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Accurate identification of rice diseases in precision agriculture using imagery remains difficult due to substantial misclassification rates, a wide range of influencing factors, and fluctuating conditions. While machine learning together with convolutional neural networks has yielded encouraging results for detecting rice diseases, these approaches have struggled to elucidate the interconnections among disease characteristics. In this work, we introduce an enhanced classification framework for rice diseases that combines a convolutional neural network (CNN) with a bidirectional gated recurrent unit (BiGRU). Concretely, a residual connection was embedded into the Inception module, its depth was enlarged, and a refined Convolutional Block Attention Module (CBAM) was incorporated. The upgraded CNN and the BiGRU were trained and evaluated separately; their outputs were then concatenated and forwarded to the classification layer for final prediction. Trials confirm that this strategy achieves 98.21% classification accuracy across four rice disease categories, delivering a robust solution for rice disease detection research.

An efficient pipeline for sorting clusters of touching corn kernels—potentially containing sound grains, defective grains, and foreign matter—is introduced. The method operates in two steps. First, a next-generation instance segmentation network, trained entirely on a corpus of synthetically generated images, separates the input scene into distinct individual objects. Second, a purpose-built, lightweight CNN architecture assigns each segmented object to one of three categories (sound, defective, or foreign matter). The segmentation model is learned using a strategy that eliminates the need for labor‑intensive, subjective manual annotation. For the classification step, the custom network is deliberately constructed with a minimal number of layer blocks, yielding a compact architecture suitable for on-device deployment. Experimental evaluations and comparative benchmarks confirm gains in both recognition accuracy and processing speed. The proposed segmentation and classification framework can be readily transferred to other cereal grains.

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.

Traditional weed control strategies are ineffective and cannot be readily combined with intelligent farm machinery. The automated recognition and categorization of weeds is crucial for weed management and for enhancing agricultural output. The success of intelligent, precise spot-spraying platforms hinges on the accuracy of computer-vision-based detectors used for autonomous weed suppression. This work examined the practicality of deep learning frameworks (Alexnet, GoogLeNet, InceptionV3, Xception) for detecting weeds in RGB pictures taken in a bell pepper crop. The architectures were trained with differing epoch values (10, 20, 30) and batch sizes (16, 32), while hyperparameters were fine-tuned to secure the best possible results. Overall accuracy for the selected architectures spanned from 94.5 to 97.7%. InceptionV3 stood out, delivering peak performance with a 30-epoch run and a batch size of 16, attaining 97.7% accuracy, 98.5% precision, and 97.8% recall. Regarding this InceptionV3 configuration, the type I and type II error rates were 1.4% and 0.9%, respectively. The proven power of deep learning models charts a straightforward course for integrating these algorithms with image-guided herbicide delivery units to achieve precise weed management.