balanced by data augmentation and SVN and KNN tend to perform better in case of balanced samples. CNN is used for training, SVM/KNN is used for classification. The advantages of CNN and SVM are used in proposed the CNN and SVM and KNN model. It is assumed that such a combined model would incorporate the benefits of CNN and SVM. Here, we identified the four types of diseases that affect <em>basil</em> plant leaves as Leaf spot, Downy mildew, Fusarium wilt, Fungal, and Healthy. Since there isn’t a standard dataset for <em>basil</em> leaves, we created our own 803 picture data set and used various machine learning techniques to train and evaluate the model. However, over other existing algorithms, our hybrid model i.e., CNN+SVM has produced more accurate results. For five classes of <em>basil</em> plant leaves, the proposed model produced 95.02% accuracy of for leaf diseases.</p>

Application of Artificial intelligence (AI) in agriculture sector plays an important role to enhance the yields by predicting and detecting the plant diseases and improving health of the crop. The early detection of plant diseases is crucial and helpful to the farmers to minimize the crop loss and ensure healthy crop. However, the traditional manual methods, are time consuming, error-prone, and inefficient for large-scale farming. Adopting recent technologies in Artificial Intelligence (AI) and Deep Learning, particularly Convolutional Neural Networks (CNNs) in the recent years have revolutionized the automation process of plant disease detection. However, singlemodal approaches depend on only RGB images often fail to capture critical physiological and biochemical changes in plants. To overcome these limitations, we propose a Single-Stream CNN in Multi-Modal Plant Disease Detection, integrating RGB, thermal, and hyperspectral imaging into a unified model. Unlike traditional multistream architectures that increase computational complexity, our model processes multi-modal data as a single 4-channel input tensor, optimizing feature fusion while maintaining computational efficiency. The proposed architecture, based on a Modified VGG-16 CNN model, which enhances disease detection accuracy by leveraging complementary information from different imaging modalities. Experimental evaluations demonstrate significant improvements in classification accuracy compared to RGB-only models. Furthermore, our model is optimized for real-time deployment on edge computing devices, making it scalable for precision agriculture applications, including automated greenhouse monitoring, drone-based crop surveillance, and IoTintegrated farming systems. This research work highlights the transformative potential of AI-driven multi-modal plant disease detection, flagging the way for more efficient, cost-effective, and scalable agricultural solutions.

Agricultural development is a critical strategy for promoting prosperity and addressing the challenge of feeding nearly 10 billion people by 2050. Plant diseases can significantly impact food production, reducing both quantity and diversity. Therefore, early detection of plant diseases through automatic detection methods based on deep learning can improve food production quality and reduce economic losses. While previous models have been implemented for a single type of plant to ensure high accuracy, they require high-quality images for proper classification and are not effective with low-resolution images. To address these limitations, this paper proposes the use of pre-trained model based on convolutional neural networks (CNN) for plant disease detection. The focus is on fine-tuning the hyperparameters of popular pre-trained model such as EfficientNetV2S, to achieve higher accuracy in detecting plant diseases in lower resolution images, crowded and misleading backgrounds, shadows on leaves, different textures, and changes in brightness. The study utilized the Plant Diseases Dataset, which includes infected and uninfected crop leaves comprising 38 classes. In pursuit of improving the adaptability and robustness of our neural networks, we intentionally exposed them to a deliberately noisy training dataset. This strategic move followed the modification of the Plant Diseases Dataset, tailored to better suit the demands of our training process. Our objective was to enhance the network's ability to generalize effectively and perform robustly in real-world scenarios. This approach represents a critical step in our study's overarching goal of advancing plant disease detection, especially in challenging conditions, and underscores the importance of dataset optimization in deep learning applications.

Abstract: Plant diseases are a major threat to crop yield and food security. Early detection and diagnosis are crucial to prevent the spread of disease and minimize crop losses. Plant diseases can have devastating effects on crop yields and food security. Disease detection is crucial for effective disease management. In recent years, deep learning techniques, such as Convolutional Neural Networks (CNN), have shown promising results in disease detection. In this study, we propose a plant disease detection system using CNN and Arduino. The system involves capturing images of plant leaves using a camera module connected to an Arduino board. In recent years, deep learning techniques such as Convolutional Neural Networks (CNNs) have shown great promise in image-based disease detection. Additionally, the use of low-cost microcontrollers like Arduino can provide a costeffective solution for real-time disease detection in the field. This paper proposes a plant disease detection system that combines CNN and Arduino for early and accurate disease detection. The system utilizes a CNN model to classify plant disease images and an Arduino board to analyze the results and provide real-time feedback to the user. The proposed system is tested on a publicly available dataset of plant disease images, achieving high accuracy, and demonstrating its potential for practical use. The results show that the proposed system can provide an affordable and efficient solution for early plant disease detection, facilitating prompt action and reducing crop losses.

