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The choice of methodologies is a pivotal factor in disease identification, particularly when computer vision intersects with agricultural innovation. Safeguarding global food supplies depends on the timely and accurate identification of crop diseases. Deep learning offers a feasible pathway to satisfy this demand. To move this research forward, we engineered and tested a disease identification framework grounded in a novel ensemble strategy. Our contribution includes the presentation of DenseNetMini, a more compact iteration of the DenseNet architecture. We advocate merging DenseNetMini with a learning resizer via an ensemble approach to improve training fidelity and accelerate knowledge acquisition. A further innovative element involves deploying Gradient Product (GP) as the optimization mechanism, efficiently reducing the training time while simultaneously improving model performance. Investigating imagery at varying magnifications reveals remarkable improvements in diagnostic consensus and precision. Accuracy figures on test data reach 99.65%, 98.96%, and 98.11% for the Plantvillage, Tomato leaf, and Appleleaf9 collections, respectively. A core triumph of this investigation is the substantial reduction in computational time, suggesting that GP adoption could make agricultural disease screening more accessible and operationally efficient. Beyond numerical gains, the work foregrounds Explainable Artificial Intelligence (XAI) approaches as indispensable for enhancing the interpretability and clarity of the disease detection framework. By visually pinpointing diagnostically relevant zones on foliage, XAI enhances the model’s comprehensibility, thereby nurturing user confidence and insight into how it arrives at its conclusions.

Leaf blight, caused by bacterial and fungal infections, threatens plant health by inducing leaf discoloration and reducing crop yield. To address this, we propose a MobileNetV3-based classifier for the Jasmine plant, leveraging streamlined Convolutional Neural Networks (CNNs) to precisely identify disease stages. The design combines depthwise convolutional layers with max-pooling layers to extract superior features, focusing on vital low-level features that signal the condition. Via preprocessing methods that include data augmentation with a Conditional GAN and Particle Swarm Optimization for feature selection, the classifier achieves reliable performance. Assessment on carefully assembled datasets shows a remarkable 97% training accuracy, demonstrating its potency. Real-world trials across varying settings, including severe camera tilts and fluctuating illumination, verify the model’s durability, delivering test accuracies within the 94% to 96% range. The dataset’s bespoke structure for CNN-based classification ensures the reliability of the outcomes. Notably, the model’s compact classification, characterized by swift processing speed and minimal size, makes it a practical choice for immediate deployment. This all-encompassing strategy highlights the presented classifier’s value in managing leaf blight spot disease difficulties in cash crops.

A large segment of India’s rural population depends on agriculture for its livelihood, yet difficulties with manual processes and disease management persist. To address these problems, this study presents an automated framework for detecting leaf damage and identifying plant diseases using advanced deep learning methods. The introduced technique unfolds in six steps: first, YOLOv8 is harnessed to pinpoint regions of interest from drone-acquired images; second, DeepLabV3+ is applied for background stripping to aid disease categorization; third, a CNN model is deployed for reliable disease sorting, registering strong training and validation accuracy figures (96.97% and 92.89%, respectively); fourth, UNet-based semantic segmentation is utilized for meticulous pixel-scale damage identification, with an assessment accuracy of 99%; fifth, disease severity is gauged; and sixth, customized remedial measures are put forward according to the disease category and the extent of damage. Experimental trials using the Plant Village dataset confirm the technique’s ability to detect various defects across crops, including apple, tomato, and corn. This mechanized strategy carries significant potential to boost agricultural output and strengthen disease management both within India and globally.

Achieving optimal cannabis crop performance and product quality hinges on dependable, automated systems for leaf disease identification that allow for swift diagnosis and action. Present-day automated methods fall short of being purpose-built for the distinct difficulties inherent in spotting cannabis diseases, rarely holding up across shifting environmental contexts and varying phases of plant maturity. This paper proposes a novel Hybrid Adaptive Multi-Intelligence System for Deep Learning Ensembles (HyAMIS-DLE), grounded in an extensive data collection that mirrors the breadth of cannabis leaf pathologies and their unfolding. The design pairs non-population-based decision merging within the image preparation pipeline with population-based decision merging at the classification stage, calling upon a variety of CNN backbones. The outcome is a meaningful leap in key performance measures: HyAMIS-DLE achieves 99.58% accuracy, besting standard architectures by up to 4.16%, while also offering markedly improved resilience and a boosted Area Under the Curve (AUC) metric, enabling a clear distinction between unaffected and afflicted foliage. Rolling HyAMIS-DLE into our Automated Cannabis Leaf Disease Classification System (A-CLDC-S) confirms its on-the-ground value, helping achieve higher harvest volumes, reduce waste, and advance ecologically sound farming practices.

Forest health is under mounting pressure from diseases driven by invasive pathogens, and the ability to detect these diseases early and precisely is vital for targeted, prompt forest management. Breakthroughs in spectral imaging and artificial intelligence have paved the way for novel approaches to plant disease identification in both crop and tree species. The present study employed the Dutch elm disease (DED; caused by Ophiostoma novo-ulmi) and American elm (Ulmus americana) pathosystem as a model to test the efficacy of two custom-fabricated, high-precision, portable hyper- and multispectral leaf scanners, paired with machine learning, as innovative instruments for forest disease surveillance. Hyper- and multispectral imagery was collected from leaves of American elm genotypes with varying degrees of disease susceptibility following mock inoculation and inoculation with O. novo-ulmi in controlled greenhouse conditions. Both classical machine learning and cutting-edge deep learning architectures were trained on extracted spectra and directly on spectral image volumes. Deep learning frameworks that leverage combined spectral and spatial information from high-definition spectral leaf images surpass classical machine learning approaches that rely exclusively on spectral data for DED identification. Within the deep learning framework, leaf margins and symptomatic lesions were accentuated as discriminative spatial cues for separating leaves originating from inoculated versus mock-inoculated specimens. Furthermore, the spectral and spatial signature profiles uncovered by the machine learning-based models corresponded to the DED susceptibility levels of the elm genotypes. While supplementary investigations are warranted to gauge transferability across other pathosystems, hyper- and multispectral leaf scanners integrated with machine learning show considerable promise as emerging tools for disease phenotyping in tree species.

