Innocent Nyalala

doi.org/10.1016/j.aiia.2024.11.004

@article{Zhang2024paper,
    title={A salient feature establishment tactic for cassava disease recognition},
    author={Jiayu Zhang; Baohua Zhang; Zixuan Chen; Innocent Nyalala; Kunjie Chen; Junfeng Gao},
    journal={Artificial Intelligence in Agriculture},
    volume={114},
    year={2024},
    doi={10.1016/j.aiia.2024.11.004},
    url={https://doi.org/10.1016/j.aiia.2024.11.004}
}
                                        

doi.org/10.1016/j.psj.2024.104232doi.org/10.1016/j.psj.2024.104232

@article{Nyalala2024paper,
    title={Online chicken carcass volume estimation using depth imaging and 3-D reconstruction},
    author={Innocent Nyalala; Zhang Jiayu; Chen Zixuan; Chen Junlong; Kunjie Chen},
    journal={Poultry Science},
    volume={},
    year={2024},
    doi={10.1016/j.psj.2024.104232},
    url={https://doi.org/10.1016/j.psj.2024.104232}
}
                                        

doi:10.1016/j.compag.2024.109351

@article{Zhang2024paper,
    title={MAIANet: Signal modulation in cassava leaf disease classification},
    author={Jiayu Zhang; Baohua Zhang; Chao Qi; Innocent Nyalala; Peter Mecha; Kunjie Chen; Junfeng Gao},
    journal={Computers and Electronics in Agriculture},
    volume={225},
    year={2024},
    doi={10.1016/j.compag.2024.109351},
    url={https://doi.org/10.1016/j.compag.2024.109351}
                            }
                                        

doi:10.1016/j.psj.2021.101474

@article{Nyalala2021paper,
    title={On-line weight estimation of broiler carcass and cuts by a computer vision system},
    author={Innocent Nyalala; Cedric Okinda; Nelson Makange; Tchalla Korohou; Qi Chao; Luke Nyalala; Zhang Jiayu; Zuo Yi; Khurram Yousaf; Liu Chao; Chen Kunjie},
    journal={Poultry Science},
    volume={100},
    year={2021},
    doi={10.1016/j.psj.2021.101474},
    url={https://doi.org/10.1016/j.psj.2021.101474}
}
                                                

Prediction of a precise subsoiling using an analytical model (AM) and Discrete Element Method (DEM) was conducted to explain cutting forces and the soil profile induced changes by a subsoiler. Although sensors, AMs, and DEM exist, there are still cases of soil structure deformation during deep tillage. Therefore, this study aimed to provide a clear understanding of the deep tillage using prediction models. Experimental data obtained in the soil bin trolley with force sensors were used for verification of the models. Experiments were designed using the Taguchi method. In the AM, the modified-McKyes and Willat and Willis equations were used to determine cutting forces and soil furrow profiles, respectively. Calculations were done using MATLAB software. The elastoplastic behavior of soil was incorporated into the DEM. The DEM predicted results with the best regression of 0.984 at a of 1.936 while the AM had the lowest of 0.957, at a of 6.008. All regression results were obtained at p < 0.05. The ANOVA test showed that the p-values for the horizontal and vertical forces were 0.9396 and 0.9696, respectively. The DEM predicted better than the AM. DEM is easy to use and is effective in developing models for precision subsoiling.

doi:10.1038/s41598-021-90682-w

@article{Makange2021paper,
    title={Prediction of precise subsoiling based on analytical method, discrete element simulation and experimental data from soil bin},
    author={Nelson Richard Makange; Changying Ji; Innocent Nyalala; Idris Idris Sunusi; Samwel Opiyo},
    journal={Scientific Reports},
    volume={11},
    year={2021},
    doi={10.1038/s41598-021-90682-w},
    url={https://doi.org/10.1038/s41598-021-90682-w}
}
                                                

Detecting the flowering stage of tea chrysanthemum is a key mechanism of the selective chrysanthemum harvesting robot. However, under complex, unstructured scenarios, such as illumination variation, occlusion, and overlapping, detecting tea chrysanthemum at a specific flowering stage is a real challenge. This paper proposes a highly fused, lightweight detection model named the Fusion-YOLO (F-YOLO) model. First, cutout and mosaic input components are equipped, with which the fusion module can better understand the features of the chrysanthemum through slicing. In the backbone component, the Cross-Stage Partial DenseNet (CSPDenseNet) network is used as the main network, and feature fusion modules are added to maximize the gradient flow difference. Next, in the neck component, the Cross-Stage Partial ResNeXt (CSPResNeXt) network is taken as the main network to truncate the redundant gradient flow. Finally, in the head component, the multi-scale fusion network is adopted to aggregate the parameters of two different detection layers from different backbone layers. The results show that the F-YOLO model is superior to state-of-the-art technologies in terms of object detection, that this method can be deployed on a single mobile GPU, and that it will be one of the key technologies to build a selective chrysanthemum harvesting robot system in the future.