Chapter 2 - Current status of plant diseases and food security

Abstract. Plant diseases are a critical barrier to agricultural sustainability, contributing to annual crop losses of 30–40% in some regions. Such diseases arise from diverse pathogens, including fungi, bacteria, and viruses, and can severely degrade crop yield and quality if undetected. Early diagnosis is crucial for timely intervention, reduced pesticide use, and long-term soil health. Traditional approaches, which rely on manual visual inspection of leaves, remain slow, subjective, and impractical for monitoring large or remote farms. In recent years, the convergence of artificial intelligence (AI), computer vision, and image-based analysis has enabled automated disease detection systems that are both scalable and real-time. Classical techniques first employed color, texture, and shape descriptors combined with machine learning models such as Support Vector Machines (SVM), k-Nearest Neighbor (KNN), and Random Forests. These methods achieved moderate success but struggled with variability in lighting, background, and leaf orientation. The introduction of deep learning, particularly Convolutional Neural Networks (CNNs), transformed plant disease detection by allowing end-to-end feature learning directly from raw images. Modern lightweight architectures like MobileNet and EfficientNet further enhance deployability on mobile and edge devices. In parallel, segmentation-assisted and hybrid models improve robustness under complex field conditions. This review consolidates these developments, evaluating methods in terms of accuracy, generalization, computational efficiency, and field-readiness. It also identifies persistent challenges—such as limited annotated datasets and the opaque decision-making of deep models—and highlights future directions in explainable AI, multi-modal sensing, and IoT-integrated precision agriculture.

Agricultural production is one of the main sources of income in most countries. Enormous losses will be incurred if agricultural product is disturbed by plant disease. The key to reduce losses in agricultural product output and quantity is early detection of plant diseases. A diseased plant usually reflecting its disease by showing symptoms on its leaves. A potato leaf disease classification technique by using image processing and artificial neural network method is proposed in this study. The method can be used to determine the potato leaf is either healthy or diseased. With the aid of this technique, farmers can save time and cost in their farming activities. The main goal of this study is to detect potato plant (Solanum tuberosum L.) disease using image processing techniques. The K-Means clustering algorithm is used to segment the disease in potato leaf image. The segmented features of potato leaf disease are then extracted by using Gray Level Co-occurrence Matrix (GLCM) and these features are then fed into ANN for classification. With the proposed system, classification accuracy obtained is 94%.

Plant diseases have a direct impact on the productivity of crops, and therefore the early detection of diseases is crucial.

A review on automated plant disease detection: motivation, limitations, challenges, and recent advancements for future research

The diagnosis of the plants is carried out with a visual inspection by experts and a biological examination is the second choice if necessary. They are usually expensive and time consuming. This inspired several computer methodologies to detect plant blights based on their leaf images. We apply a computer methodology on Deep Learning systems based on artificial neural networks, this branch also allows for the early detection of plant diseases, by applying convolutional neural networks (CNNs) familiar with some of the famous architectures, notably the “ResNet” architecture, using an augmented dataset containing images of healthy and diseased leaves (each leaf is manually cut and placed on a uniform background) with acceptable accuracy rates in the research environment. This Deep Learning technique has shown very good performance for various object detection problems. The model fulfills its role by classifying images into two categories (disease-free) and (diseased). According to the results obtained, the developed system achieves better detection performances than those proposed in the state of the art. Finally, to compare their performances, we use the implementation under Anaconda 2019.10.

