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Crop diseases constitute a danger to worldwide food stability, diminishing harvest yield, grade, and total farming efficiency. Conventionally, the identification of plant ailments has depended on lengthy visual assessments by specialists, a method that frequently results in mistakes. Machine learning (ML) and artificial intelligence (AI), in particular Vision Transformers (ViTs) and Convolutional Neural Networks, deliver a swifter, automated substitute for recognizing plant illnesses through leaf image analysis. However, these frameworks are regularly faulted for their "black box" character, which curbs confidence in their outputs due to a lack of clarity. Our outcomes indicate that integrating Explainable AI (XAI) approaches like Grad-CAM, Integrated Gradients, and LIME notably boosts model explainability, rendering it simpler for users to detect the fundamental indicators of plant sicknesses. This investigation not only adds to the area of plant disease recognition but also supplies an original viewpoint on advancing AI openness in actual farming settings through the application of XAI methods. With training accuracy rates of 100.00% for ViT, 96.88% for EfficientNetB7, 93.75% for EfficientNetB0, and 87.50% for ResNet50, and equivalent validation accuracy rates of 96.39% for ViT, 86.98% for EfficientNetB7, and 82.00% for EfficientNetB0, our suggested architectures exceed earlier studies performed on the identical dataset. This illustrates a considerable leap in model capability while upholding clarity and dependability by way of explainable and trustworthy decision procedures.

Quantifying soil total nitrogen (TN) with high accuracy is vital for boosting crop vigor and quality. A major obstacle, however, is that soil diversity from one region to another undermines the transferability of calibration models. To confront this obstacle, this paper puts forward a framework called transfer-learning-assisted laser-induced breakdown spectroscopy for cross-regional soil analysis (TransLIBS-CRS), which is custom-built to counteract the weak cross-domain predictive capacity caused by differences in regional soil characteristics. By adapting the model with a small batch of samples from the target setting, the technique substantially elevates the cross-domain utility of LIBS measurements, easing the difficulties tied to procuring soil specimens from geographically dispersed locations. In tests where Beijing data were used to infer TN levels in Guangzhou, the TransLIBS-CRS model yielded the strongest outcomes, recording an R2V of 0.846 and an RMSEV of 0.814 g/kg. A closer look via saliency mapping and chemometric tools indicated that carbon emission wavelengths at 193.0 nm and 247.8 nm serve a decisive function in TN quantification. Crucially, these spectral markers kept their predictive power unchanged when carried over to the Guangzhou soil dataset. This tactic furnishes a workable route toward broad-scale, high-efficiency soil TN assessment.

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.

Remarkable progress has been made in applying deep learning models to distinguish between weed and crop species within agricultural settings. Still, a significant gap persists in the body of work on how models are trained and evaluated when environmental conditions diverge. The dominant tendency involves either dividing a single dataset distribution into training, validation, and test splits or first pooling datasets originating from varied conditions or distributions and then partitioning them. What our study brings forward is an effort to close this gap: we incorporate multiple broadleaf weed categories spanning dissimilar distributions, probe what happens when convolutional neural networks are trained exclusively on condition- or distribution-specific data, and then gauge their behaviour in entirely foreign environments across three experimental setups. By surveying an array of network designs alongside distinct learning modes (fine-tuning versus feature extraction), testing multiple architectures, adopting varied training protocols, and consolidating data, we arrived at straightforward guiding principles that ensure models retain precision and accuracy even under starkly contrasting deployment conditions. Experiment 1, confined to a single uniform environment, produced accuracies of 80%–100% across models and strategies; fine-tuning yielded higher accuracies (94%–99.9%) than feature extraction (80%–92.96%). Experiment 2 showed a steep drop-off in performance: whenever the test partition came from sources other than the training and validation sets, accuracy ranged from 25% to 60%, most often around 40%. Experiment 3, centred on merging datasets and distributions, delivered encouraging numbers — ResNet in fine-tuning mode attained a high of 99.6%, whereas InceptionV3 in feature extraction mode dropped to a low of 69.9%. These core insights underscore that fusing data from mismatched distributions, paired with fine-tuning on modern backbones like ResNet and MobileNet, markedly improves capability, unlike the comparatively modest outputs of leaner architectures such as AlexNet. Our results suggest that accepting data heterogeneity and flexible training regimens are fundamental to sharpening weed classification pipelines. The study provides a workable approach for handling diverse datasets that reflect genuine agricultural variability.

In some Asian countries, jute is a fundamental source of revenue and contributes to Gross Domestic Product (GDP). Like other crops, jute is vulnerable to pest invasions, and detecting them often relies on visual inspection in countries such as Bangladesh, India, Myanmar, and China. This practice is also slow, difficult, and somewhat inaccurate, posing a significant economic risk. To mitigate this challenge, the investigation proposes a highly accurate and resilient transfer learning (TL)- based JutePestDetect model for detecting jute pests at their initial stages. To begin with, we assembled a jute pest collection comprising 17 categories and roughly 380 images per category, which were scrutinized after manual and automated preprocessing and refinement, including background removal and scaling. Following this, five distinguished pre-trained architectures—DenseNet201, InceptionV3, MobileNetV2, VGG19, and ResNet50—were picked from an earlier work to formulate the JutePestDetect model. Every architecture was adjusted by replacing the classification layer with a global average pooling layer and inserting a dropout layer for regularization. For performance measurement, a range of indicators, including precision, recall, F1 score, ROC curve, and confusion matrix, were used. These examinations provided a deeper understanding of the models’ capabilities. Within this set, the proposed JutePestDetect model, based on a tailored regularized DenseNet201, outperformed its counterparts, achieving an outstanding accuracy of 99%. Hence, our suggested method and tactics deliver a refined solution for pest recognition specific to jute, with the potential to substantially assist cultivators worldwide.

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.