Vegetation cultivated in aquaponics environments is prone to multiple diseases or biotic pressures throughout its development, similarly to conventional farming. Timely identification of these diseases is essential to maintaining the effectiveness of the aquaponics setup and advancing its development. Aquaponics merges recirculating aquaculture with soilless hydroponic cultivation and has the potential to ensure food security, alleviate water shortages, and reduce carbon emissions. To facilitate the widespread adoption of this agricultural method, an integrated platform is required to identify plant ailments and assist scientists and growers in identifying potential causes and remedies at an early stage. This research introduces an automated plant diagnostic framework for recognizing biotic pressures and controlling diseases in four leafy green vegetables—lettuce, basil, spinach, and parsley—cultivated within an aquaponics environment. Initially, a collection of 2640 images is assembled. Subsequently, a disease identification system operating in three stages is created. The initial stage involves a crop classification mechanism that determines the plant species. The second stage comprises a disease identification mechanism that assesses the plant’s health condition. The concluding stage is a disease-detection mechanism that pinpoints diseased and healthy areas on foliage and classifies the illness. The suggested method has yielded encouraging results, achieving accuracies of 95.83%, 94.13%, and 82.13% in the three stages, respectively. The ultimate disease-detection mechanism is then combined with an ontology framework via a cloud-based interface. This ontology framework includes domain expertise concerning plant pathology, specifically the origins and remedies for various ailments of the examined leafy green vegetables, which can be automatically retrieved upon illness detection, enabling farm operators to implement preventive actions. The suggested application is important as a decision-support tool that automates health surveillance in aquaponics facilities and helps agricultural practitioners make decisions on crop and disease control.

Global food security is critically threatened by plant diseases, which can be especially catastrophic for smallholder farmers whose economic survival hinges on a productive groundnut crop. It is therefore vital to engineer an algorithm capable of early, automated plant disease diagnosis. Working alongside a pathologist, a large-scale dataset of groundnut leaf imagery was assembled to enable this automated detection. In image classification tasks, Convolutional Neural Networks have demonstrated remarkable efficiency. This investigation constructs a disease identification and classification system using a tri-CNN architecture that incorporates DenseNet169, Inception, and Xception, all of which were initially trained on the ImageNet database. The proposed ensemble strategy formulates final predictions for test data by applying two custom nonlinear functions to the decision outputs of these base learners, integrating their scores across four standard performance measures: recall, precision, accuracy, and F1-score. The tailored CNN models are thus utilized to train our system for superior results. The methodology was tested on a real-world groundnut leaf dataset. The introduced model registered an accuracy reading of 98.46%.

Agricultural production is affected by plant diseases, affecting both quality and quantity. Yet, forecasting these diseases demonstrates its value for crop quality and reducing the chance of yield losses. The process of detecting plant diseases—whether by the naked eye or through conventional methods—is generally painstaking, time-consuming, and subject to substantial margins of error. This work presents a comprehensive exploration of diverse CNN architectures paired with distinct optimization algorithms, applied to olive disease identification via classification methods, and identifies the optimal model for an efficient disease detector. The exploration presents a collection of 5571 olive leaf images, gathered by hand in field settings across different regions of Morocco, and additionally includes a healthy class to identify olive diseases. Moreover, a primary aim of this research was to explore the interplay between CNN architectures and optimization algorithms as measured by accuracy and additional performance indicators. The peak score in the trained models reached 100%, while the peak score in experiments without data augmentation was 92.59%. A further emphasis of this exploration is the effect of optimization algorithms on neural network functioning. Based on the experiments, the MobileNet architecture with Rmsprop outperformed the other pairings in both detection performance and efficiency.

In recent years, the deployment of artificial intelligence (AI) in farming has emerged as a critical priority. Embracing technological innovation in agriculture, when pursued imaginatively, unlocks significant value. A vital procedure involves managing affected leaves throughout crop cultivation. The prompt identification, categorization, and examination of foliar diseases, together with viable remedies, perpetually foster agricultural progress. A central emphasis of our proposed study is the detection and categorization of diseases across different crops, specifically targeting tomatoes and grapes. The principal aim is to anticipate the type of disease likely to affect grape and tomato foliage at an early stage. Techniques employing Convolutional Neural Networks (CNNs) are harnessed for Multi-Crop Leaf Disease (MCLD) detection. A deep learning-centric model carried out image feature extraction to differentiate between infected and vigorous leaves. The CNN-dependent Visual Geometry Group (VGG) model is applied to achieve superior performance indicators. A collection of crop leaf imagery serves to both train and evaluate the model. Metrics gauging performance, including accuracy, sensitivity, specificity, precision, recall, and the F1-score, were determined and observed. The overarching research aim with the proposed framework is to secure steady enhancements in outcomes. The engineered model distinguishes leaves impacted by disease with heightened precision. In the experimental setup, the suggested investigation achieved 98.40% accuracy for grapevines and 95.71% for tomato plants. This body of work directly contributes to boosting agricultural food output.