Tomato leaf diseases significantly affect crop productivity, and manual inspection often leads to misclassification due to the visual similarity of symptoms. Recent studies have shown that Convolutional Neural Networks (CNN) provide high accuracy in leaf–based plant disease classification across various plant species, highlighting their potential for early disease detection. This study aims to develop an accurate tomato leaf disease classification system using a CNN model trained on the Kaggle tomato leaf dataset consisting of four classes: Leaf Blight, Bacterial Spot, Leaf Scab, and Healthy. The methodology includes literature review, dataset acquisition, preprocessing, augmentation, CNN architecture design, model training, and performance evaluation. Preprocessing techniques such as resizing and normalization were applied, followed by augmentation using random flipping and rotation to increase dataset variability. The proposed model was trained for 40 epochs with a batch size of 16. Results show consistent accuracy improvement, reaching 0.98 training accuracy with a loss of 0.07, while validation accuracy peaked at 0.94. Testing on both single and multiple images demonstrates strong prediction confidence, with minor misclassifications in visually similar cases. Overall, the system effectively identifies tomato leaf diseases and reinforces the suitability of CNN for supporting early plant disease detection in smart agriculture applications.

The agricultural sector contributes significantly to the gross domestic product of emerging nations. One of the primary factors that reduce agricultural productivity is plant diseases. Detecting plant diseases is a crucial step requiring attention for a productive economy and agriculture. Traditional techniques of crop disease detection are laborious and time-consuming, requiring a great deal of skill and effort. Academicians, researchers and practitioners have recently shown a great interest in the new area of automatic plant disease identification. As soon as they arise on plant leaves, early detection of plant diseases can be aided by machine learning (ML) and deep learning (DL). A comprehensive analysis has been conducted in this review paper to assess the viability to identify plant diseases applying machine learning models. This paper also considers the illnesses and viruses that affect different crops: black gram, chickpea, mungbean and potato. A detailed procedure has been done to recognise and classify plant diseases. The analysis of various ML and DL algorithms adopted by researchers to classify diseases has been explained. Finally, challenges are highlighted to recognise and classify plant diseases utilising ML and DL algorithms, particularly in legume crops. Developing and integrating real-time datasets on plant diseases using advanced models is the future for monitoring, detecting and managing plant ailments.

Modern reviews of challenges related to deep learning application in agriculture mention restricted access to open datasets with high-resolution natural images taken in field conditions. Therefore, artificial intelligence solutions trained on these datasets containing low-resolution images and disease symptoms in the advanced stage are not suitable for early detection of plant diseases. The study aims to train a convolutional neural network for apple scab detection in an early stage of disease development. In this study a dataset was collected and used to develop a convolutional neural network based on the sliding-window method. The convolutional neural network was trained using the transfer-learning approach and MobileNetV2 architecture tuned on for embedded devices. The quality analysis in laboratory conditions showed the following accuracy results: F 1 score 0.96 and Cohen’s kappa 0.94; and the occlusion maps — correct classification features.

Early detection of plant disease is a challenging research topic in crop protection and precision agriculture. Progress in remote sensing, embedded sensors and AI has enabled researchers to develop innovative data processing algorithms and models to predict plant disease symptoms. However, continuous research is required to update and develop new solutions for detecting plant disease early in the infection. It requires extensive experimentation and timelapse collection of data with different imaging sensors, platforms, plant samples, and environmental conditions before developing a robust model. This paper proposes a conceptual design of an apparatus that can be used for data collection from different imaging sensors to perform high-throughput phenotyping and analysis for early plant disease diagnosis. The paper includes two different design concepts for two distinct scenarios of data collection, 1. on-field standalone device; and 2. laboratory desktop apparatus. Each design is explained with various operational and technical considerations, an electronic block diagram and a mechanical 3D sketch. The paper aims to provide a generic design of an apparatus to enable researchers worldwide to carry out phenotypic studies and develop innovative solutions to benefit the end-user, i.e., the farmer, and reduce global economic and environmental losses.

Plant diseases are a vital risk to crop yield and early detection of plant diseases remains a complex task for the farmers due to the similar appearance in color, shape, and texture. In this work, authors have proposed an automatic plant disease detection technique using deep ensemble neural networks (DENN). Transfer learning is employed to fine-tune the pre-trained models. Data augmentation techniques include image enhancement, rotation, scaling, and translation are applied to overcome overfitting. This paper presents a detailed taxonomy on the performance of different pre-trained neural networks and presents the performance of a weighted ensemble of those models relevant to plant leaf disease detection. Further, the performance of the proposed work is evaluated on publicly available plant village dataset, which comprises of 38 classes collected from 14 crops. The performance of DENN outperform state-of-the-art pre-trained models such as ResNet 50 & 101, InceptionV3, DenseNet 121 & 201, MobileNetV3, and NasNet. Performance evaluation of the proposed model demonstrates that effective in categorizing various types of plant diseases that comparatively outperform pre-trained models.