doi:10.3390/agronomy11050834

@article{Qi2021paper,
    title={Detecting the early flowering stage of tea chrysanthemum using the F-YOLO model},
    author={Chao Qi; Innocent Nyalala; Kunjie Chen},
    journal={Agronomy},
    volume={11},
    year={2021},
    doi={10.3390/agronomy11050834},
    url={https://doi.org/10.3390/agronomy11050834}
}
                                                

The appearance, size, and weight of poultry meat and eggs are essential for production economics and vital in the poultry sector. These external characteristics influence their market price and consumers' preference and choice. With technological developments, there is an increase in the application and importance of vision systems in the agricultural sector. Computer vision has become a promising tool in the real-time automation of poultry weighing and processing systems. Owing to its noninvasive and nonintrusive nature and its capacity to present a wide range of information, computer vision systems can be applied in the size, mass, volume determination, and sorting and grading of poultry products. This review article gives a detailed summary of the current advances in measuring poultry products' external characteristics based on computer vision systems. An overview of computer vision systems is discussed and summarized. A comprehensive presentation of the application of computer vision-based systems for assessing poultry meat and eggs was provided, that is, weight and volume estimation, sorting, and classification. Finally, the challenges and potential future trends in size, weight, and volume estimation of poultry products are reported.

doi:10.1016/j.psj.2021.101072

@article{Nyalala2021paper,
    title={Weight and volume estimation of poultry and products based on computer vision systems: a review},
    author={Innocent Nyalala; Cedric Okinda; Chen Kunjie; Tchalla Korohou; Luke Nyalala; Qi Chao},
    journal={Poultry Science},
    volume={100},
    year={2021},
    doi={10.1016/j.psj.2021.101072},
    url={https://doi.org/10.1016/j.psj.2021.101072}
}
                                                

The fundamental characteristics of agricultural products are appearance, size, and weight, which affect their market value, consumer preference, and choice. Thus, food and agricultural industries seek rapid, simple, and nondestructive approaches to assess real-time measurements at the post-harvest stage before packaging for the consumer market. While sorting and grading may be performed by humans, it is unreliable, time-consuming, complicated, subjective, onerous, expensive, and easily influenced by surroundings. Therefore, an astute sorting and grading method for tomato fruit is required. We evaluated two tomato configurations on a conveyor belt: single tomatoes (no occlusion) and multi-tomatoes (partially occluded). We used polygon approximation for concave and convex point extraction algorithms to segment the occluded tomatoes. We developed seven models for regression using single-tomato image features. The Bayesian regularization artificial neural network outranked all the trained models in weight estimation with a root-mean-square error (RMSE) of 1.468 g and R2 of 0.971. For volume estimation, the RBF SVM had the best performance with R2 of 0.982 and RMSE of 1.2683 cm3. It is feasible to implement a proposed system as a noninvasive in-line sorting technique for tomatoes.

doi:10.1080/10942912.2021.1933024

@article{Nyalala2021paper,
    title={Weight and volume estimation of single and occluded tomatoes using machine vision},
    author={Innocent Nyalala; Cedric Okinda; Qi Chao; Peter Mecha; Tchalla Korohou; Zuo Yi; Samuel Nyalala; Zhang Jiayu; Liu Chao; Chen Kunjie},
    journal={International Journal of Food Properties},
    volume={24},
    year={2021},
    doi={10.1080/10942912.2021.1933024},
    url={https://doi.org/10.1080/10942912.2021.1933024}
}
                                                

doi:10.1016/j.jfoodeng.2019.07.012

@article{Nyalala2019paper,
    title={Tomato volume and mass estimation using computer vision and machine learning algorithms: Cherry tomato model},
    author={Innocent Nyalala; Cedric Okinda; Luke Nyalala; Nelson Makange; Qi Chao; Liu Chao; Khurram Yousaf; Kunjie Chen},
    journal={Journal of Food Engineering},
    volume={263},
    year={2019},
    doi={10.1016/j.jfoodeng.2019.07.012},
    url={https://doi.org/10.1016/j.jfoodeng.2019.07.012}
}
                                            

https://shorturl.at/lzAq6

@article{Yu2019paper,
    title={Production of Activated Carbon From Sludge and Herb Residue of Traditional Chinese Medicine Industry and Its Application for Methylene Blue Removal},
    author={Zhenwei Yu; Qi Gao; Yue Zhang; Dandan Wang; Innocent Nyalala; Kunjie Chen},
    journal={BioResources},
    volume={14},
    year={2019},
    doi={https://shorturl.at/lzAq6},
    url={https://shorturl.at/lzAq6}
}